Representing defeasible constraints and observations in action theories

Yan Zhang

doi:10.1007/bfb0095062

Representing defeasible constraints and observations in action theories

Yan Zhang

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

We propose a general formulation of reasoning about action based on prioritized logic programming, where defeasibility handling is explicitly taken into account. In particular, we consider two types of defeasibilities in our problem domains: defeasible constraints and defeasible observations. By introducing the notion of priority in action formulation, we show that our approach provides a unified framework to handle these defeasibilities in temporal prediction and postdiction reasoning with incomplete information.

Original language	English
Title of host publication	Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers
Editors	Grigoris Antoniou, John Slaney
Publisher	Springer Verlag
Pages	309-320
Number of pages	12
ISBN (Print)	3540651381, 9783540651383
DOIs	https://doi.org/10.1007/bfb0095062
Publication status	Published - 1998
Event	11th Australian Joint Conference on Artificial Intelligence, AI 98 - Brisbane, Australia Duration: 13 Jul 1998 → 17 Jul 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1502
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Australian Joint Conference on Artificial Intelligence, AI 98
Country/Territory	Australia
City	Brisbane
Period	13/07/98 → 17/07/98

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.

Keywords

Commonsense reasoning
Knowledge representation
Reasoning about action
Temporal reasoning

Access to Document

10.1007/bfb0095062

Cite this

Zhang, Y. (1998). Representing defeasible constraints and observations in action theories. In G. Antoniou, & J. Slaney (Eds.), Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers (pp. 309-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1502). Springer Verlag. https://doi.org/10.1007/bfb0095062

Zhang, Yan. / Representing defeasible constraints and observations in action theories. Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers. editor / Grigoris Antoniou ; John Slaney. Springer Verlag, 1998. pp. 309-320 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4ca84d29072447ab922d3cde08df021b,

title = "Representing defeasible constraints and observations in action theories",

abstract = "We propose a general formulation of reasoning about action based on prioritized logic programming, where defeasibility handling is explicitly taken into account. In particular, we consider two types of defeasibilities in our problem domains: defeasible constraints and defeasible observations. By introducing the notion of priority in action formulation, we show that our approach provides a unified framework to handle these defeasibilities in temporal prediction and postdiction reasoning with incomplete information.",

keywords = "Commonsense reasoning, Knowledge representation, Reasoning about action, Temporal reasoning",

author = "Yan Zhang",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1998.; 11th Australian Joint Conference on Artificial Intelligence, AI 98 ; Conference date: 13-07-1998 Through 17-07-1998",

year = "1998",

doi = "10.1007/bfb0095062",

language = "English",

isbn = "3540651381",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers",

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Zhang, Y 1998, Representing defeasible constraints and observations in action theories. in G Antoniou & J Slaney (eds), Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1502, Springer Verlag, pp. 309-320, 11th Australian Joint Conference on Artificial Intelligence, AI 98, Brisbane, Australia, 13/07/98. https://doi.org/10.1007/bfb0095062

Representing defeasible constraints and observations in action theories. / Zhang, Yan.
Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers. ed. / Grigoris Antoniou; John Slaney. Springer Verlag, 1998. p. 309-320 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1502).

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AB - We propose a general formulation of reasoning about action based on prioritized logic programming, where defeasibility handling is explicitly taken into account. In particular, we consider two types of defeasibilities in our problem domains: defeasible constraints and defeasible observations. By introducing the notion of priority in action formulation, we show that our approach provides a unified framework to handle these defeasibilities in temporal prediction and postdiction reasoning with incomplete information.

KW - Commonsense reasoning

KW - Knowledge representation

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KW - Temporal reasoning

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Zhang Y. Representing defeasible constraints and observations in action theories. In Antoniou G, Slaney J, editors, Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers. Springer Verlag. 1998. p. 309-320. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/bfb0095062

Representing defeasible constraints and observations in action theories

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