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(l) I have enough evidence to render the sentence S probable. (la) So, relative to what I know, it is rational of me to believe S. (2) Now that I have more evidence, S may no longer be probable. (2a) So now, relative to what I know, it is…

Artificial Intelligence · Computer Science 2016-11-26 Henry E. Kyburg

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the…

Artificial Intelligence · Computer Science 2022-12-15 Christoph Jansen , Georg Schollmeyer , Thomas Augustin

In Historical Economics, Persistence studies document the persistence of some historical phenomenon or leverage this persistence to identify causal relationships of interest in the present. In this chapter, we analyze the implications of…

General Economics · Economics 2021-02-17 Alberto Bisin , Andrea Moro

This note is concerned with a formal analysis of the problem of non-monotonic reasoning in intelligent systems, especially when the uncertainty is taken into account in a quantitative way. A firm connection between logic and probability is…

Artificial Intelligence · Computer Science 2013-04-05 Hung-Trung Nguyen

One advantage of paraconsistent logic is that it can deal with inconsistencies without making the system trivial. However, unlike classical propositional calculus, its deductive system is limited, and the meaning of paraconsistent negation…

Logic · Mathematics 2025-10-14 Oscar Ramírez

The bilateralist approach to logical consequence maintains that judgments of different qualities should be taken into account in determining what-follows-from-what. We argue that such an approach may be actualized by a two-dimensional…

Logic in Computer Science · Computer Science 2021-07-20 Vitor Greati , Sérgio Marcelino , João Marcos

Possibility theory offers a framework where both Lehmann's "preferential inference" and the more productive (but less cautious) "rational closure inference" can be represented. However, there are situations where the second inference does…

Artificial Intelligence · Computer Science 2013-02-18 Salem Benferhat , Didier Dubois , Henri Prade

We generalize the classical probability frame by adopting a wider family of random variables that includes nondeterministic ones. The frame that emerges is known to host a ''classical'' extension of quantum mechanics. We discuss the notion…

Quantum Physics · Physics 2007-05-23 E. G. Beltrametti , S. Bugajski

In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) v D,…

Artificial Intelligence · Computer Science 2020-04-24 Laura Giordano , Valentina Gliozzi , Antonio Lieto , Nicola Olivetti , Gian Luca Pozzato

The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic…

Artificial Intelligence · Computer Science 2012-08-07 Didier Dubois , Helene Fargier

Starting from the observation that rational closure has the undesirable property of being an "all or nothing" mechanism, we here propose a multipreferential semantics, which enriches the preferential semantics underlying rational closure in…

Artificial Intelligence · Computer Science 2018-01-23 Laura Giordano , Valentina Gliozzi

We study many-to-one matching problems between institutions and individuals, where each institution may be matched to multiple individuals. The matching market includes couples, who view pairs of institutions as complementary. Institutions'…

Theoretical Economics · Economics 2025-07-11 Shashwat Khare , Souvik Roy

This paper presents a sound and completecalculus for causal relevance, based onPearl's functional models semantics.The calculus consists of axioms and rulesof inference for reasoning about causalrelevance relationships.We extend the set of…

Artificial Intelligence · Computer Science 2013-01-14 Blai Bonet

In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order) logic to represent (probabilistic) knowledge in a non-technical fashion. Our motivation is three fold. First, for…

Artificial Intelligence · Computer Science 2023-06-27 Vaishak Belle

We study a generalized family of stochastic orders, semiparametrized by a distortion function H, namely H-distorted stochastic dominance, which may determine a continuum of dominance relations from the first- to the second-order stochastic…

Statistics Theory · Mathematics 2019-09-12 Tommaso Lando , Lucio Bertoli-Barsotti

The semantic web is an open and distributed environment in which it is hard to guarantee consistency of knowledge and information. Under the standard two-valued semantics everything is entailed if knowledge and information is inconsistent.…

Artificial Intelligence · Computer Science 2014-05-01 Wenzhao Qiao , Nico Roos

This paper explores a new class of incomplete preferences -- termed ``connected preferences'' -- in which maximal domains of comparability are topologically connected. We provide necessary and sufficient conditions for continuous…

Theoretical Economics · Economics 2026-05-01 Leandro Gorno , Alessandro Rivello

Recently, it has been emphasized that the possibility theory framework allows us to distinguish between i) what is possible because it is not ruled out by the available knowledge, and ii) what is possible for sure. This distinction may be…

Artificial Intelligence · Computer Science 2013-01-07 Salem Benferhat , Didier Dubois , Souhila Kaci , Henri Prade

We generalise the Blok-J\'onsson account of structural consequence relations, later developed by Galatos, Tsinakis and other authors, in such a way as to naturally accommodate multiset consequence. While Blok and J\'onsson admit, in place…

Logic · Mathematics 2019-04-17 Petr Cintula , José Gil Férez , Tommaso Moraschini , Francesco Paoli

A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that…

Methodology · Statistics 2022-10-17 Ying Zhou , Dingke Tang , Dehan Kong , Linbo Wang
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