Artificial Intelligence - Special issue on knowledge representation
Algorithms for a temporal decoupling problem in multi-agent planning
Eighteenth national conference on Artificial intelligence
Path consistency on triangulated constraint graphs
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Optimal temporal decoupling in multiagent systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Solving the multiagent selection and scheduling problem
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Decoupling the multiagent disjunctive temporal problem
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Flexibility and decoupling in the simple temporal problem
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Distributed reasoning for multiagent simple temporal problems
Journal of Artificial Intelligence Research
Group planning with time constraints
Annals of Mathematics and Artificial Intelligence
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Scheduling agents can use the Multiagent Simple Temporal Problem (MaSTP) formulation to efficiently find and represent the complete set of alternative consistent joint schedules in a distributed and privacy-maintaining manner. However, continually revising this set of consistent joint schedules as new constraints arise may not be a viable option in environments where communication is uncertain, costly, or otherwise problematic. As an alternative, agents can find and represent a temporal decoupling in terms of locally independent sets of consistent schedules that, when combined, form a set of consistent joint schedules. Unlike current algorithms for calculating a temporal decoupling that require centralization of the problem representation, in this paper we present a new, provably correct, distributed algorithm for calculating a temporal decoupling. We prove that this algorithm has the same theoretical computational complexity as current state-of-the-art MaSTP solution algorithms, and empirically demonstrate that it is more efficient in practice. We also introduce and perform an empirical cost/benefit analysis of new techniques and heuristics for selecting a maximally flexible temporal decoupling.