Fast planning through planning graph analysis
Artificial Intelligence
An Behavior-based Robotics
Accelerating partial-order planners: some techniques for effective search control and pruning
Journal of Artificial Intelligence Research
Flaw selection strategies for partial-order planning
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Monitoring the execution of robot plans using semantic knowledge
Robotics and Autonomous Systems
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Until recently, techniques for AI plan generation relied on highly restrictive assumptions that were almost always violated in real-world environments; consequently, robot designers adopted reactive architectures and avoided AI planning techniques. Some recent research efforts have focused on obviating such assumptions by developing techniques that enable the generation and execution of plans in dynamic, uncertain environments. In this paper, we discuss one such technique, rationale-based monitoring, originally introduced by Veloso, Pollack, and Cox (Proceedings for the Fourth International Conference on AI Planning Systems, Pittsburgh, PA, 1998, pp. 171–179) and we describe our use of it in a simple mobile robot environment. We review the original approach, describe how it can be adapted for a causal-link planner, and provide experimental results demonstrating that it can lead to improved plans without consuming excessive overhead. We also describe our use of rationale-based monitoring in a mobile robot office-assistant project currently in progress.