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[SIPTA] LATA 2019: call for participation

by IRDTA

Feb. 15, 2019

[SIPTA] Entanglement!

by Alessio Benavoli

Feb. 14, 2019

[SIPTA] 3rd Call for papers: DARe at LPNMR-19

by Ivan José Varzinczak

* Apologies if you receive multiple copies of this call * ============================== Call for Papers DARe at LPNMR 2019 Date: June 3 or 4 2019 (TBC) Philadelphia, USA *** Deadline: 19 March 2019 *** ============================== The Fifth International Workshop on "Defeasible and Ampliative Reasoning" (DARe) https://sites.google.com/view/dare-19/ collocated with LPNMR 2019 -- Workshop Description and Aims -- Classical reasoning is not flexible enough when directly applied to the formalisation of certain nuances of human quotidian decision making. These involve different kinds of reasoning such as reasoning with uncertainty, exceptions, similarity, vagueness, incomplete or contradictory information and many others. It turns out that everyday reasoning usually shows the two salient intertwined aspects below: * Ampliative aspect: augmenting the underlying reasoning by allowing more conclusions. In practical contexts, this amounts to the ability to make inferences that venture beyond the scope of the premises, somehow in an unsound but justifiable way. Prominent examples are (i) default reasoning: jumping to conclusions deemed as plausible 'by default', i.e., in the absence of information to the contrary, like applying negation as failure or adopting the closed-world assumption; (ii) inductive and abductive reasoning: taking chances in drawing conclusions that implicitly call for further scrutiny or tests by empirical observations, like in making inductive hypotheses in scientific theories or finding abductive explanations in forensics, and (iii) analogical reasoning: extrapolating from very few examples (in the worst case only one) on the basis of observable similarities or dissimilarities. * Defeasible aspect: curtailing the underlying reasoning by either disregarding or disallowing some conclusions that somehow ought not to be sanctioned. In practice, this amounts to the ability to backtrack one's conclusions or to admit exceptions in reasoning. Some examples of this are (i) retractive reasoning: withdrawing conclusions that have already been derived, like in belief contraction or in negotiation, and (ii) preemptive reasoning: preventing or blocking the inference of some conclusions by disallowing their derivation in the first place, like in dealing with exceptional cases in multiple inheritance networks and in regulatory systems. Several efforts have been put into the study and definition of formalisms within which the aforementioned aspects of everyday reasoning could adequately be captured at different levels. Despite the progress that has been achieved, a large avenue remains open for exploration. Indeed, the literature on non-monotonic reasoning has focused almost exclusively on defeasibility of argument forms (conditionals), whereas belief revision paradigms are restricted to an underlying classical (Tarskian) consequence relation. Moreover, even if some of the issues related to uncertainty in reasoning have been studied using probabilistic approaches and statistical methods, their integration with qualitative frameworks remain a challenge. Finally, well-established approaches are largely based on propositional languages or haunted by the undecidability of full first-order logic. Modern applications require formalisms with a good balance between expressive power and computational complexity. DARe aims at bringing together researchers and practitioners from core areas of artificial intelligence, cognitive sciences, philosophy and related disciplines to discuss these kinds of problems and relevant results in a multi-disciplinary forum. The goal of the workshop is to present latest research developments, to discuss current directions in the field, and to collect first-hand feedback from the community. -- Scope of the Workshop -- DARe welcomes contributions on all aspects of defeasible and ampliative reasoning such as (but not limited to): - Abductive and inductive reasoning - Explanation finding, diagnosis and causal reasoning - Inconsistency handling and exception-tolerant reasoning - Decision-making under uncertainty and incomplete information - Default reasoning, non-monotonic reasoning, non-monotonic logics, conditional logics - Specific instances and variations of ampliative and defeasible reasoning - Probabilistic and statistical approaches to reasoning - Vagueness, rough sets, granularity and fuzzy-logics - Philosophical foundations of defeasibility - Empirical studies of reasoning - Relationship with cognition and language - Contextual reasoning - Preference-based reasoning - Analogical reasoning - Similarity-based reasoning - Belief dynamics and merging - Argumentation theory, negotiation and conflict resolution - Heuristic and approximate reasoning - Defeasible normative systems - Reasoning about actions and change - Reasoning about knowledge and belief, epistemic and doxastic logics - Ampliative and defeasible temporal and spatial reasoning - Computational aspects of reasoning with uncertainty - Implementations and systems - Applications of uncertainty in reasoning -- Submission Requirements -- We invite submissions of papers presenting original research results or position statements. Submissions must be prepared using the Springer LNAI/LNCS format and should be no longer than 13 pages (not counting the references). Please submit to: https://easychair.org/conferences/?conf=dare19 -- Workshop Proceedings/Notes -- Accepted papers will be made available electronically in the CEUR Workshop Proceedings series (http://ceur-ws.org) Copyright of papers remain with the authors. The 2014 proceedings are available at http://ceur-ws.org/Vol-1212/ The 2015 proceedings are available at http://ceur-ws.org/Vol-1423/ The 2016 proceedings are available at http://ceur-ws.org/Vol-1626/ The 2017 proceedings are available at http://ceur-ws.org/Vol-1872/ -- Attendance -- The selection of accepted contributions will be based on relevance, significance and the work's potential to foster discussions and cross-pollination. Therefore submissions of ongoing work are also strongly encouraged. At least one co-author of each accepted paper must register for the workshop. Please check the LPNMR 2019 website for registration procedure, fees as well as cancellation policies. -- Important Dates -- - Submission deadline: 19 March 2019 - Notification to authors: 26 April 2019 - Camera ready version: 6 May 2019 - Workshop date: 3 or 4 June 2019 (to be confirmed) -- Invited Speaker -- [TBA] -- Workshop Co-Chairs -- - Richard Booth, Cardiff University, UK - Giovanni Casini, University of Luxembourg - Ivan Varzinczak, CRIL, Univ. Artois & CNRS, France -- Program Committee -- - Grigoris Antoniou, University of Huddersfield, UK - Ofer Arieli, Academic College of Tel-Aviv, Israel - Guillaume Aucher, University of Rennes 1 - INRIA, France - Christoph Beierle, FernUniversitaet Hagen, Germany - Mario Benevides, Universidade Federal do Rio de Janeiro, Brazil - Alexander Bochman, Holon Institute of Technology, Israel - Arina Britz, Stellenbosch University, South Africa - James Delgrande, Simon Fraser University, Canada - Patrick Girard, University of Auckland, New Zealand - Andreas Herzig, IRIT CNRS, France - Aaron Hunter, British Columbia Institute of Technology, Canada - Souhila Kaci, Université Montpellier 2, France - Simon Kramer, SK-R&D, Switzerland - Michael Maher, University of New South Wales, Australia - João Marcos, Universidade Federal do Rio Grande do Norte, Brazil - Reka Markovich, Université du Luxembourg - Thomas Meyer, University of Cape Town, South Africa - Xavier Parent, Université du Luxembourg - Francois Schwarzentruber, ENS Rennes/IRISA, France - Sonja Smets, University of Amsterdam, The Netherlands - Umberto Straccia, CNR, Italy - Christian Straßer, Ruhr-Universitaet Bochum, Germany - Rafael Testa, University of Campinas, Brazil - Leon van der Torre, Université du Luxembourg - Heinrich Wansing, Ruhr-Universitaet Bochum, Germany - Renata Wassermann, Universidade de São Paulo, Brazil - Anna Zamansky, University of Haifa, Israel -- Further Information -- Please visit the workshop website (https://sites.google.com/view/dare-19/) for further information and regular updates. Enquiries should be sent to dare.to.contact.us(a)gmail.com -- Ivan Varzinczak - https://www.ijv.ovh CRIL, Université d'Artois & CNRS, France

Feb. 12, 2019

Re: [SIPTA] Newsletter: ASCE-ASME Journal

by Kreinovich

Feb. 11, 2019

[SIPTA] Bayes@Lund 2019 - Invitation and Call for Presentations

by TROFFAES

*Please disseminate where appropriate.* Bayes(a)Lund 2019 – a Mini-conference on Bayesian Methods at Lund University, 7th of May, 2019 You are welcome to participate in the sixth edition of Bayes(a)Lund! The purpose of this conference is to bring together researchers and professionals working with or interested in Bayesian methods. Bayes(a)Lund aims at being accessible to researchers with little experience of Bayesian methods while still being relevant to experienced practitioners. The focus is on how Bayesian methods are used in research and in the industry, what advantages Bayesian methods have over classical alternatives, and how the use and teaching of Bayesian methods can be encouraged. (see last year's conference<https://bayesat.github.io/lund2018/bayes_at_lund_2018.html> for what to expect). The conference will take place at Lund University, Sweden on the 7th of May 2019 starting at 9.00 and ending at 17.00. It will include contributed talks and invited presentations. This year we are more than pleased to have Sharon Bertsch McGrayne as an invited speaker. She is the author of the The Theory That Would Not Die<http://www.mcgrayne.com/the_theory_that_would_not_die__how_bayes__rule_crac…>, the bestselling book that describes the rise and fall and rise again of Bayesian statistics. For more information and how register, please see the conference web site<https://indico.esss.lu.se/event/1191/>. Call for Presentations We invite you to submit a talk or short tutorial to Bayes(a)Lund 2019 on a topic related to Bayesian methods in research as well as in a non-academic context, including, but not restricted to: · Case studies. Have you used Bayesian methods in your work? Describe what you did, and how it worked out. · Method development. Are you developing novel Bayesian methods or software that you want to share? · Teaching Bayes. Do you have experiences teaching or introducing Bayesian methods? What were the challenges, and do you have any useful tips? Each accepted speaker is given 20 minutes and, as the audience will be highly heterogeneous from different disciplines and from within and outside academia, the talk is expected to be accessible and engaging for a multidisciplinary audience. Alternatively, short tutorials are 10/20/30 minutes long tutorials where you introduce the audience to a tool, package or methodology you find useful. It doesn’t need to be something you’ve developed, just something you want to show the audience! For more information on how to submit a talk proposal please see here<https://indico.esss.lu.se/event/1191/abstracts/>. We hope to see you there! All best, The committee Ullrika Sahlin, Rasmus Bååth, Alex Holmes, and Jakob Lavröd

Feb. 11, 2019

[SIPTA] FW: metrological (measurement-related) applications of uncertainty

by Kreinovich

Feb. 10, 2019

[SIPTA] ISIPTA 2019: one month until paper submission closes

by Jasper De Bock

Feb. 2, 2019

[SIPTA] DeepLearn 2019: early registration February 2

by IRDTA

Jan. 29, 2019

[SIPTA] DeepLearn 2019: early registration February 2

by IRDTA

Jan. 22, 2019

[SIPTA] 10 postdoc researchers @ IDSIA

by Marco Zaffalon

IDSIA.ch is looking for 10 postdoctoral researchers in the following areas: Probabilistic machine learning, Bayesian statistics, (Bayesian) optimisation, time series analysis, probabilistic programming, graphical models/causal inference, probabilistic deep learning, natural language processing/understanding. The positions will be filled as soon as suitable candidates will be found. For details, please check: http://www.supsi.ch/home_en/supsi/lavora-con-noi/2019-06-30-bando586.html.

