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Various structured argumentation frameworks utilize preferences as part of their standard inference procedure to enable reasoning with preferences. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify…

Artificial Intelligence · Computer Science 2020-05-13 Quratul-ain Mahesar , Nir Oren , Wamberto W. Vasconcelos

While a large body of work has scrutinized the meaning of conditional sentences, considerably less attention has been paid to formal models of their pragmatic use and interpretation. Here, we take a probabilistic approach to pragmatic…

Computation and Language · Computer Science 2022-10-14 Britta Grusdt , Daniel Lassiter , Michael Franke

Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing uncertainty is the same. In the case of reasoning about…

Artificial Intelligence · Computer Science 2021-04-07 Matthew Harrison-Trainor , Wesley H. Holliday , Thomas F. Icard

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

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting…

Artificial Intelligence · Computer Science 2011-07-04 J. Keppens , Q. Shen

In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a multilayer neural network model. Weighted knowledge bases for a simple description logic with…

Artificial Intelligence · Computer Science 2023-09-20 Mario Alviano , Francesco Bartoli , Marco Botta , Roberto Esposito , Laura Giordano , Daniele Theseider Dupré

We are aiming at a semantics of logic programs with preferences defined on rules, which always selects a preferred answer set, if there is a non-empty set of (standard) answer sets of the given program. It is shown in a seminal paper by…

Logic in Computer Science · Computer Science 2011-09-05 Jan Sefranek , Alexander Simko

The following three sections and appendices are taken from my thesis "The Foundations of Inference and its Application to Fundamental Physics" from 2021, in which I construct a theory of entropic inference from first principles. The…

Other Statistics · Statistics 2022-07-19 Nicholas Carrara

A knowledge system S describing a part of real world does in general not contain complete information. Reasoning with incomplete information is prone to errors since any belief derived from S may be false in the present state of the world.…

Artificial Intelligence · Computer Science 2011-05-20 Eliezer L. Lozinskii

We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of the main approaches for solving this problem, but the…

Machine Learning · Computer Science 2021-10-26 Sina Akbari , Ehsan Mokhtarian , AmirEmad Ghassami , Negar Kiyavash

Inference systems are a widespread framework used to define possibly recursive predicates by means of inference rules. They allow both inductive and coinductive interpretations that are fairly well-studied. In this paper, we consider a…

Logic in Computer Science · Computer Science 2023-06-22 Francesco Dagnino

Probing experiments investigate the extent to which neural representations make properties -- like part-of-speech -- predictable. One suggests that a representation encodes a property if probing that representation produces higher accuracy…

Computation and Language · Computer Science 2021-09-21 John Hewitt , Kawin Ethayarajh , Percy Liang , Christopher D. Manning

This paper outlines a general formal framework for reasoning systems, intended to support future analysis of inference architectures across domains. We model reasoning systems as structured tuples comprising phenomena, explanation space,…

Artificial Intelligence · Computer Science 2025-08-05 Saleh Nikooroo , Thomas Engel

Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make and retract (tentative) conclusions from inconclusive evidence. This paper gives a possible-worlds interpretation of the nonmonotonic reasoning problem based on…

Artificial Intelligence · Computer Science 2013-04-10 Carl Kadie

Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database…

Computation and Language · Computer Science 2012-02-02 Yuriy Ostapov

Many systems that exhibit nonmonotonic behavior have been described and studied already in the literature. The general notion of nonmonotonic reasoning, though, has almost always been described only negatively, by the property it does not…

Artificial Intelligence · Computer Science 2007-05-23 Sarit Kraus , Daniel Lehmann , Menachem Magidor

In this article, we review selective inference, a set of techniques for inference when the statistical question asked is a function of the data. This setting often arises in contemporary scientific workflows, where hypotheses and parameters…

Methodology · Statistics 2026-04-14 Anna Neufeld , Ronan Perry , Daniela Witten

Stereotypical reasoning assumes that the situation at hand is one of a kind and that it enjoys the properties generally associated with that kind of situation. It is one of the most basic forms of nonmonotonic reasoning. A formal model for…

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

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound…

Artificial Intelligence · Computer Science 2013-03-08 Judea Pearl

Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal…

Machine Learning · Computer Science 2020-07-21 Purva Pruthi , Javier González , Xiaoyu Lu , Madalina Fiterau