Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
Modeling Uncertainty and its Implications to Sophisticated Control in Tæms Agents
Autonomous Agents and Multi-Agent Systems
Mathematical programming for deliberation scheduling in time-limited domains
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Controlling deliberation in a Markov decision process-based agent
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Introduction to planning in multiagent systems
Multiagent and Grid Systems - Planning in multiagent systems
Resource-driven mission-phasing techniques for constrained agents in stochastic environments
Journal of Artificial Intelligence Research
Towards a model of social coherence in multi-agent organizations
COIN@AAMAS'10 Proceedings of the 6th international conference on Coordination, organizations, institutions, and norms in agent systems
Using conflict resolution to inform decentralized learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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TÆMS is a hierarchical modeling language capable of representing complex task networks with intra-task uncertainties and inter-task dependencies. The uncertainty and complexity of the application domains represented in TÆMS models often lead to very large state spaces, which push the need to design efficient solution algorithms for TÆMS problems. In this paper, we present a solver that integrates selective state space search techniques with state space decomposition techniques. Our experiments demonstrate that the solver can find an (approximately) optimal solution much faster than prior approaches.