Jan. 15, 2019

[SIPTA] DeepLearn 2019: early registration February 2

by IRDTA

Jan. 15, 2019

[SIPTA] AlCoB 2019: submission deadline extended to January 19

by IRDTA

Jan. 8, 2019

[SIPTA] Stream at EURO 2019

by Barbara Vantaggi

Dear friends Happy new year! We would like to inform SIPTA comunity about the following stream "Decision Making under Imprecise and Vague Information" we are organizing for the 30th EUROPEAN CONFERENCE ON OPERATIONAL RESEARCH EURO 2019 https://www.euro2019dublin.com/ that will be in Dublin 23-26 June. If you decide to participate to the stream or to organize a session of 4 papers, please let us know, that we will invite you through the conference system. The deadline for the abstract is 8.2.2019. All the best Barbara, Davide, Giulianella

Jan. 4, 2019

[SIPTA] BigDat 2019: regular registration January 4

by IRDTA

Jan. 2, 2019

[SIPTA] Fw: Paper submission and registration now open: IFSA - NAFIPS 2019

by Kreinovich

FYI, papers related to imprecise probabilities -- especially fuzzy-valued -- are always welcome at this series of conferences ________________________________________ From: Ralph Baker Kearfott <rbk5287(a)louisiana.edu> Sent: Monday, December 31, 2018 4:00 PM Please distribute the following Baker -------------------------------------------------------------- Colleagues: Paper submission and registration are currently available for the 2019 International Fuzzy Systems Association World Congress, held jointly with the annual meeting of the North American Fuzzy Information Processing Society, June 18-22, 2019 in Lafayette, Louisiana. See https://interval.louisiana.edu/IFSA-NAFIPS/index.html for details, including special sessions, keynote speakers, instructions for authors, and on-line registration. The conference will take place a week before the New Orleans FUZZ-IEEE conference. Shuttles from Lafayette to New Orleans will be scheduled. The 12-th International Workshop on Constraint Programming and Decision Making will be held at the conference venue a day before the conference; a nominal registration fee will be collected on-site. There will also be a summer school, given by Ildar Batyrshin, on an Introduction to General Theory of Similarity and Association Functions, to be held during the first two days of the conference, with separate registration, dinner, and discussion. Details will appear shortly. Sincerely, Ralph Baker Kearfott, co-chair IFSA-NAFIPS 2019 --------------------------------------------------------------

Jan. 1, 2019

[SIPTA] Vacancies at Utrecht University

by Cassio P. de Campos

6 Assistant Professors in Information and Computing Sciences (Tenure Track 0.8 - 1.0 FTE) https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs Job description Due to our successful teaching programmes and our ambitions in research, the Department of Information and Computing Sciences is expanding. We are therefore actively searching for six motivated, ambitious Assistant Professors in Information and Computing Sciences. Please note that positions offered will vary, depending on experience and expertise. You have a background in Computing and Information Sciences, preferably in the areas of Information Science, Artificial Intelligence, Data Science, Algorithms, Software, Computer Science Education and Process Management and Analytics. Excellent candidates with other areas of expertise related to our current research groups and teaching programmes are also invited to apply. You have proven ambition and talent for research. As teaching is an important and satisfying part of our work, we are searching for people with a demonstrable motivation to teach. The Department is expanding and provides a dynamic work environment. If you are excited to actively participate in shaping the department, you are very welcome to apply. One PhD candidate will be funded for you by the Department in order to start up your research. Qualifications We are looking for 6 Assistant Professors. Ideally your eligibility is exemplified by Research: PhD in Computer Science, Information Science or another relevant discipline; international publications in leading conferences and journals; experience with or good prospects for acquiring external research funds; vision on future research directions in own area of expertise; experience with or readiness to supervise PhD projects; active role in international scientific communities. Teaching: experience with and enthusiasm for teaching and student supervision; ability to teach in departmental BSc and MSc programmes; well-developed didactic skills; excellent command of the English language; experience with or willingness to use innovative teaching methods and (e-learning) technologies; vision on teaching and your own contribution to teaching. Please note that we are also interested in candidates with a focus on teaching. Leadership: play an active and cooperative role in the department and the university; willingness to organize scientific events, such as research seminars or teaching seminars; willingness to partake in departmental committees. The department finds gender balance specifically and diversity in a broader sense very important. In recent procedures we attracted a significant number of women (five out of 10 positions). We are very keen on appointing more female scientists and therefore strongly encourage qualified women to apply. Publication track record, teaching record, and acquired research funds will be weighed against the number of years of academic experience, also taking into account parental leave or a part-time work schedule. Offer We offer a position of 0.8 - 1.0 FTE. The gross salary - depending on previous qualifications and experience - ranges between €3,255 and €5,656 (scale 10/12 according to the Collective Labour Agreement Dutch Universities) gross per month for a full-time employment. Salaries are supplemented with a holiday bonus of 8 % and a year-end bonus of 8.3 % per year. We offer a pension scheme, (partly paid) parental leave, collective insurance schemes and flexible employment conditions (multiple choice model). Facilities for sports and child care are available on our campus, which is only 15 minutes away from the historical city center of Utrecht. More information is available at: working at Utrecht University. We offer you the possibility to develop towards a Basic Teaching Qualification, supported with educational development programs offered by the University. We also offer candidates the possibility to travel to conferences. We offer you the possibility to develop towards a Basic Teaching Qualification, supported with educational development programmes offered by the University. We also offer candidates the possibility to travel to conferences. About the organization A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University, colleagues from various disciplines collaborate intensively towards major societal themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Sustainability. The city of Utrecht is one of the oldest cities in the Netherlands, with a charming old center and an internationally oriented culture that is strongly influenced by its century-old university. Utrecht city has been consistently ranked as one of the most livable cities in the Netherlands. The Faculty of Science consists of six Departments: Biology, Pharmaceutical Sciences, Information and Computing Sciences, Physics and Astronomy, Chemistry and Mathematics. The Faculty is home to 5,900 students and nearly 1,600 staff and is internationally renowned for the quality of its research. The Faculty's academic programmes reflect developments in today's society. The Department of Information and Computing Sciences is nationally and internationally renowned for its research in Computing Science and Information Science. Current research groups are Algorithmic Data Analysis, Algorithms and Complexity, Decision Support Systems, Intelligent Systems, Simulation of Complex Systems, Multimedia, Human-Centered Computing, Geometric Computing, Process Management and Analytics, Organization and Information, Natural Language Processing, Intelligent Systems, Software Technology and Software Technology of Learning and Teaching. Relevant areas of interdisciplinary research include Game Research, Foundations of Complex Systems, Applied Data Science, Artificial Intelligence and Integrative Bioinformatics. The Department has, among others, close collaborations with the University Medical Center, the Departments of Physics and Mathematics, and the Faculties of Humanities and Geosciences. The Department offers Bachelor programmes in Computer Science and Information Science, and four English Research Master programmes in Artificial Intelligence, Business Informatics, Computing Science, and Game and Media Technology. The Department is developing Master programmes in Data Science and Interaction Technology. High enrolment figures and good student ratings make the education very successful. The Department currently comprises 15 full-time Chairs and 105 other scientific staff, including Postdocs and PhD candidates. Additional information Would you like additional information about the vacancy? This can be obtained from Prof. van Kreveld, Research Director, via M.J.vanKreveld(a)uu.nl, or from Marloes Reichardt-Buijs, HR Officer for the Department, via M.S.Reichardt-Buijs(a)uu.nl. Apply To apply, please use the button below. Please attach a letter of motivation, curriculum vitae and the (e-mail) addresses of two references. If you prefer a part-time appointment, you are also invited to apply, preferably stating the desired part-time ratio. The application deadline is 14/02/2019

