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Planning actions for real robots in dynamic and uncertain environments is a challenging problem. Using a complete model of the world is not viable and an integration of deliberation and behavior-based re-active planning is most appropriate for goal achievement and uncertainty handling. This paper reports on our successful development of a system integrating(More)
Several tasks, such as plan reuse and agent modelling, need to interpret a given or observed plan to generate the underlying plan rationale. Although there are several previous methods that successfully extract plan rationales, they do not apply to complex plans, in particular to plans with actions that have conditional effects. In this paper, we introduce(More)
Because general-purpose planning methods have difficulty with large-scale planning problems, researchers have resorted to hand writing domain-specific planners to solve them. An interesting alternative is to use example plans to demonstrate how to solve problems in a particular domain and to use that information to automatically learn domain-specific(More)
Our work is driven by one of the core purposes of artificial intelligence: to develop real robotic agents that achieve complex high-level goals in real-time environments. Robotic behaviors select actions as a function of the state of the robot and of the world. Designing robust and appropriate robotic behaviors is a difficult because of noise, uncertainty(More)
The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government, or any other entity. For Jovan and Manuela, who made me do it. iv Abstract Automated problem solving involves the ability to select(More)