Individual Planning in Open and Typed Agent Systems

Muthukumaran Chandrasekaran, Adam Eck, Prashant Doshi, Leenkiat Soh
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:791-800, 2016.

Abstract

Open agent systems are multiagent systems in which one or more agents may leave the system at any time possibly resuming after some interval and in which new agents may also join. Planning in such systems becomes challenging in the absence of inter-agent communication because agents must predict if others have left the system or new agents are now present to decide on possibly choosing a different line of action. In this paper, we prioritize open systems where agents of differing types may leave and possibly reenter but new agents do not join. With the help of a realistic domain – wildfire suppression – we motivate the need for individual planning in open environments and present a first approach for robust decision-theoretic planning in such multiagent systems. Evaluations in domain simulations clearly demonstrate the improved performance compared to previous methods that disregard the openness.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-chandrasekaran16a, title = {Individual Planning in Open and Typed Agent Systems}, author = {Chandrasekaran, Muthukumaran and Eck, Adam and Doshi, Prashant and Soh, Leenkiat}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {791--800}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/chandrasekaran16a/chandrasekaran16a.pdf}, url = {https://proceedings.mlr.press/r14/chandrasekaran16a.html}, abstract = {Open agent systems are multiagent systems in which one or more agents may leave the system at any time possibly resuming after some interval and in which new agents may also join. Planning in such systems becomes challenging in the absence of inter-agent communication because agents must predict if others have left the system or new agents are now present to decide on possibly choosing a different line of action. In this paper, we prioritize open systems where agents of differing types may leave and possibly reenter but new agents do not join. With the help of a realistic domain – wildfire suppression – we motivate the need for individual planning in open environments and present a first approach for robust decision-theoretic planning in such multiagent systems. Evaluations in domain simulations clearly demonstrate the improved performance compared to previous methods that disregard the openness.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Individual Planning in Open and Typed Agent Systems %A Muthukumaran Chandrasekaran %A Adam Eck %A Prashant Doshi %A Leenkiat Soh %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-chandrasekaran16a %I PMLR %P 791--800 %U https://proceedings.mlr.press/r14/chandrasekaran16a.html %V R14 %X Open agent systems are multiagent systems in which one or more agents may leave the system at any time possibly resuming after some interval and in which new agents may also join. Planning in such systems becomes challenging in the absence of inter-agent communication because agents must predict if others have left the system or new agents are now present to decide on possibly choosing a different line of action. In this paper, we prioritize open systems where agents of differing types may leave and possibly reenter but new agents do not join. With the help of a realistic domain – wildfire suppression – we motivate the need for individual planning in open environments and present a first approach for robust decision-theoretic planning in such multiagent systems. Evaluations in domain simulations clearly demonstrate the improved performance compared to previous methods that disregard the openness. %Z Reissued by PMLR on 04 October 2026.
APA
Chandrasekaran, M., Eck, A., Doshi, P. & Soh, L.. (2016). Individual Planning in Open and Typed Agent Systems. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:791-800 Available from https://proceedings.mlr.press/r14/chandrasekaran16a.html. Reissued by PMLR on 04 October 2026.

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