Dec. 30, 2018

[SIPTA] AlCoB 2019: 3rd call for papers

by IRDTA

Dec. 24, 2018

[SIPTA] Robotic 2019: early registration December 31st

by IRDTA

Dec. 18, 2018

[SIPTA] publish or perish

by Marco Zaffalon

Dec. 18, 2018

[SIPTA] BigDat 2019: regular registration January 4

by IRDTA

Dec. 11, 2018

[SIPTA] AlCoB 2019: 2nd call for papers

by IRDTA

Dec. 4, 2018

[SIPTA] WPMSIIP 2019

by COOLEN

Nov. 21, 2018

[SIPTA] Fw: IFSA-NAFIPS 2019 Second Call for papers

by Kreinovich

Nov. 21, 2018

[SIPTA] BigDat 2019: early registration December 8

by IRDTA

Nov. 20, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Abrams

Nov. 17, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Michael Smithson

Nov. 16, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Abrams

Nov. 16, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Marshall

Wow--thanks very much, everyone, for informative replies and discussion. Mik, Jay, Gert, and Marco, yes--your understanding accurately captures the general idea that I was thinking about. Thank you for the outlines of algorithms for generating the kind of sequence I had in mind. I may explore implementing something like this, but the discussion in thread is helping to clarify my thinking, and I wonder if I really do wonder whether I really do want a sequence that behaves as if it seems to be leading to a distinct liminf and limsup. That is certainly how I had been thinking about it. I need to think about it further. This discussion has been helpful for me in multiple respects. I had glanced at Gert and Jasper's paper earlier this year, I think, but I wasn't then asking the question I'm asking now, and had not read it at the time. I still have the paper in a reading queue, but I had forgotten about precisely what it was doing. Marco is right that it's finite sequences that interest me, but Gert sketched an algortithm in one of the emails, and I clearly need to read that paper regardless. (Mik, I am not sure I follow all of the reasoning in your last email below, but that's my fault. I need to read Gert and Jasper's paper first and then come back to these comments. By the way, Fine and his collaborators have quite a few papers on objective imprecise probability. The one I mentioned has been particularly interesting to me. Marco has a very interesting recent paper, too, and I mentioned several other authors--I'd be happy to send references if you'd like.) Best, Marshall > On Nov 13, 2018, at 3:11 PM, Professor Bickis <bickis(a)snoopy.usask.ca> wrote: > > A sequence such as Gert describes will generate subsequences whose limiting relative frequencies will not be identical. Nonetheless, such a sequence as a whole would still satisfy the strong law of large numbers, so that the relative frequency will (a.s.) converge to the average success probability. I am not sure that such a sequence is what the original question was asking. > >>> Then over most large lets of outputs, the >>> frequency of 1 should lie in or near [0.6, 0.8], and as we increased >>> the size of such a set, the frequency would remain near [0.6, 0.8] but >>> would usually wander without settling down to any precise value within that interval. > > If one restricts the sets over which averages are calculated, then one would observe the phenomenon of the frequency “not settling down”, but if the sets were allowed to grow without restriction, then the frequency would settle down to an average. > > It really depends on how one defines limits. In classical probability, the limit is defined in terms of a sequence of relative frequencies. For any epsilon>0, > can you find an n such that for all larger n the observed relative frequency is within epsilon of the limit. > > One could instead consider a net of finite sets (ordered by inclusion). Given epsilon, can one find a finite set such that for every finite superset the observed relative frequency is within epsilon of the limit. This criterion would fail for sequences of Gert’s type. It’s not immediately obvious to me that a strong law of large numbers would hold even for an i.i.d. sequence with this criterion. (An exercise for grad students, perhaps? :-)) > > Mik Bickis > >> On Nov 13, 2018, at 12:03 PM, Gert De Cooman <Gert.DeCooman(a)UGent.be> wrote: >> >> My reply to Alessandro, building on my previous message : >> >> Precise randomness is more than convergence of the sample mean for the sequence – which is what you use in your argument – it implies also convergence to the same limit of the sample mean for any (computably selected) subsequence. (This is the notion of computable stochasticity, which is still weaker than randomness, and therefore implied by it). It is easy to see that if the extreme points are visited in a computable fashion, we can computably select subsequences whose sample mean will converge to any of the extreme points. Or arbitrarily close to any convex mixture of them. So the sequence will then not be precisely random, contrary to what you suggest. >> >> Computable randomness (in the precise case) of a sequence is the stronger property that unless you use the exact precise probability as your model for betting on the sequence of digits, you can be made to lose an unbounded amount of money by someone who bets against you using some computable strategy. It is this (martingale-theoretic) characterisation of (computable) randomness that we generalise to the imprecise case in our paper. >> >> Every good wish, Gert >> >> Prof. Gert de Cooman >> Universiteit Gent (ELIS–IDLab) — Durham University (Department of Mathematical Sciences) >> Technologiepark - Zwijnaarde 914, 9052 Zwijnaarde, Belgium >> t: +3292645653 — iMessage: +32496832628 >> e: gert.decooman(a)UGent.be >> w: users.ugent.be/~gdcooma >> >> >>> Op 13 nov. 2018, om 16:33 heeft Alessandro Antonucci <alessandro(a)idsia.ch> het volgende geschreven: >>> >>> Dear Marshall, >>> >>> For an imprecise subjectivist “the imprecision is in the eye of the >>> learner”. A non-stationary RNG can jump from one sampling distribution >>> to another but, again, this is done according to another >>> (second-order) distribution. The sample will be indistinguishable from >>> another one sampled from the marginal of that hierarchical model. The >>> imprecision level is basically a parameter of your learning algorithm >>> (e.g., s for the IDM, alpha for likelihood-based approachs), but not >>> something you might learn from your data. >>> >>> These are my two cents. >>> >>> Bests, >>> Alessandro >>> _________________________________ >>> >>> Alessandro Antonucci >>> IDSIA >>> Dalle Molle Institute >>> for Artificial Intelligence >>> Via Cantonale (Galleria 2) >>> CH-6928, Manno-Lugano, CH >>> >>> mail: alessandro(a)idsia.ch >>> skype: alessandro.antonucci >>> tel: +41 916108515 >>> web: www.idsia.ch/~alessandro >>> _________________________________ >>> >>> >>> ---------- Forwarded message --------- >>> From: Michael Smithson <Michael.Smithson(a)anu.edu.au> >>> Date: mar 13 nov 2018 alle ore 14:55 >>> Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random >>> number generators? >>> To: <alessandro.antonucci(a)gmail.com> >>> >>> >>> Hi Marshall, >>> >>> It isn't clear to me what you want the long-run state to be. Do you >>> want p(1) to settle down to having, say, a uniform distribution on >>> [.6,.8]? Or for the relative frequency of 1's to end up strictly >>> confined between .6 and .8? Or are you thinking of having p(1) fixed >>> at .7 and some other random variable controlling the interval width >>> around .7 that eventually settles on a width of .2? Or ... ? >>> >>> Kind regards, >>> >>> --Mike >>> >>> ________________________________ >>> From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall >>> <mabrams(a)uab.edu> >>> Sent: Tuesday, 13 November 2018 3:18:37 PM >>> To: sipta(a)idsia.ch >>> Subject: [SIPTA] Are there imprecise analogues of pseudo-random number >>> generators? >>> >>> I would like to ask a theoretical and practical question. (No >>> conference announcement here!) >>> >>> tl;dr: Are there imprecise analogues of pseudo-random number generators? >>> >>> If that question doesn't make sense, please feel free to read on, with >>> my thanks for doing so. (Sorry the following is so long; I was worried >>> that what I was asking would be unclear otherwise.) >>> >>> I am interested in imprecise chance, that is, objective imprecise >>> probability; this idea has been discussed in different ways by >>> Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan >>> Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably >>> others. >>> >>> In an agent-based model, (precise) chance can be modeled using a PRNG. >>> Can this be generalized to base an ABM on a model of imprecise chance? >>> I had thought this would be impossible, but now I am not so sure. A >>> good PRNG such as a Mersenne Twister is of course not truly chancy, >>> and its output even has low Kolmogorov complexity, but it's still a >>> good way to model chance in many contexts. Why couldn't imprecise >>> chance be modeled in an ABM using a deterministic algorithm as well? >>> >>> What would the output of a pseudo imprecise random number generator >>> (PIRNG) look like? >>> >>> Well, suppose one wrote a function that used a normal PRNG to generate >>> 0's and 1's with probability 0.7 for 1. Then over most large sets of >>> outputs, we would find the frequency of 1 to be close to 0.7, and as >>> we increased the size of such a set, the frequency would usually get >>> closer to 0.7 >>> >>> Similarly, using a PIRNG, one ought to be able to write a function >>> that generates 0's and 1's with (for example) an interval probability >>> of [0.6, 0.8] for 1. Then over most large lets of outputs, the >>> frequency of 1 should lie in or near [0.6, 0.8], and as we increased >>> the size of such a set, the frequency would remain near [0.6, 0.8] but >>> would usually wander without settling down to any precise value within >>> that interval. >>> >>> Are there known algorithms that might have this kind of behavior? Or >>> could this kind of behavior be simulated using PRNGs for sequences >>> that are not extremely long? i.e. maybe one can write a function >>> using a PRNG such that over the not-too-long-run, sequences would >>> appear to be imprecisely chancy, but they would eventually settle down >>> to precise values in the very very long run. That might be good >>> enough for some ABMs. >>> >>> Or am I just confused? (I think this is not unlikely.) >>> >>> Any suggestions about what sort of literature I should look at or what >>> kinds of keywords to search on would be welcome. >>> >>> [In some parts of Fierens, Rego, and Fine's "A frequentist >>> understanding of sets of measures" (2009), it sounds as if such a >>> class of algorithms might be described, at least in a loose, general >>> way, but if that is so, I have not understood the article well enough >>> to see how what's given there does so. Their model uses sequence >>> specified to be of intermediate Kolmogorov complexity, which itself >>> seems like a difficult thing to generate.] >>> >>> >>> Thank you! >>> >>> Marshall >>> >>> >>> Marshall Abrams, Associate Professor >>> Department of Philosophy, University of Alabama at Birmingham >>> Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 >>> Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 >>> Website: http://members.logical.net/~marshall >>> >>> _______________________________________________ >>> SIPTA mailing list >>> SIPTA(a)idsia.ch >>> http://mailman2.ti-edu.ch/mailman/listinfo/sipta >>> _______________________________________________ >>> SIPTA mailing list >>> SIPTA(a)idsia.ch >>> http://mailman2.ti-edu.ch/mailman/listinfo/sipta >> >> _______________________________________________ >> SIPTA mailing list >> SIPTA(a)idsia.ch >> http://mailman2.ti-edu.ch/mailman/listinfo/sipta > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 15, 2018

