Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Irrelevance reasoning in knowledge-based systems
Irrelevance reasoning in knowledge-based systems
Multiple roles, multiple teams, dynamic environment: autonomous Netrek agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Exploiting irrelevance reasoning to guide problem solving
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Hierarchical planning in a distributed environment
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Scope and abstraction: two criteria for localized planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Agent architectures for flexible, practical teamwork
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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
Conversational Case-Based Reasoning
Applied Intelligence
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
Abstract reasoning for planning and coordination
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Group planning with time constraints
Annals of Mathematics and Artificial Intelligence
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Efficient and effective distributed planning requires careful control over how much information the planning agents broadcast to one another. Sending too little information could result in incorrect plans, while sending too much information could overtax the distributed planning system's resources (bandwidth and computational power). Ideally, distributed planning systems would have an efficient technique for filtering a large amount of irrelevant information from the message stream while retaining all the relevant messages. This paper describes an approach to controlling information distribution among planning agents using irrelevance reasoning (Levy & Sagiv 1993). In this approach, each planning agent maintains a data structure encoding the planning effects that could potentially be relevant to each of the other agents, and uses this structure to decide which of the planning effects that it generates will be sent to other agents. We describe an implementation of this approach within a distributed version of the SIPE-2 planner. Our experiments with this implementation show two important benefits of the approach: first, a noticeable speedup of the distributed planners; second--and, we argue, more importantly--a substantial reduction in message traffic.