Abduction without minimality

Abhaya C. Nayak, Norman Y. Foo

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


In most accounts of common-sense reasoning, only the most preferred among models supplied by the evidence are retaiined (and the rest eUminated) in order to enheince the inferential prowess. One problem with this strategy is that the agent's working set of models shrinks quickly in the process. We argue that instead of rejecting all the nonbest models, the reasoner should reject only the worst models and then examine the consequences of adopting this principle in the context of abductive reasoning. Apart from providing the releveint representation results, we indicate why an iterated account of abduction is feasible in this framework.

Original languageEnglish
Title of host publicationAdvanced Topics in Artificial Intelligence
Subtitle of host publication12th Australian Joint Conference on Artificial Intelligence, AI’99 Sydney, Australia, December 6–10, 1999 Proceedings
EditorsNorman Foo
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Number of pages13
ISBN (Electronic)9783540466956
ISBN (Print)9783540668220
Publication statusPublished - 1999
Event12th Australian Joint Conference on Artificial Intelligence, AI 1999 - Sydney, Australia
Duration: 6 Dec 199910 Dec 1999

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th Australian Joint Conference on Artificial Intelligence, AI 1999


  • Belief revision
  • Common-sense reasoning
  • Philosophical foundations

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