O-Plan: the open planning architecture
Artificial Intelligence
Divide and conquer in multi-agent planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Analyzing external conditions to improve the efficiency of HTN planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Top-down search for coordinating the hierarchical plans of multiple agents
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Theory for coordinating concurrent hierarchical planning agents using summary information
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Abstract Reasoning for Planning and Coordination
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
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Recent research has provided methods for coordinating the individually formed concurrent hierarchical plans (CHiPs) of a group of agents in a shared environment. A reasonable criticism of this technique is that the summary information can grow exponentially as it is propagated up a plan hierarchy. This paper analyzes the complexity of the coordination problem to show that in spite of this exponential growth, coordinating CHiPs at higher levels is still exponentially cheaper than at lower levels. In addition, this paper offers heuristics, including "fewest threats first" (FTF) and "expand most threats first" (EMTF), that take advantage of summary information to smartly direct the search for a global plan. Experiments showthat for a particular domain these heuristics greatly improve the search for the optimal global plan compared to a "fewest alternatives first" (FAF) heuristic that has been successful in Hierarchical Task Network (HTN) Planning.