Re: [SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators?

by Marco Cattaneo

Dear all, I can only recommend the paper by Gert and Jasper, which includes several deep results about infinite sequences of 0's and 1's. However, as far as I understand, Marshall is interested in finite sequences of 0's and 1's that are generally accepted as "random", like the ones generated by PRNGs. Unfortunately, I do not know of any non-artificial way of generating such sequences for imprecise probabilities. Since iid sequences of 0's and 1's have relative frequencies that converge due to the law of large numbers, sequences for which the relative frequencies do not converge can be artificially obtained by dropping one (or both) of the two i's in iid. For instance, a sequence with relative frequency of 1's tending to oscillate between 0.6 and 0.8 can be obtained by keeping independence but varying the probability of 1's according to the following sequence: once 0.6, two times 0.9, six times 0.5, 18 times 0.9, 54 times 0.5, 162 times 0.9, and so on (alternating between 0.5 and 0.9 with block lengths increasing by threefold each time). By adding some randomness to the sequence of probabilities, this could be made less artificial, but I do not know of any work in which this kind of ideas is explicitly developed. Best wishes, Marco -----Original Message----- From: SIPTA <sipta-bounces(a)idsia.ch> On Behalf Of Gert De Cooman Sent: Tuesday, November 13, 2018 7:04 PM To: Dr. Alessandro Antonucci <alessandro(a)idsia.ch> Cc: sipta(a)idsia.ch Subject: Re: [SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators? My reply to Alessandro, building on my previous message : Precise randomness is more than convergence of the sample mean for the sequence – which is what you use in your argument – it implies also convergence to the same limit of the sample mean for any (computably selected) subsequence. (This is the notion of computable stochasticity, which is still weaker than randomness, and therefore implied by it). It is easy to see that if the extreme points are visited in a computable fashion, we can computably select subsequences whose sample mean will converge to any of the extreme points. Or arbitrarily close to any convex mixture of them. So the sequence will then not be precisely random, contrary to what you suggest. Computable randomness (in the precise case) of a sequence is the stronger property that unless you use the exact precise probability as your model for betting on the sequence of digits, you can be made to lose an unbounded amount of money by someone who bets against you using some computable strategy. It is this (martingale-theoretic) characterisation of (computable) randomness that we generalise to the imprecise case in our paper. Every good wish, Gert Prof. Gert de Cooman Universiteit Gent (ELIS–IDLab) — Durham University (Department of Mathematical Sciences) Technologiepark - Zwijnaarde 914, 9052 Zwijnaarde, Belgium t: +3292645653 — iMessage: +32496832628 e: gert.decooman(a)UGent.be w: users.ugent.be/~gdcooma > Op 13 nov. 2018, om 16:33 heeft Alessandro Antonucci <alessandro(a)idsia.ch> het volgende geschreven: > > Dear Marshall, > > For an imprecise subjectivist “the imprecision is in the eye of the > learner”. A non-stationary RNG can jump from one sampling distribution > to another but, again, this is done according to another > (second-order) distribution. The sample will be indistinguishable from > another one sampled from the marginal of that hierarchical model. The > imprecision level is basically a parameter of your learning algorithm > (e.g., s for the IDM, alpha for likelihood-based approachs), but not > something you might learn from your data. > > These are my two cents. > > Bests, > Alessandro > _________________________________ > > Alessandro Antonucci > IDSIA > Dalle Molle Institute > for Artificial Intelligence > Via Cantonale (Galleria 2) > CH-6928, Manno-Lugano, CH > > mail: alessandro(a)idsia.ch > skype: alessandro.antonucci > tel: +41 916108515 > web: www.idsia.ch/~alessandro > _________________________________ > > > ---------- Forwarded message --------- > From: Michael Smithson <Michael.Smithson(a)anu.edu.au> > Date: mar 13 nov 2018 alle ore 14:55 > Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random > number generators? > To: <alessandro.antonucci(a)gmail.com> > > > Hi Marshall, > > It isn't clear to me what you want the long-run state to be. Do you > want p(1) to settle down to having, say, a uniform distribution on > [.6,.8]? Or for the relative frequency of 1's to end up strictly > confined between .6 and .8? Or are you thinking of having p(1) fixed > at .7 and some other random variable controlling the interval width > around .7 that eventually settles on a width of .2? Or ... ? > > Kind regards, > > --Mike > > ________________________________ > From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall > <mabrams(a)uab.edu> > Sent: Tuesday, 13 November 2018 3:18:37 PM > To: sipta(a)idsia.ch > Subject: [SIPTA] Are there imprecise analogues of pseudo-random number > generators? > > I would like to ask a theoretical and practical question. (No > conference announcement here!) > > tl;dr: Are there imprecise analogues of pseudo-random number generators? > > If that question doesn't make sense, please feel free to read on, with > my thanks for doing so. (Sorry the following is so long; I was worried > that what I was asking would be unclear otherwise.) > > I am interested in imprecise chance, that is, objective imprecise > probability; this idea has been discussed in different ways by > Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan > Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably > others. > > In an agent-based model, (precise) chance can be modeled using a PRNG. > Can this be generalized to base an ABM on a model of imprecise chance? > I had thought this would be impossible, but now I am not so sure. A > good PRNG such as a Mersenne Twister is of course not truly chancy, > and its output even has low Kolmogorov complexity, but it's still a > good way to model chance in many contexts. Why couldn't imprecise > chance be modeled in an ABM using a deterministic algorithm as well? > > What would the output of a pseudo imprecise random number generator > (PIRNG) look like? > > Well, suppose one wrote a function that used a normal PRNG to generate > 0's and 1's with probability 0.7 for 1. Then over most large sets of > outputs, we would find the frequency of 1 to be close to 0.7, and as > we increased the size of such a set, the frequency would usually get > closer to 0.7 > > Similarly, using a PIRNG, one ought to be able to write a function > that generates 0's and 1's with (for example) an interval probability > of [0.6, 0.8] for 1. Then over most large lets of outputs, the > frequency of 1 should lie in or near [0.6, 0.8], and as we increased > the size of such a set, the frequency would remain near [0.6, 0.8] but > would usually wander without settling down to any precise value within > that interval. > > Are there known algorithms that might have this kind of behavior? Or > could this kind of behavior be simulated using PRNGs for sequences > that are not extremely long? i.e. maybe one can write a function > using a PRNG such that over the not-too-long-run, sequences would > appear to be imprecisely chancy, but they would eventually settle down > to precise values in the very very long run. That might be good > enough for some ABMs. > > Or am I just confused? (I think this is not unlikely.) > > Any suggestions about what sort of literature I should look at or what > kinds of keywords to search on would be welcome. > > [In some parts of Fierens, Rego, and Fine's "A frequentist > understanding of sets of measures" (2009), it sounds as if such a > class of algorithms might be described, at least in a loose, general > way, but if that is so, I have not understood the article well enough > to see how what's given there does so. Their model uses sequence > specified to be of intermediate Kolmogorov complexity, which itself > seems like a difficult thing to generate.] > > > Thank you! > > Marshall > > > Marshall Abrams, Associate Professor > Department of Philosophy, University of Alabama at Birmingham > Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 > Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; > Office: HB 418 > Website: http://members.logical.net/~marshall > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta _______________________________________________ SIPTA mailing list SIPTA(a)idsia.ch http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 14, 2018

Re: [SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators?

