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The aim of this paper is to propose a many-valued modal framework to formalize reasoning with both graded preferences and propositions, in the style of van Benthem et al.'s classical modal logics for preferences. To do so, we start from Bou…

Logic in Computer Science · Computer Science 2019-11-18 Amanda Vidal , Francesc Esteva , Lluis Godo

In a standard possibilistic logic, prioritized information are encoded by means of weighted knowledge base. This paper proposes an extension of possibilistic logic for dealing with partially ordered information. We Show that all basic…

Artificial Intelligence · Computer Science 2012-12-12 Salem Benferhat , Sylvain Lagrue , Odile Papini

This note introduces the concept of a partially specified prior distribution for certain post hoc inference problems, where a finite population is sampled once in order to make a decision on the presence or complete absence of some…

Statistics Theory · Mathematics 2008-07-17 K. Govindaraju , G. Jones

Model theoretic results such as Characterization and Definability give important information about different logics. It is well known that the proofs of those results for several modal logics have, somehow, the same 'taste'. A general proof…

Logic in Computer Science · Computer Science 2010-11-23 Facundo Carreiro

We investigate how the choice of decision makers can be varied under the presence of risk and uncertainty. Our analysis is based on the approach we have previously applied to individual decision makers, which we now generalize to the case…

Physics and Society · Physics 2014-09-03 V. I. Yukalov , D. Sornette

This paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature…

Artificial Intelligence · Computer Science 2013-03-26 Wray L. Buntine

This paper studies a decentralized many-to-one matching market where preferences remain uncertain during the matching process. Institutions initiate matching by sending offers, and applicants decide whether to accept upon receiving them.…

Theoretical Economics · Economics 2025-11-14 Yu-Ting Ho

Two special situations where the standard uncertainty product inequality appears to be useless are modified. One such case is noted to also trivialize the recently-introduced alternatives [Phys. Rev. Lett. 113, 260401 (2014); Sci. Rep. 6,…

Quantum Physics · Physics 2016-12-30 Kamal Bhattacharyya

People rationalize their past choices, even those that were mistakes in hindsight. We propose a formal theory of this behavior. The theory predicts that sunk costs affect later choices. Its model primitives are identified by choice behavior…

Theoretical Economics · Economics 2022-03-21 Erik Eyster , Shengwu Li , Sarah Ridout

In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first…

Statistics Theory · Mathematics 2012-09-25 M. J. Bayarri , J. O. Berger , A. Forte , G. García-Donato

When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable properties in their construction of explanations. In this work, we…

Machine Learning · Computer Science 2025-07-22 Hiwot Belay Tadesse , Alihan Hüyük , Yaniv Yacoby , Weiwei Pan , Finale Doshi-Velez

We present a general logical framework for reasoning about agents' cognitive attitudes of both epistemic type and motivational type. We show that it allows us to express a variety of relevant concepts for qualitative decision theory…

Artificial Intelligence · Computer Science 2023-06-22 Emiliano Lorini

Possibilistic logic offers a qualitative framework for representing pieces of information associated with levels of uncertainty of priority. The fusion of multiple sources information is discussed in this setting. Different classes of…

Artificial Intelligence · Computer Science 2013-01-18 Salem Benferhat , Didier Dubois , Souhila Kaci , Henri Prade

Preferential Bayesian optimization allows optimization of objectives that are either expensive or difficult to measure directly, by relying on a minimal number of comparative evaluations done by a human expert. Generating candidate…

If uncertainty is modelled by a probability measure, decisions are typically made by choosing the option with the highest expected utility. If an imprecise probability model is used instead, this decision rule can be generalised in several…

Artificial Intelligence · Computer Science 2020-03-27 Jasper De Bock

It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency.…

Artificial Intelligence · Computer Science 2022-01-25 Xiuyi Fan , Francesca Toni

Tarski gave a general semantics for deductive reasoning: a formula a may be deduced from a set A of formulas iff a holds in all models in which each of the elements of A holds. A more liberal semantics has been considered: a formula a may…

Artificial Intelligence · Computer Science 2007-05-23 Daniel Lehmann

In order to enrich dynamic semantic theories with a `pragmatic' capacity, we combine dynamic and nonmonotonic (preferential) logics in a modal logic setting. We extend a fragment of Van Benthem and De Rijke's dynamic modal logic with…

cmp-lg · Computer Science 2008-02-03 Jan Jaspars , Megumi Kameyama

We propose and solve a negotiation model of multiple players facing many alternative solutions. The model can be generalized to many relevant circumstances where stakeholders' interests partially overlap and partially oppose. We also show…

Physics and Society · Physics 2022-02-01 Izat B. Baybusinov , Enrico Maria Fenoaltea , Yi-Cheng Zhang

Preference elicitation explicitly asks users what kind of recommendations they would like to receive. It is a popular technique for conversational recommender systems to deal with cold-starts. Previous work has studied selection bias in…

Information Retrieval · Computer Science 2024-05-02 Shashank Gupta , Harrie Oosterhuis , Maarten de Rijke
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