Planning under Uncertainty in Dynamic Domains - Carnegie Mellon ...

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AbstractPlanning, the process of nding a course of action which can be executed to achievesome goal, is an important and well-studied area of AI. One of the central assumptionsof classical AI-based planning is that after performing an action the resulting statecan be predicted completely and with certainty. This assumption has allowed thedevelopment of planning algorithms that provably achieve their goals, but it has alsohindered the use of planners in many real-world applications because of their inherentuncertainty.Recently, several planners have been implemented that reason probabilistically aboutthe outcomes of actions and the initial state of a planning problem. However, theirrepresentations and algorithms do not scale well enough to handle large problemswith many sources of uncertainty. This thesis introduces a probabilistic planningalgorithm that can handle such problems by focussing on a smaller set of relevantsources of uncertainty, maintained as the plan is developed. This is achieved by usingthe candidate plan to constrain the sources of uncertainty that are considered, incrementallyconsidering more sources as they are shown to be relevant. The algorithmis demonstrated in an implemented planner, called Weaver, that can handle uncertaintyabout actions taken by external agents, in addition to the kinds of uncertaintyhandled in previous planners. External agents may cause many simultaneous changesto the world that are not relevant to the success of a plan, making the ability todetermine and ignore irrelevant events a crucial requirement for an ecient planner.Three additional techniques are presented that improve the planner's eciency inanumber of domains. First, the possible external events are analyzed before planningtime to produce factored Markov chains which can greatly speed up the probabilisticevaluation of the plan when structural conditions are met. Second, domainindependentheuristics are introduced for choosing an incremental modication toapply to the current plan. These heuristics are based on the observation that thefailure of the candidate plan can be used to condition the probability that the modicationwill be successful. Third, analogical replay is used to share planning eortacross branches of the conditional plan. Empirical evidence shows that Weaver cancreate high-probability plans in a planning domain for managing the clean-up of oilspills at sea.iii
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