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This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the…

Artificial Intelligence · Computer Science 2020-10-27 Antonis Kakas , Loizos Michael

This chapter presents probability logic as a rationality framework for human reasoning under uncertainty. Selected formal-normative aspects of probability logic are discussed in the light of experimental evidence. Specifically, probability…

Artificial Intelligence · Computer Science 2019-10-16 Niki Pfeifer

Standard models of multi-agent modal logic do not capture the fact that information is often \emph{ambiguous}, and may be interpreted in different ways by different agents. We propose a framework that can model this, and consider different…

Artificial Intelligence · Computer Science 2014-01-10 Joseph Y. Halpern , Willemien Kets

Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence,…

Artificial Intelligence · Computer Science 2020-09-15 Inga Ibs , Nico Potyka

A semantics is given to possibilistic logic, a logic that handles weighted classical logic formulae, and where weights are interpreted as lower bounds on degrees of certainty or possibility, in the sense of Zadeh's possibility theory. The…

Artificial Intelligence · Computer Science 2013-03-26 Jerome Lang , Didier Dubois , Henri Prade

In this talk - based on the results of a forthcoming paper (Coletti, Scozzafava and Vantaggi 2002), presented also by one of us at the Conference on "Non Classical Logic, Approximate Reasoning and Soft-Computing" (Anacapri, Italy, 2001) -…

Probability · Mathematics 2007-05-23 Giulianella Coletti , Romano Scozzafava , Barbara Vantaggi

It is claimed that a variety of facts concerning ellipsis, event reference, and interclausal coherence can be explained by two features of the linguistic form in question: (1) whether the form leaves behind an empty constituent in the…

cmp-lg · Computer Science 2008-02-03 Andrew Kehler

The well-studied notion of deductive explosion describes the situation where any formula can be deduced from an inconsistent set of formulas. Paraconsistent logic, on the other hand, is the umbrella term for logical systems where the…

Logic in Computer Science · Computer Science 2011-11-14 Can Baskent

We combine the concepts of modal logics and many-valued logics in a general and comprehensive way. Namely, given any finite linearly ordered set of truth values and any set of propositional connectives defined by truth tables, we define the…

Logic in Computer Science · Computer Science 2025-01-03 Amir Karniel , Michael Kaminski

Inclusion logic is a variant of dependence logic that was shown to have the same expressive power as positive greatest fixed-point logic. Inclusion logic is not axiomatizable in full, but its first-order consequences can be axiomatized. In…

Logic · Mathematics 2020-01-22 Fan Yang

In this paper an approach to automated deduction under uncertainty,based on possibilistic logic, is proposed ; for that purpose we deal with clauses weighted by a degree which is a lower bound of a necessity or a possibility measure,…

Artificial Intelligence · Computer Science 2013-04-08 Didier Dubois , Jerome Lang , Henri Prade

We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of $n$ conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional…

Probability · Mathematics 2019-09-27 Angelo Gilio , Giuseppe Sanfilippo

Ambiguity is a natural language phenomenon occurring at different levels of syntax, semantics, and pragmatics. It is widely studied; in Psycholinguistics, for instance, we have a variety of competing studies for the human disambiguation…

Computation and Language · Computer Science 2023-11-16 Daphne Wang , Mehrnoosh Sadrzadeh

When reasoning in description, modal or temporal logics it is often useful to consider axioms representing universal truths in the domain of discourse. Reasoning with respect to an arbitrary set of axioms is hard, even for relatively…

Logic in Computer Science · Computer Science 2007-05-23 Ian Horrocks , Stephan Tobies

The notion of events has occupied a central role in modeling and has an influence in computer science and philosophy. Recent developments in diagrammatic modeling have made it possible to examine conceptual representation of events. This…

Artificial Intelligence · Computer Science 2017-04-28 Sabah Al-Fedaghi

In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract…

Artificial Intelligence · Computer Science 2024-02-07 Y. Munro , C. Sarmiento , I. Bloch , G. Bourgne , M. -J. Lesot

The standard approach to logic in the literature in philosophy and mathematics, which has also been adopted in computer science, is to define a language (the syntax), an appropriate class of models together with an interpretation of…

Artificial Intelligence · Computer Science 2009-09-25 Joseph Y. Halpern

We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces. This enables a semantics for conditionals founded on a calculus of intervention, and axiomatization of causal reasoning for…

Artificial Intelligence · Computer Science 2021-06-03 Duligur Ibeling , Thomas Icard

This paper revisits the multi-agent epistemic logic presented in [10], where agents and sets of agents are replaced by abstract, intensional "names". We make three contributions. First, we study its model theory, providing adequate notions…

Logic in Computer Science · Computer Science 2021-06-23 Marta Bílková , Zoé Christoff , Olivier Roy

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this…

Machine Learning · Computer Science 2021-08-09 Khimya Khetarpal , Zafarali Ahmed , Gheorghe Comanici , Doina Precup