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Fuzziness and randomicity widespread exist in natural science, engineering, technology and social science. The purpose of this paper is to present a new logic - uncertain propositional logic which can deal with both fuzziness by taking…

Logic · Mathematics 2015-06-11 Maokang Luo , Wei He

Predicting the future is an important component of decision making. In most situations, however, there is not enough information to make accurate predictions. In this paper, we develop a theory of causal reasoning for predictive inference…

Artificial Intelligence · Computer Science 2013-04-10 Thomas L. Dean , Keiji Kanazawa

This comparative survey explores three formal approaches to reasoning with partly true statements and degrees of truth, within the family of {\L}ukasiewicz logic. These approaches are represented by infinite-valued {\L}ukasiewicz logic…

Logic in Computer Science · Computer Science 2021-12-14 Zuzana Haniková

Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the…

Artificial Intelligence · Computer Science 2014-07-29 Joseph Y. Halpern , Riccardo Pucella

We address the problem of compiling defeasible theories to Datalog$^\neg$ programs. We prove the correctness of this compilation, for the defeasible logic $DL(\partial_{||})$, but the techniques we use apply to many other defeasible logics.…

Logic in Computer Science · Computer Science 2024-08-28 Michael J. Maher

In this article, the decidability and computability issues of dynamic probability logic (DPL) are addressed. Firstly, a proof system $\mathcal{H}_{DPL}$ is introduced for DPL and shown that it is weakly complete. Furthermore, this logic has…

Logic in Computer Science · Computer Science 2024-06-25 Somayeh Chopoghloo , Mahdi Heidarpoor , Massoud Pourmahdian

We present a declarative language, PP, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional…

Artificial Intelligence · Computer Science 2009-09-29 Tran Cao Son , Enrico Pontelli

Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly entailed by a premise, but rather only likely given the…

Computation and Language · Computer Science 2026-03-02 Gaurav Kamath , Sreenath Madathil , Sebastian Schuster , Marie-Catherine de Marneffe , Siva Reddy

Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking. However, there is an ongoing debate about the extent to which LLMs are…

Machine Learning · Computer Science 2024-08-16 Javier González , Aditya V. Nori

Convincing someone of the truth value of a premise requires understanding and articulating the core logical structure of the argument which proves or disproves the premise. Understanding the logical structure of an argument refers to…

Computation and Language · Computer Science 2025-08-21 Krunal Shah , Dan Roth

Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable…

Artificial Intelligence · Computer Science 2012-02-20 Vibhav Gogate , Pedro Domingos

Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In…

Computation and Language · Computer Science 2022-10-27 Jiangjie Chen , Qiaoben Bao , Changzhi Sun , Xinbo Zhang , Jiaze Chen , Hao Zhou , Yanghua Xiao , Lei Li

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine…

Artificial Intelligence · Computer Science 2019-08-19 Francesco Olivieri , Guido Governatori , Claudio Tomazzoli , Matteo Cristani

This extended abstract presents a logic, called Lp, that is capable of representing and reasoning with a wide variety of both qualitative and quantitative statistical information. The advantage of this logical formalism is that it offers a…

Artificial Intelligence · Computer Science 2013-04-08 Fahiem Bacchus

Representing defeasibility is an important issue in common sense reasoning. In reasoning about action and change, this issue becomes more difficult because domain and action related defeasible information may conflict with general inertia…

Artificial Intelligence · Computer Science 2007-05-23 Yan Zhang

Recent authors have proposed analyzing conditional reasoning through a notion of intervention on a simulation program, and have found a sound and complete axiomatization of the logic of conditionals in this setting. Here we extend this…

Artificial Intelligence · Computer Science 2018-07-31 Duligur Ibeling

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

Existential fixed point logic (EFPL) is a natural fit for some applications, and the purpose of this talk is to attract attention to EFPL. The logic is also interesting in its own right as it has attractive properties. One of those…

Logic in Computer Science · Computer Science 2008-11-07 Andreas Blass , Yuri Gurevich

We extend description logics (DLs) with non-monotonic reasoning features. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann and Magidor in the propositional…

Artificial Intelligence · Computer Science 2019-04-17 Katarina Britz , Giovanni Casini , Thomas Meyer , Kody Moodley , Uli Sattler , Ivan Varzinczak

The combination of evidence in Dempster-Shafer theory is compared with the combination of evidence in probabilistic logic. Sufficient conditions are stated for these two methods to agree. It is then shown that these conditions are minimal…

Artificial Intelligence · Computer Science 2013-04-11 Daniel Hunter