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Belief and plausibility are weaker measures of uncertainty than that of probability. They are motivated by the situations when full probabilistic information is not available. However, information can also be contradictory. Therefore, the…

Artificial Intelligence · Computer Science 2022-05-31 Sabine Frittella , Ondrej Majer , Sajad Nazari

A new combinatorial-probabilistic diagnostic entropy has been introduced. It describes the pair-wise sum of probabilities of system conditions that have to be distinguished during the diagnosing process. The proposed measure describes the…

Information Theory · Computer Science 2009-09-29 Henryk Borowczyk

An elaboration of Dempster's method of constructing belief functions suggests a broadly applicable strategy for constructing lower probabilities under a variety of evidentiary constraints.

Artificial Intelligence · Computer Science 2013-03-08 Carl G. Wagner , Bruce Tonn

Data based judgments go into artificial intelligence applications but they undergo paradoxical reversal when seemingly unnecessary additional data is provided. Examples of this are Simpson's reversal and the disjunction effect where the…

Artificial Intelligence · Computer Science 2017-09-14 Subhash Kak

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

As a generalization of Dempster-Shafer theory, D number theory provides a framework to deal with uncertain information with non-exclusiveness and incompleteness. However, some basic concepts in D number theory are not well defined. In this…

Artificial Intelligence · Computer Science 2019-12-03 Xinyang Deng

A common feature of recent unification-based grammar formalisms is that they give the user the ability to define his own structures. However, this possibility is mostly limited and does not include nonmonotonic operations. In this paper we…

cmp-lg · Computer Science 2008-02-03 Lena Stromback

Recent works have investigated the problem of the conflict redistribution in the fusion rules of evidence theories. As a consequence of these works, many new rules have been proposed. Now, there is not a clear theoretical criterion for a…

Logic · Mathematics 2007-06-13 Frederic Dambreville

Poole has shown that nonmonotonic logics do not handle the lottery paradox correctly. In this paper we will show that Pollock's theory of defeasible reasoning fails for the same reason: defeasible reasoning is incompatible with the…

Artificial Intelligence · Computer Science 2013-02-08 Jens Doerpmund

The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that…

Artificial Intelligence · Computer Science 2013-02-18 Mathias Bauer

Logics for knowledge representation suffer from over-specialization: while each logic may provide an ideal representation formalism for some problems, it is less than optimal for others. A solution to this problem is to choose from several…

Artificial Intelligence · Computer Science 2007-05-23 G. Antoniou , D. Billigton , G. Governatori , M. J. Maher

We present a metagrammatical formalism, {\em generic rules}, to give a default interpretation to grammar rules. Our formalism introduces a process of {\em dynamic binding} interfacing the level of pure grammatical knowledge representation…

cmp-lg · Computer Science 2008-02-03 Julio Gonzalo , Teresa Solias

In this paper we study the uses and the semantics of non-monotonic negation in probabilistic deductive data bases. Based on the stable semantics for classical logic programming, we introduce the notion of stable formula, functions. We show…

Artificial Intelligence · Computer Science 2013-03-26 Raymond T. Ng , V. S. Subrahmanian

Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision…

Artificial Intelligence · Computer Science 2012-07-19 Leila Amgoud , Henri Prade

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been and still remains of primal importance for the development of reliable information fusion systems.…

Artificial Intelligence · Computer Science 2007-05-23 Jean Dezert , Florentin Smarandache

In this paper, we present a probabilistic adaptation of an Assume/Guarantee contract formalism. For the sake of generality, we assume that the extended state machines used in the contracts and implementations define sets of runs on a given…

Performance · Computer Science 2009-04-20 Benoît Delahaye , Benoît Caillaud

A considerable body of work in AI has been concerned with aggregating measures of confirmatory and disconfirmatory evidence for a common set of propositions. Claiming classical probability to be inadequate or inappropriate, several…

Artificial Intelligence · Computer Science 2013-04-15 Benjamin N. Grosof

We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the Dempster-Shafer theory of belief functions and possibility…

Artificial Intelligence · Computer Science 2022-02-17 Thierry Denoeux

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences…

Artificial Intelligence · Computer Science 2017-08-11 Yiru Zhang , Tassadit Bouadi , Arnaud Martin

The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on…

Computation and Language · Computer Science 2020-12-16 Faeze Brahman , Vered Shwartz , Rachel Rudinger , Yejin Choi