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A practical approach to error recovery for multiagent planning systems

Authors:

Hyugoo Han,

Kai-Hsiung ChangAuthors Info & Claims

CSC '90: Proceedings of the 1990 ACM annual conference on Cooperation

Pages 201 - 207

https://doi.org/10.1145/100348.100379

Published: 01 January 1990 Publication History

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Abstract

A practical approach to error recovery strategy for real-time multiagent environments is presented. In a dynamic domain, when things do not happen as a pre-generated plan has expected, parts of the plan should be recovered to cope with any unexpected situations rather than abandoning the original plan and replanning from scratch. In order to deal with such situations, domain heuristics are used to order and classify conditions, and a resource management scheme is developed. Two tables, Wedge Table and Action Table, are introduced for prompt location of the contaminated plan parts. Plan rationale and wedge structure of a plan tree are recorded in these two tables. In order to solve the truth maintenance problem, action dependency calculation is established. This error recovery approach for multiagent planning environments not only promises successful achievement of a goal but also secures the safety of facilities in a domain. It enhances system performance and flexibility due to the versatility in real-time plan execution.

References

[1]

Chang, K. H. and Edhala, M., "Execution Error ecovery for Planning Systems", IEEE Trans. on Systems, Men, and Cybernetics, Vol. 19, No. 1, 1989 pp. 130-134

Crossref

Google Scholar

[2]

Cheeseman, P,., 'A Reoresent, ation of Time for Automatic Planning, Proe. IEEE, Intl Conf. on Robotics, Atlanta, GA, Mar., 1984

Google Scholar

[3]

Fikes, R. E. and Nilsson, hi. J., "STRIPS: A New pproach to the ApNication of Theorem Proving to Problem Sorving", Artificiar'intelligence, Vol. 2, 1971, pp. 189-208

Crossref

Google Scholar

[4]

Fikes, R. E., Hart, P. E., and Nilsson, N.J., "Learning and Executing Generalized Robot Plans, Artificial Intelligence, Vol. 3, 1972, pp 251-288

Digital Library

Google Scholar

[5]

Gasser, L., Report on,the 1985 Wrokshop on Distributed Artificial Intelligence, AI Magazine, Vol. 8 No. 2, Summer 1987, pp. 91-97

Digital Library

Google Scholar

[6]

Georgeff, M., "Communication and Interaction in Multi- Agent PIanning, Readings ia Distributed Artificial Intelligence Editted by Bond, A. H. and Gasser, L., Morgan Kaufniann Pub.,I, nc, San Mateo, CA, 1988, pp. 200-204

Digital Library

Google Scholar

[7]

Han, H., Error Recovery for Multiagent Planaing Systems", Ph.D. Dissertation, Dept. of Computer Science an/t Engineering, Auburn University, AL, June 1990 (To appear)

Digital Library

Google Scholar

[8]

Lansk~, A. L., "A Representation of Parallel A/~tivity based on Events, Structure, and Causali~, Reasoning about Actions & Plans: Proc of 1986 Workshop Timberline, OR, June, 1986,pp. 123-159, Editted by (;eorgeff, M. and Lansky, A., Mdrgan Kaufm,~ Pub. Inc., Los Altos, CA

Google Scholar

[9]

R,osenschein, I. S., Synchronization of Multi-Agent Plans, Readings in Distributed Artificial Intelligence Editted by Bond, A. H. and Gasser, L., Morgan Kaufmann Pub. Inc, San Mateo, CA, 1988, pp. 187-191

Digital Library

Google Scholar

[10]

Sacerdoti, E. D., 'A Structure for Plans and Behavior, Elsevier, North-HoUand New York, NY 1977

Google Scholar

[11]

Stuart,C., "An Implementation of a Multi-Agent Plan yn. chronizer, Readings m Distributed Artificial Intelligence Editted by Bond, A. H. and Gasser, L., Morgan Kauf-mann Pub. Inc, San Mateo, CA, 1988, pp. 216-219

Digital Library

Google Scholar

[12]

Tate, A., "Generating t'rolect Networks, IICAI-77, Cambridge, MA, Aug.,197"F, pp. 888-893

Google Scholar

[13]

Wi}kins, D. E., "Recovering from Execution Errors in SIPE, Computational Intelligence, Vol. 1, 1985, pp. 33-45

Crossref

Google Scholar

Cited By

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Chang KDay WMatthews M(1990)Adaptive multiagent planning in a distributed environmentProceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 210.1145/98894.98936(828-837)Online publication date: 1-Jun-1990
https://dl.acm.org/doi/10.1145/98894.98936

Index Terms

A practical approach to error recovery for multiagent planning systems
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
    2. Search methodologies
      1. Discrete space search
      2. Game tree search
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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Published In

CSC '90: Proceedings of the 1990 ACM annual conference on Cooperation

January 1990

475 pages

ISBN:0897913485

DOI:10.1145/100348

Chairman:
Arun Sood
George Mason Univ., Fairfax, VA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 1990

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CSC90: 18th Annual ACM Computer Science Conference

February 20 - 22, 1990

D.C., Washington, USA

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Chang KDay WMatthews M(1990)Adaptive multiagent planning in a distributed environmentProceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 210.1145/98894.98936(828-837)Online publication date: 1-Jun-1990
https://dl.acm.org/doi/10.1145/98894.98936

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Error recovery for multiagent planning systems

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