In this talk I will motivate the need for handling incomplete specifications in planning, and describe the challenges foregrounded by such "model-lite" planning. I will start with the need for novel solution concepts for planning. I will present diverse plans and robust plans as the appropriate solution concepts for partially specified goals/preferences, and partially specified domain models respectively. I will then outline our progress towards efficient planners to support these solution concepts. I will also present open world quantified goals as a way of handling goal and model incompleteness together, and discuss how they are used in a planner that controls a robotic agent in a disaster rescue scenario.
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|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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