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In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. Our approach can be applied to more realistic scenarios than are typically studied in most previous work, including agents(More)
In recent years, the power of domain−independent planners which use hand coded domain−specific control knowledge has been demonstrated [AIP00]. This approach, though fruitful in terms of planner performance, has several issues asociated with it. Firstly, control rules need to be hand coded for each domain. This affords no scope for reuse, and the control(More)
In recent years, post-silicon debugging has become a significantly difficult exercise due to the increase in the size of the electrical state of the IC being debugged, coupled with the limited fraction of this state that is visible to the debug engineer. As the number of transistors increases, the number of possible electrical states increases(More)
Knowledge engineering for AI planning is the process that deals with the acquisition, validation and maintenance of planning domain models, and the selection and optimization of appropriate planning machinery to work on them. Evidence from the growing body of experience in applying planning technology suggests that knowledge engineering issues are crucial(More)
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