by Professor Bickis

A sequence such as Gert describes will generate subsequences whose limiting relative frequencies will not be identical. Nonetheless, such a sequence as a whole would still satisfy the strong law of large numbers, so that the relative frequency will (a.s.) converge to the average success probability. I am not sure that such a sequence is what the original question was asking. >> Then over most large lets of outputs, the >> frequency of 1 should lie in or near [0.6, 0.8], and as we increased >> the size of such a set, the frequency would remain near [0.6, 0.8] but >> would usually wander without settling down to any precise value within that interval. If one restricts the sets over which averages are calculated, then one would observe the phenomenon of the frequency “not settling down”, but if the sets were allowed to grow without restriction, then the frequency would settle down to an average. It really depends on how one defines limits. In classical probability, the limit is defined in terms of a sequence of relative frequencies. For any epsilon>0, can you find an n such that for all larger n the observed relative frequency is within epsilon of the limit. One could instead consider a net of finite sets (ordered by inclusion). Given epsilon, can one find a finite set such that for every finite superset the observed relative frequency is within epsilon of the limit. This criterion would fail for sequences of Gert’s type. It’s not immediately obvious to me that a strong law of large numbers would hold even for an i.i.d. sequence with this criterion. (An exercise for grad students, perhaps? :-)) Mik Bickis > On Nov 13, 2018, at 12:03 PM, Gert De Cooman <Gert.DeCooman(a)UGent.be> wrote: > > My reply to Alessandro, building on my previous message : > > Precise randomness is more than convergence of the sample mean for the sequence – which is what you use in your argument – it implies also convergence to the same limit of the sample mean for any (computably selected) subsequence. (This is the notion of computable stochasticity, which is still weaker than randomness, and therefore implied by it). It is easy to see that if the extreme points are visited in a computable fashion, we can computably select subsequences whose sample mean will converge to any of the extreme points. Or arbitrarily close to any convex mixture of them. So the sequence will then not be precisely random, contrary to what you suggest. > > Computable randomness (in the precise case) of a sequence is the stronger property that unless you use the exact precise probability as your model for betting on the sequence of digits, you can be made to lose an unbounded amount of money by someone who bets against you using some computable strategy. It is this (martingale-theoretic) characterisation of (computable) randomness that we generalise to the imprecise case in our paper. > > Every good wish, Gert > > Prof. Gert de Cooman > Universiteit Gent (ELIS–IDLab) — Durham University (Department of Mathematical Sciences) > Technologiepark - Zwijnaarde 914, 9052 Zwijnaarde, Belgium > t: +3292645653 — iMessage: +32496832628 > e: gert.decooman(a)UGent.be > w: users.ugent.be/~gdcooma > > >> Op 13 nov. 2018, om 16:33 heeft Alessandro Antonucci <alessandro(a)idsia.ch> het volgende geschreven: >> >> Dear Marshall, >> >> For an imprecise subjectivist “the imprecision is in the eye of the >> learner”. A non-stationary RNG can jump from one sampling distribution >> to another but, again, this is done according to another >> (second-order) distribution. The sample will be indistinguishable from >> another one sampled from the marginal of that hierarchical model. The >> imprecision level is basically a parameter of your learning algorithm >> (e.g., s for the IDM, alpha for likelihood-based approachs), but not >> something you might learn from your data. >> >> These are my two cents. >> >> Bests, >> Alessandro >> _________________________________ >> >> Alessandro Antonucci >> IDSIA >> Dalle Molle Institute >> for Artificial Intelligence >> Via Cantonale (Galleria 2) >> CH-6928, Manno-Lugano, CH >> >> mail: alessandro(a)idsia.ch >> skype: alessandro.antonucci >> tel: +41 916108515 >> web: www.idsia.ch/~alessandro >> _________________________________ >> >> >> ---------- Forwarded message --------- >> From: Michael Smithson <Michael.Smithson(a)anu.edu.au> >> Date: mar 13 nov 2018 alle ore 14:55 >> Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random >> number generators? >> To: <alessandro.antonucci(a)gmail.com> >> >> >> Hi Marshall, >> >> It isn't clear to me what you want the long-run state to be. Do you >> want p(1) to settle down to having, say, a uniform distribution on >> [.6,.8]? Or for the relative frequency of 1's to end up strictly >> confined between .6 and .8? Or are you thinking of having p(1) fixed >> at .7 and some other random variable controlling the interval width >> around .7 that eventually settles on a width of .2? Or ... ? >> >> Kind regards, >> >> --Mike >> >> ________________________________ >> From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall >> <mabrams(a)uab.edu> >> Sent: Tuesday, 13 November 2018 3:18:37 PM >> To: sipta(a)idsia.ch >> Subject: [SIPTA] Are there imprecise analogues of pseudo-random number >> generators? >> >> I would like to ask a theoretical and practical question. (No >> conference announcement here!) >> >> tl;dr: Are there imprecise analogues of pseudo-random number generators? >> >> If that question doesn't make sense, please feel free to read on, with >> my thanks for doing so. (Sorry the following is so long; I was worried >> that what I was asking would be unclear otherwise.) >> >> I am interested in imprecise chance, that is, objective imprecise >> probability; this idea has been discussed in different ways by >> Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan >> Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably >> others. >> >> In an agent-based model, (precise) chance can be modeled using a PRNG. >> Can this be generalized to base an ABM on a model of imprecise chance? >> I had thought this would be impossible, but now I am not so sure. A >> good PRNG such as a Mersenne Twister is of course not truly chancy, >> and its output even has low Kolmogorov complexity, but it's still a >> good way to model chance in many contexts. Why couldn't imprecise >> chance be modeled in an ABM using a deterministic algorithm as well? >> >> What would the output of a pseudo imprecise random number generator >> (PIRNG) look like? >> >> Well, suppose one wrote a function that used a normal PRNG to generate >> 0's and 1's with probability 0.7 for 1. Then over most large sets of >> outputs, we would find the frequency of 1 to be close to 0.7, and as >> we increased the size of such a set, the frequency would usually get >> closer to 0.7 >> >> Similarly, using a PIRNG, one ought to be able to write a function >> that generates 0's and 1's with (for example) an interval probability >> of [0.6, 0.8] for 1. Then over most large lets of outputs, the >> frequency of 1 should lie in or near [0.6, 0.8], and as we increased >> the size of such a set, the frequency would remain near [0.6, 0.8] but >> would usually wander without settling down to any precise value within >> that interval. >> >> Are there known algorithms that might have this kind of behavior? Or >> could this kind of behavior be simulated using PRNGs for sequences >> that are not extremely long? i.e. maybe one can write a function >> using a PRNG such that over the not-too-long-run, sequences would >> appear to be imprecisely chancy, but they would eventually settle down >> to precise values in the very very long run. That might be good >> enough for some ABMs. >> >> Or am I just confused? (I think this is not unlikely.) >> >> Any suggestions about what sort of literature I should look at or what >> kinds of keywords to search on would be welcome. >> >> [In some parts of Fierens, Rego, and Fine's "A frequentist >> understanding of sets of measures" (2009), it sounds as if such a >> class of algorithms might be described, at least in a loose, general >> way, but if that is so, I have not understood the article well enough >> to see how what's given there does so. Their model uses sequence >> specified to be of intermediate Kolmogorov complexity, which itself >> seems like a difficult thing to generate.] >> >> >> Thank you! >> >> Marshall >> >> >> Marshall Abrams, Associate Professor >> Department of Philosophy, University of Alabama at Birmingham >> Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 >> Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 >> Website: http://members.logical.net/~marshall >> >> _______________________________________________ >> SIPTA mailing list >> SIPTA(a)idsia.ch >> http://mailman2.ti-edu.ch/mailman/listinfo/sipta >> _______________________________________________ >> SIPTA mailing list >> SIPTA(a)idsia.ch >> http://mailman2.ti-edu.ch/mailman/listinfo/sipta > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 13, 2018

Re: [SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators?

by Gert De Cooman

My reply to Alessandro, building on my previous message : Precise randomness is more than convergence of the sample mean for the sequence – which is what you use in your argument – it implies also convergence to the same limit of the sample mean for any (computably selected) subsequence. (This is the notion of computable stochasticity, which is still weaker than randomness, and therefore implied by it). It is easy to see that if the extreme points are visited in a computable fashion, we can computably select subsequences whose sample mean will converge to any of the extreme points. Or arbitrarily close to any convex mixture of them. So the sequence will then not be precisely random, contrary to what you suggest. Computable randomness (in the precise case) of a sequence is the stronger property that unless you use the exact precise probability as your model for betting on the sequence of digits, you can be made to lose an unbounded amount of money by someone who bets against you using some computable strategy. It is this (martingale-theoretic) characterisation of (computable) randomness that we generalise to the imprecise case in our paper. Every good wish, Gert Prof. Gert de Cooman Universiteit Gent (ELIS–IDLab) — Durham University (Department of Mathematical Sciences) Technologiepark - Zwijnaarde 914, 9052 Zwijnaarde, Belgium t: +3292645653 — iMessage: +32496832628 e: gert.decooman(a)UGent.be w: users.ugent.be/~gdcooma > Op 13 nov. 2018, om 16:33 heeft Alessandro Antonucci <alessandro(a)idsia.ch> het volgende geschreven: > > Dear Marshall, > > For an imprecise subjectivist “the imprecision is in the eye of the > learner”. A non-stationary RNG can jump from one sampling distribution > to another but, again, this is done according to another > (second-order) distribution. The sample will be indistinguishable from > another one sampled from the marginal of that hierarchical model. The > imprecision level is basically a parameter of your learning algorithm > (e.g., s for the IDM, alpha for likelihood-based approachs), but not > something you might learn from your data. > > These are my two cents. > > Bests, > Alessandro > _________________________________ > > Alessandro Antonucci > IDSIA > Dalle Molle Institute > for Artificial Intelligence > Via Cantonale (Galleria 2) > CH-6928, Manno-Lugano, CH > > mail: alessandro(a)idsia.ch > skype: alessandro.antonucci > tel: +41 916108515 > web: www.idsia.ch/~alessandro > _________________________________ > > > ---------- Forwarded message --------- > From: Michael Smithson <Michael.Smithson(a)anu.edu.au> > Date: mar 13 nov 2018 alle ore 14:55 > Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random > number generators? > To: <alessandro.antonucci(a)gmail.com> > > > Hi Marshall, > > It isn't clear to me what you want the long-run state to be. Do you > want p(1) to settle down to having, say, a uniform distribution on > [.6,.8]? Or for the relative frequency of 1's to end up strictly > confined between .6 and .8? Or are you thinking of having p(1) fixed > at .7 and some other random variable controlling the interval width > around .7 that eventually settles on a width of .2? Or ... ? > > Kind regards, > > --Mike > > ________________________________ > From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall > <mabrams(a)uab.edu> > Sent: Tuesday, 13 November 2018 3:18:37 PM > To: sipta(a)idsia.ch > Subject: [SIPTA] Are there imprecise analogues of pseudo-random number > generators? > > I would like to ask a theoretical and practical question. (No > conference announcement here!) > > tl;dr: Are there imprecise analogues of pseudo-random number generators? > > If that question doesn't make sense, please feel free to read on, with > my thanks for doing so. (Sorry the following is so long; I was worried > that what I was asking would be unclear otherwise.) > > I am interested in imprecise chance, that is, objective imprecise > probability; this idea has been discussed in different ways by > Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan > Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably > others. > > In an agent-based model, (precise) chance can be modeled using a PRNG. > Can this be generalized to base an ABM on a model of imprecise chance? > I had thought this would be impossible, but now I am not so sure. A > good PRNG such as a Mersenne Twister is of course not truly chancy, > and its output even has low Kolmogorov complexity, but it's still a > good way to model chance in many contexts. Why couldn't imprecise > chance be modeled in an ABM using a deterministic algorithm as well? > > What would the output of a pseudo imprecise random number generator > (PIRNG) look like? > > Well, suppose one wrote a function that used a normal PRNG to generate > 0's and 1's with probability 0.7 for 1. Then over most large sets of > outputs, we would find the frequency of 1 to be close to 0.7, and as > we increased the size of such a set, the frequency would usually get > closer to 0.7 > > Similarly, using a PIRNG, one ought to be able to write a function > that generates 0's and 1's with (for example) an interval probability > of [0.6, 0.8] for 1. Then over most large lets of outputs, the > frequency of 1 should lie in or near [0.6, 0.8], and as we increased > the size of such a set, the frequency would remain near [0.6, 0.8] but > would usually wander without settling down to any precise value within > that interval. > > Are there known algorithms that might have this kind of behavior? Or > could this kind of behavior be simulated using PRNGs for sequences > that are not extremely long? i.e. maybe one can write a function > using a PRNG such that over the not-too-long-run, sequences would > appear to be imprecisely chancy, but they would eventually settle down > to precise values in the very very long run. That might be good > enough for some ABMs. > > Or am I just confused? (I think this is not unlikely.) > > Any suggestions about what sort of literature I should look at or what > kinds of keywords to search on would be welcome. > > [In some parts of Fierens, Rego, and Fine's "A frequentist > understanding of sets of measures" (2009), it sounds as if such a > class of algorithms might be described, at least in a loose, general > way, but if that is so, I have not understood the article well enough > to see how what's given there does so. Their model uses sequence > specified to be of intermediate Kolmogorov complexity, which itself > seems like a difficult thing to generate.] > > > Thank you! > > Marshall > > > Marshall Abrams, Associate Professor > Department of Philosophy, University of Alabama at Birmingham > Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 > Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 > Website: http://members.logical.net/~marshall > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 13, 2018

