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Goals, as typically conceived in AI planning, provide an insu cient basis for choice of action, and hence are de cient as the sole expression of an agent's objectives. Decision-theoretic utilities o er a more adequate basis, yet lack many of the computational advantages of goals. We provide a preferential semantics for goals that grounds them in decision theory and preserves the validity of some, but not all, common goal operations performed in planning. This semantic account provides a criterion for verifying the design of goal-based planning strategies, thus providing a new framework for knowledge-level analysis of planning systems.