Robust scheduling and runtime adaptation of multi-agent plan execution

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Title: Robust scheduling and runtime adaptation of multi-agent plan execution
Author/Creator: Wang, Mingzhong | Ramamohanarao, K | Chen, J
Description: Robustness and reliability with respect to the successful completion of a schedule are crucial requirements for scheduling in multi-agent systems because agent autonomy makes execution environments dynamic and nondeterministic. We introduce a model to incorporate trust which indicates the probability that an agent will comply with its commitments into scheduling, thus improving the predicability and stability of the schedule. To deal with exceptions during execution, we adapt and evolve the schedule at runtime by interleaving the processes of evaluation, scheduling, execution and monitoring in the life cycle of a plan. Experiments show that schedules maximizing participants' trust are more likely to survive and succeed in open and dynamic environments. The results also prove that the proposed plan evaluation approach conforms with the simulation result, thus being helpful for plan selection. © 2008 IEEE.
Relation: IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), Sydney, Australia 9-12 December 2008
Relation: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT) / L C Jain; Yuefeng Li (eds): pp.366-372
Relation: http://dx.doi.org/10.1109/WIIAT.2008.136
Year: 2008
Publisher: IEEE Computer Society
Subject: FoR 0801 (Artificial Intelligence and Image Processing) | life cycle | scheduling | dynamic environments | execution environments | multi-agent plans | plan evaluations | robust scheduling | run-time | run-time adaptations | simulation results | multi agent systems

Resource Type: Conference Paper
Identifier: ISBN: 9780769534961
Rights: Copyright © 2008 IEEE. Reproduced here in accordance with the publisher's copyright policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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