Re: [SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators?

by Gert De Cooman

Dear Marshall I refer to our paper about imprecise randomness (Computable randomness is inherently imprecise, De Cooman and De Bock) in the latest ISIPTA proceedings: https://biblio.ugent.be/publication/8531435/file/8531437.pdf In the paper we show that non-stationarity is indeed a source of imprecise randomness. We also prove a number of convergence results. All this for sequences of 0-1, which is the simplest case. We show, for instance, that alternating between a two un-fair coins with probabilities p and q for heads (in a deterministic fashion) will produce an imprecise random sequence of 0’s and 1’s with characterising probability interval [p,q]. The meaning of this is explained in the paper, but the underlying idea is that anyone using an interval betting model that does not include [p,q] - so for instance a precise r in this interval - can be made to lose an infinite amount of money in an infinite sequence of betting events on the digits in the sequence (using a computable strategy); and anywhere who uses a betting model that includes [p,q] is immune to such computable strategies. So this suggests an algorithm in the more general case: use an algorithm that visits the externe points of a credal set deterministically (eg, cyclically) , and for each visit to an extreme point, use the corresponding random generator to generate an outcome. Whether this also works in the case of more than two possible outcomes, still remains to be investigated, however. Best wishes, Gert > Op 13 nov. 2018, om 16:33 heeft Alessandro Antonucci <alessandro(a)idsia.ch> het volgende geschreven: > > Dear Marshall, > > For an imprecise subjectivist “the imprecision is in the eye of the > learner”. A non-stationary RNG can jump from one sampling distribution > to another but, again, this is done according to another > (second-order) distribution. The sample will be indistinguishable from > another one sampled from the marginal of that hierarchical model. The > imprecision level is basically a parameter of your learning algorithm > (e.g., s for the IDM, alpha for likelihood-based approachs), but not > something you might learn from your data. > > These are my two cents. > > Bests, > Alessandro > _________________________________ > > Alessandro Antonucci > IDSIA > Dalle Molle Institute > for Artificial Intelligence > Via Cantonale (Galleria 2) > CH-6928, Manno-Lugano, CH > > mail: alessandro(a)idsia.ch > skype: alessandro.antonucci > tel: +41 916108515 > web: www.idsia.ch/~alessandro > _________________________________ > > > ---------- Forwarded message --------- > From: Michael Smithson <Michael.Smithson(a)anu.edu.au> > Date: mar 13 nov 2018 alle ore 14:55 > Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random > number generators? > To: <alessandro.antonucci(a)gmail.com> > > > Hi Marshall, > > It isn't clear to me what you want the long-run state to be. Do you > want p(1) to settle down to having, say, a uniform distribution on > [.6,.8]? Or for the relative frequency of 1's to end up strictly > confined between .6 and .8? Or are you thinking of having p(1) fixed > at .7 and some other random variable controlling the interval width > around .7 that eventually settles on a width of .2? Or ... ? > > Kind regards, > > --Mike > > ________________________________ > From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall > <mabrams(a)uab.edu> > Sent: Tuesday, 13 November 2018 3:18:37 PM > To: sipta(a)idsia.ch > Subject: [SIPTA] Are there imprecise analogues of pseudo-random number > generators? > > I would like to ask a theoretical and practical question. (No > conference announcement here!) > > tl;dr: Are there imprecise analogues of pseudo-random number generators? > > If that question doesn't make sense, please feel free to read on, with > my thanks for doing so. (Sorry the following is so long; I was worried > that what I was asking would be unclear otherwise.) > > I am interested in imprecise chance, that is, objective imprecise > probability; this idea has been discussed in different ways by > Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan > Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably > others. > > In an agent-based model, (precise) chance can be modeled using a PRNG. > Can this be generalized to base an ABM on a model of imprecise chance? > I had thought this would be impossible, but now I am not so sure. A > good PRNG such as a Mersenne Twister is of course not truly chancy, > and its output even has low Kolmogorov complexity, but it's still a > good way to model chance in many contexts. Why couldn't imprecise > chance be modeled in an ABM using a deterministic algorithm as well? > > What would the output of a pseudo imprecise random number generator > (PIRNG) look like? > > Well, suppose one wrote a function that used a normal PRNG to generate > 0's and 1's with probability 0.7 for 1. Then over most large sets of > outputs, we would find the frequency of 1 to be close to 0.7, and as > we increased the size of such a set, the frequency would usually get > closer to 0.7 > > Similarly, using a PIRNG, one ought to be able to write a function > that generates 0's and 1's with (for example) an interval probability > of [0.6, 0.8] for 1. Then over most large lets of outputs, the > frequency of 1 should lie in or near [0.6, 0.8], and as we increased > the size of such a set, the frequency would remain near [0.6, 0.8] but > would usually wander without settling down to any precise value within > that interval. > > Are there known algorithms that might have this kind of behavior? Or > could this kind of behavior be simulated using PRNGs for sequences > that are not extremely long? i.e. maybe one can write a function > using a PRNG such that over the not-too-long-run, sequences would > appear to be imprecisely chancy, but they would eventually settle down > to precise values in the very very long run. That might be good > enough for some ABMs. > > Or am I just confused? (I think this is not unlikely.) > > Any suggestions about what sort of literature I should look at or what > kinds of keywords to search on would be welcome. > > [In some parts of Fierens, Rego, and Fine's "A frequentist > understanding of sets of measures" (2009), it sounds as if such a > class of algorithms might be described, at least in a loose, general > way, but if that is so, I have not understood the article well enough > to see how what's given there does so. Their model uses sequence > specified to be of intermediate Kolmogorov complexity, which itself > seems like a difficult thing to generate.] > > > Thank you! > > Marshall > > > Marshall Abrams, Associate Professor > Department of Philosophy, University of Alabama at Birmingham > Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 > Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 > Website: http://members.logical.net/~marshall > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 13, 2018

[SIPTA] Fwd: Are there imprecise analogues of pseudo-random number generators?

by Alessandro Antonucci

Dear Marshall, For an imprecise subjectivist “the imprecision is in the eye of the learner”. A non-stationary RNG can jump from one sampling distribution to another but, again, this is done according to another (second-order) distribution. The sample will be indistinguishable from another one sampled from the marginal of that hierarchical model. The imprecision level is basically a parameter of your learning algorithm (e.g., s for the IDM, alpha for likelihood-based approachs), but not something you might learn from your data. These are my two cents. Bests, Alessandro _________________________________ Alessandro Antonucci IDSIA Dalle Molle Institute for Artificial Intelligence Via Cantonale (Galleria 2) CH-6928, Manno-Lugano, CH mail: alessandro(a)idsia.ch skype: alessandro.antonucci tel: +41 916108515 web: www.idsia.ch/~alessandro _________________________________ ---------- Forwarded message --------- From: Michael Smithson <Michael.Smithson(a)anu.edu.au> Date: mar 13 nov 2018 alle ore 14:55 Subject: Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators? To: <alessandro.antonucci(a)gmail.com> Hi Marshall, It isn't clear to me what you want the long-run state to be. Do you want p(1) to settle down to having, say, a uniform distribution on [.6,.8]? Or for the relative frequency of 1's to end up strictly confined between .6 and .8? Or are you thinking of having p(1) fixed at .7 and some other random variable controlling the interval width around .7 that eventually settles on a width of .2? Or ... ? Kind regards, --Mike ________________________________ From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall <mabrams(a)uab.edu> Sent: Tuesday, 13 November 2018 3:18:37 PM To: sipta(a)idsia.ch Subject: [SIPTA] Are there imprecise analogues of pseudo-random number generators? I would like to ask a theoretical and practical question. (No conference announcement here!) tl;dr: Are there imprecise analogues of pseudo-random number generators? If that question doesn't make sense, please feel free to read on, with my thanks for doing so. (Sorry the following is so long; I was worried that what I was asking would be unclear otherwise.) I am interested in imprecise chance, that is, objective imprecise probability; this idea has been discussed in different ways by Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably others. In an agent-based model, (precise) chance can be modeled using a PRNG. Can this be generalized to base an ABM on a model of imprecise chance? I had thought this would be impossible, but now I am not so sure. A good PRNG such as a Mersenne Twister is of course not truly chancy, and its output even has low Kolmogorov complexity, but it's still a good way to model chance in many contexts. Why couldn't imprecise chance be modeled in an ABM using a deterministic algorithm as well? What would the output of a pseudo imprecise random number generator (PIRNG) look like? Well, suppose one wrote a function that used a normal PRNG to generate 0's and 1's with probability 0.7 for 1. Then over most large sets of outputs, we would find the frequency of 1 to be close to 0.7, and as we increased the size of such a set, the frequency would usually get closer to 0.7 Similarly, using a PIRNG, one ought to be able to write a function that generates 0's and 1's with (for example) an interval probability of [0.6, 0.8] for 1. Then over most large lets of outputs, the frequency of 1 should lie in or near [0.6, 0.8], and as we increased the size of such a set, the frequency would remain near [0.6, 0.8] but would usually wander without settling down to any precise value within that interval. Are there known algorithms that might have this kind of behavior? Or could this kind of behavior be simulated using PRNGs for sequences that are not extremely long? i.e. maybe one can write a function using a PRNG such that over the not-too-long-run, sequences would appear to be imprecisely chancy, but they would eventually settle down to precise values in the very very long run. That might be good enough for some ABMs. Or am I just confused? (I think this is not unlikely.) Any suggestions about what sort of literature I should look at or what kinds of keywords to search on would be welcome. [In some parts of Fierens, Rego, and Fine's "A frequentist understanding of sets of measures" (2009), it sounds as if such a class of algorithms might be described, at least in a loose, general way, but if that is so, I have not understood the article well enough to see how what's given there does so. Their model uses sequence specified to be of intermediate Kolmogorov complexity, which itself seems like a difficult thing to generate.] Thank you! Marshall Marshall Abrams, Associate Professor Department of Philosophy, University of Alabama at Birmingham Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 Website: http://members.logical.net/~marshall _______________________________________________ SIPTA mailing list SIPTA(a)idsia.ch http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 13, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Professor Bickis

