Distributed rational decision making
Multiagent systems
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Nearly deterministic abstractions of Markov decision processes
Eighteenth national conference on Artificial intelligence
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Semantical considerations on dialectical and practical commitments
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
FLECS: planning with a flexible commitment strategy
Journal of Artificial Intelligence Research
Agents towards vehicle routing problems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Desire-space analysis and action selection for multiple dynamic goals
CLIMA'04 Proceedings of the 5th international conference on Computational Logic in Multi-Agent Systems
An approach to understanding policy based on autonomy and voluntary cooperation
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
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Autonomous agents, by definition, have the freedom to make their own decisions. Rational agents execute actions that are in their "best interests" according to their desires. Action selection is complicated due to uncertainty when operating in a dynamic environment or where other agents can also influence the environment. This paper presents an action selection framework and algorithms that are rational with respect to multiple desires and responsive to changing desires. Coordination is layered on top of this framework by describing and analyzing how commitments affect the agents' desires in their action selection models. Commitments may have a positive or a negative effect on an agent's ability to satisfy its desires. This research uses simulation in the domain of UAV surveillance to experimentally explore the balance between under-commitment and over-commitment.