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Related papers: Modal Logics for Qualitative Possibility and Belie…

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As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing…

Artificial Intelligence · Computer Science 2013-02-08 Urszula Chajewska , Joseph Y. Halpern

We obtain, for the first time, a modular many-valued semantics for combined logics, which is built directly from many-valued semantics for the logics being combined, by means of suitable universal operations over partial non-deterministic…

Logic · Mathematics 2024-05-22 Carlos Caleiro , Sérgio Marcelino

We explore the theory of illfounded and cyclic proofs for the propositional modal $\mu$-calculus. A fine analysis of provability for classical and intuitionistic modal logic provides a novel bridge between finitary, cyclic and illfounded…

Logic · Mathematics 2025-09-03 Bahareh Afshari , Graham E. Leigh , Guillermo Menèndez Turata

This paper from 2008 is the first in a series of three related papers on modal methods in interpretability logics and applications. In this first paper the foundations are laid for later results. These foundations consist of a thorough…

Logic · Mathematics 2020-04-16 Evan Goris , Joost J. Joosten

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

The framework of Pearl's Causal Hierarchy (PCH) formalizes three types of reasoning: probabilistic (i.e. purely observational), interventional, and counterfactual, that reflect the progressive sophistication of human thought regarding…

Artificial Intelligence · Computer Science 2025-02-07 Julian Dörfler , Benito van der Zander , Markus Bläser , Maciej Liskiewicz

The combined approach of the Qualitative Reasoning and Probabilistic Functions for the knowledge representation is proposed. The method aims at represent uncertain, qualitative knowledge that is essential for the moving blocks task's…

Robotics · Computer Science 2013-07-30 P. A. Wałȩga

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with…

Computation and Language · Computer Science 2021-03-03 Noortje J. Venhuizen , Petra Hendriks , Matthew W. Crocker , Harm Brouwer

The first contribution of this paper is the presentation of a Pavelka - like formulation of possibilistic logic in which the language is naturally enriched by two connectives which represent negation (eg) and a new type of conjunction…

Artificial Intelligence · Computer Science 2013-02-21 Luca Boldrin , Claudio Sossai

We design an expansion of Belnap--Dunn logic with belief and plausibility functions that allow non-trivial reasoning with inconsistent and incomplete probabilistic information. We also formalise reasoning with non-standard probabilities and…

Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are…

Artificial Intelligence · Computer Science 2021-08-31 Tim French , Tom Smoker

We present a family of paraconsistent counterparts of the constructive modal logic CK. These logics aim to formalise reasoning about contradictory but non-trivial propositional attitudes like beliefs or obligations. We define their…

Logic in Computer Science · Computer Science 2025-08-26 Han Gao , Daniil Kozhemiachenko , Nicola Olivetti

The generation of comprehensible explanations is an essential feature of modern artificial intelligence systems. In this work, we consider probabilistic logic programming, an extension of logic programming which can be useful to model…

Artificial Intelligence · Computer Science 2023-08-17 Germán Vidal

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed…

Artificial Intelligence · Computer Science 2019-06-25 Vaishak Belle , Brendan Juba

The aim of this paper is to propose a generalization of previous approaches in qualitative decision making. Our work is based on the binary possibilistic utility (PU), which is a possibilistic counterpart of Expected Utility (EU).We first…

Artificial Intelligence · Computer Science 2012-07-09 Paul Weng

Bayesian reasoning plays a significant role both in human rationality and in machine learning. In this paper, we introduce transfinite modal logic, which combines modal logic with ordinal arithmetic, in order to formalize Bayesian reasoning…

Artificial Intelligence · Computer Science 2022-04-08 Xinyu Wang

Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those…

Artificial Intelligence · Computer Science 2007-05-23 Joseph Y. Halpern

Communication facilitates coordination, but coordination might fail if there's too much uncertainty. I discuss a scenario in which vagueness-driven uncertainty undermines the possibility of publicly sharing a belief. I then show that…

Computer Science and Game Theory · Computer Science 2021-06-23 Giorgio Sbardolini

Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly…

Artificial Intelligence · Computer Science 2014-01-16 Didier Dubois , Hélène Fargier , Jean-François Bonnefon

The fuzzy modality `probably` is interpreted over probabilistic type spaces by taking expected truth values. The arising probabilistic fuzzy description logic is invariant under probabilistic bisimilarity; more informatively, it is…

Logic in Computer Science · Computer Science 2019-06-05 Paul Wild , Lutz Schröder , Dirk Pattinson , Barbara König
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