Hello Marshall: I think I understand what you are looking for. The law of large numbers says that with probability one, a sequence of i.i.d. random bits will settle down to a relative frequency corresponding to the probability of a 1. i.e., the lim sup and the lim inf of the relative frequency would both converge to the same number. What you are looking for is a generator of a sequence in which the lim sup is (e.g.) 0.8 and the lim inf is 0.6. Certainly such sequences exist, and with some ingenuity one could construct a way of generating such sequences. However, I am skeptical about what kind of probabilistic interpretation could be given to them, considering what the law of large numbers says. I imagine you would have to build in some kind of memory to the process, violating the independence of the trials. The obvious trick would be to vary the underlying probability that generates the sequence, but I don’t that would work. The relative frequency would just settle down to the average of the probabilities. My understanding of imprecise probability is that it is purely epistemic, but I note that you reference a (relatively) recent paper by Fine et al. about objective imprecise probability. I’ll have to look that up. These are just my immediate thoughts. It is an interesting theoretical question. I am not quite clear about what the application might be. Just an afterthought. There is a concept in analysis called “almost periodic sequences”, which are related to the phenomenon of quasi-crystals, and things like Penrose tilings. I don’t know much about these, but there may be a connection. Mik Bickis Professor Emeritus Department of Mathematics and Statistics University of Saskatchewan > On Nov 13, 2018, at 06:34 AM, Michael Smithson <Michael.Smithson(a)anu.edu.au> wrote: > > Hi Marshall, > It isn't clear to me what you want the long-run state to be. Do you want p(1) to settle down to having, say, a uniform distribution on [.6,.8]? Or for the relative frequency of 1's to end up strictly confined between .6 and .8? Or are you thinking of having p(1) fixed at .7 and some other random variable controlling the interval width around .7 that eventually settles on a width of .2? Or ... ? > Kind regards, > --Mike > From: SIPTA <sipta-bounces(a)idsia.ch> on behalf of Abrams, Marshall <mabrams(a)uab.edu> > Sent: Tuesday, 13 November 2018 3:18:37 PM > To: sipta(a)idsia.ch > Subject: [SIPTA] Are there imprecise analogues of pseudo-random number generators? > > I would like to ask a theoretical and practical question. (No conference announcement here!) > > tl;dr: Are there imprecise analogues of pseudo-random number generators? > > If that question doesn't make sense, please feel free to read on, with my thanks for doing so. (Sorry the following is so long; I was worried that what I was asking would be unclear otherwise.) > > I am interested in imprecise chance, that is, objective imprecise probability; this idea has been discussed in different ways by Terrence Fine and his colleagues, Marco Cattaneo, Luke Glynn, Alan Hajek, Suppes and Zanotti, Stephan Hartmann, Igor Gorban, and probably others. > > In an agent-based model, (precise) chance can be modeled using a PRNG. Can this be generalized to base an ABM on a model of imprecise chance? I had thought this would be impossible, but now I am not so sure. A good PRNG such as a Mersenne Twister is of course not truly chancy, and its output even has low Kolmogorov complexity, but it's still a good way to model chance in many contexts. Why couldn't imprecise chance be modeled in an ABM using a deterministic algorithm as well? > > What would the output of a pseudo imprecise random number generator (PIRNG) look like? > > Well, suppose one wrote a function that used a normal PRNG to generate 0's and 1's with probability 0.7 for 1. Then over most large sets of outputs, we would find the frequency of 1 to be close to 0.7, and as we increased the size of such a set, the frequency would usually get closer to 0.7 > > Similarly, using a PIRNG, one ought to be able to write a function that generates 0's and 1's with (for example) an interval probability of [0.6, 0.8] for 1. Then over most large lets of outputs, the frequency of 1 should lie in or near [0.6, 0.8], and as we increased the size of such a set, the frequency would remain near [0.6, 0.8] but would usually wander without settling down to any precise value within that interval. > > Are there known algorithms that might have this kind of behavior? Or could this kind of behavior be simulated using PRNGs for sequences that are not extremely long? i.e. maybe one can write a function using a PRNG such that over the not-too-long-run, sequences would appear to be imprecisely chancy, but they would eventually settle down to precise values in the very very long run. That might be good enough for some ABMs. > > Or am I just confused? (I think this is not unlikely.) > > Any suggestions about what sort of literature I should look at or what kinds of keywords to search on would be welcome. > > [In some parts of Fierens, Rego, and Fine's "A frequentist understanding of sets of measures" (2009), it sounds as if such a class of algorithms might be described, at least in a loose, general way, but if that is so, I have not understood the article well enough to see how what's given there does so. Their model uses sequence specified to be of intermediate Kolmogorov complexity, which itself seems like a difficult thing to generate.] > > > Thank you! > > Marshall > > > Marshall Abrams, Associate Professor > Department of Philosophy, University of Alabama at Birmingham > Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 > Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB 418 > Website: http://members.logical.net/~marshall > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta

Nov. 13, 2018

Re: [SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Michael Smithson

Nov. 13, 2018

[SIPTA] AlCoB 2019: 1st call for papers

by IRDTA

Nov. 13, 2018

[SIPTA] Are there imprecise analogues of pseudo-random number generators?

by Abrams

Nov. 13, 2018

[SIPTA] FW: EUSFLAT 2019 - Call for papers

by Kreinovich

Nov. 10, 2018

[SIPTA] FW: open position in computational science

by Kreinovich

Nov. 8, 2018

[SIPTA] 2018 Lotfi A. Zadeh Pioneer Award

by Kreinovich

Dear Friends, I have just learned that Ronald R. Yager was aearded the 2018 Lotfi A. Zadeh Pioneer Award from the IEEE Systems, Man, and Cybernetics Society. The award is given to honor a person with outstanding and pioneering contributions to academic and/or industrial research in systems science and engineering, human-machine systems, and/or cybernetics. Dr. Yager is a pioneer in many areas of fuzzy research. He also had published several pioneering seminal papers on interval-valued probabilities and other imprecise probability topics. Congratulations to Dr. Yager!

