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The traditional AI definition of planning provides only a very narrow notion of plan quality, namely that a plan is good just in case it achieves a specified goal. For many applications, this black-or-white notion of quality is insufficiently expressive. My work extends the definition of planning to provide a much richer conception of plan quality.
I propose that a planning problem should be posed in terms of a value function, and that planning should be seen as an optimization process. I provide a class of goal-directed value functions which is strictly more expressive than the kind of goal formulas used by classical planning. Such value functions allow one to express not just one's goals, but also how important those goals are, ways in which they may be partially achieved, and the worth of resources that may be consumed by a plan. I will describe Pyrrhus, an optimizing planning algorithm for this class of value functions, and discuss empirical investigations into its heuristic effectiveness.