Planning under uncertainty: structural assumptions and computational leverage
New directions in AI planning
Backtracking algorithms for disjunctions of temporal constraints
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Commitment-driven distributed joint policy search
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
A survey of automated web service composition methods
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
Software Engineering for Service-Oriented MAS
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
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We present a methodology for the composition of rich servicesthat exhibit temporal uncertainty and complex task dependencies.Our multi-agent approach incorporates temporal and stochastic planningparadigms and commitment-based negotiation to achieve coordinatedprovision of services with stochastic outcomes. This is all captured withina service-choreography protocol, by which agents can request services andreceive probabilistic temporal service promises, to iteratively convergeon coordinated behavior. We argue that such an approach partially decouplesthe problems of negotiating service interactions and computingservice policies, so as to more efficiently converge on good solutions.