Nov. 7, 2018

[SIPTA] [UR @ FLAIRS 32] CALL FOR PAPERS

by Alessandro Antonucci

*** Apologies for multiple postings *** Special Track on Uncertain Reasoning at Flairs 32 - Call for Papers Special Track on Uncertain Reasoning at the 32nd Florida Artificial Intelligence Research Society Conference (FLAIRS-32) May 19-22, 2019 - Sarasota, Florida (US) Paper submission deadline: November 19, 2018 Conference website: http://www.flairs-32.info Special track website: http://w3.uqo.ca/allimo01/UR_2019/ur19.html ::: Call for Papers ::: Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. The objective of this track is to present and discuss a broad and diverse range of current work on uncertain reasoning, including theoretical and applied research based on different paradigms. We hope that the variety and richness of this track will help to promote cross fertilization among the different approaches for uncertain reasoning, and in this way foster the development of new ideas and paradigms. The Special Track on Uncertain Reasoning (UR) is the oldest track in FLAIRS conferences, running annually since 1996. The UR '19 Special Track at the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS-32) is the 24th in the series. Like the past tracks, UR'19 seeks to bring together researchers working on broad issues related to reasoning under uncertainty. Papers on all aspects of uncertain reasoning are invited. Papers of particular interest include, but are not limited to: - Uncertain reasoning formalisms, calculi and methodologies - Reasoning with probability, possibility, fuzzy logic, belief function, vagueness, granularity, rough sets, and probability logics - Modeling and reasoning using imprecise and indeterminate information, such as: Choquet capacities, comparative orderings, convex sets of measures, and interval-valued probabilities - Exact, approximate and qualitative uncertain reasoning - Inference and learning with graphical models of uncertainty (e.g., Bayesian networks) - Multi-agent uncertain reasoning and decision making - Decision-theoretic planning and Markov decision process - Temporal reasoning and uncertainty; nonmonotonic reasoning; similarity-based reasoning - Conditional logics, description logic, logic programming - Argumentation - Belief change and merging - Construction of models from elicitation, data mining and knowledge discovery - Uncertain reasoning in information retrieval, filtering, fusion, diagnosis, prediction, situation assessment - Uncertain reasoning in data management - Practical applications of uncertain reasoning (e.g., machine learning, computer vision and animation) All accepted papers will be published as FLAIRS proceedings by AAAI Press. A special issue of an international journal will be devoted to extended versions of the top papers at the track. ::: Program Committee ::: : Track Chairs : - Mohand Said Allili (University of Quebec in Outaouais, Canada) - Alessandro Antonucci (IDSIA, Switzerland) - Salem Benferhat (University of Artois, France) : PC Members : - Ofer Arieli (The Academic College of Tel-Aviv, Israel) - Nizar Bouguila (Concordia University, Canada) - Cory Butz (University of Regina, Canada) - Ameur Bensefia (Higher Colleges of Technology, UAE) - Martine Ceberio (University of Texas at El Paso, US) - Sebastien Destercke (University of Technology of Compiègne, France) - Love Ekenberg (Stockholm University, Sweden) - Lluis Godo (Spanish National Research Council, Spain) - Christophe Gonzales (LIP6, France) - Ilyes Jenhani (Prince Mohammad Bin Fahd University, Saudi Arabia) - Amanda Hicks (University of Florida, US) - Sedki Karima (University of Paris 13, France) - Gabriele Kern-Isberner (Technical University of Dortmund, Germany) - Vladik Kreinovich (University of Texas at El Paso, US) - Rafael Peñaloza (Free University of Bozen-Bolzano, Italy) - Philippe Leray (University of Nantes, France) - Lalintha G. Polpitiya (University of Miami, US) - Choh Man Teng (Institute for Human and Machine Cognition, US) - Steven Schockaert (Cardiff University, UK) - Karim Tabia (Artois University, France) - Barbara Vantaggi (Sapienza University of Rome, Italy) - Ronald Yager (Iona College, USA) ::: Other Information ::: FLAIRS 32 will be held in Sarasota, Florida. Additional information on the conference location and travel planning can be found here. Feel free to write the organizers (alessandro(a)idsia.ch or MohandSaid.allili(a)uqo.ca) for any further information about the special track. ::: Submission ::: Interested authors should format their papers according to AAAI formatting guidelines. The papers should be original work (i.e., not submitted, in submission, or submitted to another conference while in review). Papers should not exceed 6 pages (4 pages for a poster) and are due by November 19, 2018. For FLAIRS-32, the 2019 conference, the reviewing is a double blind process. Author names and affiliations must be omitted on submitted papers to provide double-blind reviewing. The papers will be reviewed by at least three reviewers. Papers must be submitted as PDF through the EasyChair conference system, which can also be accessed through the main conference web site. Authors should indicate the special track "Uncertain Reasoning" for submissions. The proceedings of FLAIRS-32 will be published by the AAAI. Authors of accepted papers will be required to sign a form transferring copyright of their contribution to AAAI. At least one author of each accepted paper is required to register, attend, and present the paper at FLAIRS-32. _________________________________ Alessandro Antonucci IDSIA Dalle Molle Institute for Artificial Intelligence Via Cantonale (Galleria 2) CH-6928, Manno-Lugano, CH mail: alessandro(a)idsia.ch web: www.idsia.ch/~alessandro _________________________________

Nov. 5, 2018

[SIPTA] Robotic 2019: early registration November 13

by IRDTA

Nov. 2, 2018

[SIPTA] Postdoc position in Milan (deadline 9th November 2018)

by hykel hosni

Oct. 31, 2018

[SIPTA] Extended Deadline: IJAR Special Issue on Robustness in Probabilistic Graphical Models

by Denis Mauá

Oct. 30, 2018

[SIPTA] ISIPTA 2019: not just any other conference!

by Jasper De Bock

Oct. 29, 2018

[SIPTA] Fw: CoProd information

by Kreinovich

Papers related to imprecise probabilities are welcome at this series of workshops ________________________________________ Welcome to 12th International Workshop on Constraint Programming and Decision Making CoProD'2019 June 17, 2019 -- Lafayette, Louisiana CoProD'19 is the twelfth edition of CoProD http://coprod.constraintsolving.com/. It will be held in Lafayette, Louisiana, USA on June 17, 2018 (exact location TBA), right before the 2019 IFSA World Congress and NAFIPS Annual Conference IFSA/NAFIPS'2019 (June 18-22, 2019, Lafayette, Louisiana), see https://interval.louisiana.edu/IFSA-NAFIPS/ Constraint programming techniques are important components of intelligent systems. They constitute a declarative and efficient methodology to represent and solve many practical problems. They have been applied successfully to a number of fields, such as scheduling of air traffic, software engineering, networks security, chemistry, and biology. Despite the proved usefulness of these techniques, they are still under-utilized in real-life applications. One reason is the perceived lack of effective communication between constraint programming experts and domain practitioners about constraints, in general, and their use in decision making, in particular. Registration: Registration will be collected on site and will be about $30, to cover coffee breaks and room and equipment rental Objectives of CoProD: * To present advances in constraint solving, optimization, and related topics; * To develop a network of researchers interested in constraint techniques, in particular researchers and practitioners that use numeric and symbolic approaches (or a combination of them) to solve constraint and optimization problems; * To address the gap between the great capacity of these techniques and their limited use. CoProD aims at encouraging presentation and discussion of on-going work. In particular, please note that there is room for presentation of ideas, as opposed to results only. It also aims at facilitating networking opportunities as well as cross-fertilization between the approaches used in the different attending communities. Therefore, besides active researchers in decision making and constraint programming techniques, we expect to have a wide attendance and participation of domain scientists -- whose input is highly valued in this workshop. The previous editions of CoProD featured invited talks by highly recognized experts (listed in alphabetic order): * Purushotham Bangalore, CIS department, University of Alabama at Birmingham * Martin Berz, Michigan State University, USA •Stefano Bistarelli, University of Perugia, Italy * Alessandro Dal Palu, University of Parma, Italy * Rina Dechter, Information and Computer Sciences, University of California Irvine * Scott Ferson, Applied Bioinformatics, New York, USA * Juan Carlos Figueroa, Universidad Distrital de Bogota, Colombia * Patty Hough, Sandia National Lab, Livermore CA * Luc Jaulin, Ecole Nationale Superieure des Techniques Avancees (ENSTA) Bretagne, France * Kyoko Makino, Michigan State University, USA * Tiago Oliveira, National Institute of Informatics (NII) in Tokyo, Japan. * Enrico Pontelli, New Mexico State University, Las Cruces, NM * James Raynolds, College of Nanoscale and Engineering, SUNY Albany * Sigfried Rump, Hamburg University of Technology and Waseda University, Japan * Ken Satoh, National Institute of Informatics (NII) in Tokyo, Japan. * Bart Selman, CS department, Cornell University * Yaroslav D. Sergeyev, Università della Calabria, Italy * Sergey Shary, Novosibirsk State University * Young Jun Son, The University of Arizona, Tucson * Xiaobai Sun, Duke University * Alfredo Vaccaro, University of Sannio, Department of Engineering, Benevento, Italy * Leticia Velazquez, The University of Texas at El Paso * Francois Modave, Texas Tech Health Center, El Paso Proceedings / Publication: Papers accepted for the workshop will be published in the same Springer volume as all the papers from the IFSA/NAFIPS'2019 conference. Please follow the conference instructions (template, paper size, deadlines, etc.) when submitting your papers. Submission should be sent to us by email to mceverio(a)utep.edu and vladik(a)utep.edu. Participation / Submission: Participation is encouraged from people doing research in the area of decision making as well as from domain scientists. Submissions of ideas are also encouraged. Important dates: January 5: paper submission February 5: notification of acceptance March 5: camera-ready copy of papers due June 17: workshop Organizers: Martine Ceberio, the University of Texas at El Paso, mceberio(a)utep.edu Vladik Kreinovich, the University of Texas at El Paso, vladik(a)utep.edu

Oct. 19, 2018

Re: [SIPTA] call for papers: IFSA-NAFIPS 2019

by Kreinovich

FYI, papers on fuzzy-valued imprecise probabilities are always welcome. This time, the conference has a strong emphasis on relation with interval techniques, so papers on interval-valued probabilities are also welcome. ________________________________________ From: Ralph Baker Kearfott <rbk5287(a)louisiana.edu> Sent: Thursday, October 18, 2018 7:46 AM IFSA-NAFIPS 2019 Call for papers 2019 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS and the 2019 NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY ANNUAL MEETING Jun3 18-22, 2019 Lafayette, Louisiana Hosted by the North American association, this biennial event brings together participants from all national and regional fuzzy system associations. IFSA-NAFIPS 2019 will be hosted at the Wyndham Garden near Vermilion Bayou in Lafayette, Louisiana, the center of Cajun culture and cuisine. Topics will include implementation and applications of fuzzy information processing technology, associated mathematical analysis, comparison with alternative rough set, statistical, and interval techniques, and, generally, other topics related to fuzzy sets. Further information can be found at: https://interval.louisiana.edu/IFSA-NAFIPS/ This conference will be held a week before the New Orleans FUZZ-IEEE conference. New Orleans is 140 miles (225 kilometers) from Lafayette, a 2.5 hour drive, from Lafayette, and we plan to arrange post-conference charter buses from Lafayette to New Orleans. Participants will thus have leverage to double their professional and cultural experience. Deadlines (extended): Special session and workshop proposals: December 1, 2018 Paper submission: January 15, 2019 A number of special sessions have already been accepted, and keynote speakers have already agreed. Information concerning these will soon appear at https://interval.louisiana.edu/IFSA-NAFIPS/

Oct. 19, 2018

[SIPTA] Fw: 29th European Safety and Reliability Conference (ESREL 2019), Hannover, 22 - 26 September 2019

by Kreinovich

Oct. 18, 2018

[SIPTA] BigDat 2019: early registration October 15

by IRDTA

Oct. 15, 2018

[SIPTA] IJAR- Special Issue on DM with imprecise information

by IGNACIO MONTES GUTIERREZ

Oct. 5, 2018