Minimal Undefinedness for Fuzzy Answer Sets

Mario Alviano, Giovanni Amendola, Rafael Peñaloza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fuzzy Answer Set Programming (FASP) combines the non-monotonic reasoning typical of Answer Set Programming with the capability of Fuzzy Logic to deal with imprecise information and paraconsistent reasoning. In the context of paraconsistent reasoning, the fundamental principle of minimal undefinedness states that truth degrees close to 0 and 1 should be preferred to those close to 0.5, to minimize the ambiguity of the scenario. The aim of this paper is to enforce such a principle in FASP through the minimization of a measure of undefinedness. Algorithms that minimize undefinedness of fuzzy answer sets are presented, and implemented.
Original languageEnglish
Title of host publicationProceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA.
PublisherAAAI Press
Pages3694-3700
Number of pages7
ISBN (Print)978-1-57735-781-0
Publication statusPublished - 9 Feb 2017
EventThirty-First AAAI Conference on Artificial Intelligence - San Francisco, United States
Duration: 4 Feb 20179 Feb 2017
https://www.aaai.org/Conferences/AAAI/aaai17.php

Publication series

Name
PublisherAAAI
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThirty-First AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-17
CountryUnited States
CitySan Francisco
Period4/02/179/02/17
Internet address

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