Rational sensing for an AI planner: a cost-based approach (original) (raw)

1993

Abstract

Task planning involves the construction of plans to accomplish goals and the execution of these plans in the task environment by an agent. We have developed a planner/executor that can plan to perform sensor operations which allow an agent to gather the information necessary to complete planning and achieve its goals in the face of missing or uncertain environmental information. The problem can be viewed as one of choosing among various sensing policies in order to maximize some reward (a successful plan) or minimize some cost (execution plan length). Determining an optimal policy for a given planning problem consists of computing tradeoffs between domain-specific factors such as sensor reliabilities, the cost of firing sensors, premature action recovery costs, bad data recovery costs, and the cost of human intervention. Some of these costs are determined while others are analytically derived. Exhaustive strategies for this computation become intractable for even a modest degree of environmental uncertainty. A major objective of this research is to suggest computationally feasible ways to solve this problem. Two methods will be presented in two distinct demonstration domains. The first method, static sensor scheduling, involves probabilistically constructing a sensor schedule offline which is used by the planner/executor to minimize expected plan length. The second, dynamic sensor selection, consists of building a directed graph offline and using actual world states as an index into the graph as planning and execution progress. Both algorithms have been implemented and can be shown to be polynomial in both time and space complexity.

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