State minimization re-visited

Norman Foo; Abhaya Nayak; Maurice Pagnucco; Dongmo Zhang

doi:10.1007/3-540-45656-2_14

State minimization re-visited

Norman Foo
, Abhaya Nayak
, Maurice Pagnucco
, Dongmo Zhang

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

1 Citation (Scopus)

Abstract

A well-known circumscription policy in situation calculus theories of actions is to minimize the Abnormality predicate by varying the Holds predicate. Unfortunately this admitted counter-intuitive models. A different policy of varying the Result function eliminated these models. Explanations of how it did this are not entirely satisfactory, but seem to appeal to informal notions of state minimization. We re-examine this policy and show that there are simple justifications for it that are based on classical automata theory. It incidentally turns out that the description “state minimization” for the varying Result policy is more accurate than the original nomenclature had intended.

Original language	English
Title of host publication	AI 2001
Subtitle of host publication	Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings
Editors	Markus Stumptner, Dan Corbett, Mike Brooks, Dan Corbett
Publisher	Springer Verlag
Pages	153-164
Number of pages	12
ISBN (Print)	9783540429609
DOIs	https://doi.org/10.1007/3-540-45656-2_14
Publication status	Published - 2001
Externally published	Yes
Event	14th Australian Joint Conference on Artificial Intelligence, AI 2001 - Adelaide, Australia Duration: 10 Dec 2001 → 14 Dec 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2256
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Australian Joint Conference on Artificial Intelligence, AI 2001
Country/Territory	Australia
City	Adelaide
Period	10/12/01 → 14/12/01

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

Access to Document

10.1007/3-540-45656-2_14

Cite this

Foo, N., Nayak, A., Pagnucco, M., & Zhang, D. (2001). State minimization re-visited. In M. Stumptner, D. Corbett, M. Brooks, & D. Corbett (Eds.), AI 2001: Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 153-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2256). Springer Verlag. https://doi.org/10.1007/3-540-45656-2_14

Foo, Norman ; Nayak, Abhaya ; Pagnucco, Maurice et al. / State minimization re-visited. AI 2001: Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings. editor / Markus Stumptner ; Dan Corbett ; Mike Brooks ; Dan Corbett. Springer Verlag, 2001. pp. 153-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "State minimization re-visited",

abstract = "A well-known circumscription policy in situation calculus theories of actions is to minimize the Abnormality predicate by varying the Holds predicate. Unfortunately this admitted counter-intuitive models. A different policy of varying the Result function eliminated these models. Explanations of how it did this are not entirely satisfactory, but seem to appeal to informal notions of state minimization. We re-examine this policy and show that there are simple justifications for it that are based on classical automata theory. It incidentally turns out that the description “state minimization” for the varying Result policy is more accurate than the original nomenclature had intended.",

author = "Norman Foo and Abhaya Nayak and Maurice Pagnucco and Dongmo Zhang",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 14th Australian Joint Conference on Artificial Intelligence, AI 2001 ; Conference date: 10-12-2001 Through 14-12-2001",

year = "2001",

doi = "10.1007/3-540-45656-2\_14",

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

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Foo, N, Nayak, A, Pagnucco, M & Zhang, D 2001, State minimization re-visited. in M Stumptner, D Corbett, M Brooks & D Corbett (eds), AI 2001: Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2256, Springer Verlag, pp. 153-164, 14th Australian Joint Conference on Artificial Intelligence, AI 2001, Adelaide, Australia, 10/12/01. https://doi.org/10.1007/3-540-45656-2_14

State minimization re-visited. / Foo, Norman; Nayak, Abhaya; Pagnucco, Maurice et al.
AI 2001: Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings. ed. / Markus Stumptner; Dan Corbett; Mike Brooks; Dan Corbett. Springer Verlag, 2001. p. 153-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2256).

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

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Foo N, Nayak A, Pagnucco M, Zhang D. State minimization re-visited. In Stumptner M, Corbett D, Brooks M, Corbett D, editors, AI 2001: Advances in Artificial Intelligence - 14th Australian Joint Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2001. p. 153-164. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45656-2_14

State minimization re-visited

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