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Similarity-Based Inconsistency-Tolerant Logics

  • Ofer Arieli
  • Anna Zamansky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6341)

Abstract

Many logics for AI applications that are defined by denotational semantics are trivialized in the presence of inconsistency. It is therefore often desirable, and practically useful, to refine such logics in a way that inconsistency does not cause the derivation of any formula, and, at the same time, inferences with respect to consistent premises are not affected. In this paper, we introduce a general method of doing so by incorporating preference relations defined in terms of similarities. We exemplify our method for three of the most common denotational semantics (standard many-valued matrices, their non-deterministic generalization, and possible worlds semantics), and demonstrate their usefulness for reasoning with inconsistency.

Keywords

Aggregation Function Denotational Semantic World Semantic Investment House Strict Partial Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ofer Arieli
    • 1
  • Anna Zamansky
    • 2
  1. 1.Department of Computer ScienceThe Academic College of Tel-AvivIsrael
  2. 2.Department of Software EngineeringJerusalem College of EngineeringIsrael

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