Decision-Theoretic and Symbolic Planning

随着人工智能规划系统应用于更现实的问题， 出现了在备选方案中进行选择的问题。 的 规划师通常无法证明任何替代方案将或将 不成功，所以它在计划中选择的基础上， 他们实现目标的可能性和效率 他们这样做。 决策理论提供了一个规范模型， 做出这些选择，但不直接适用于 规划问题，因为人工智能提出了它，更重要的是， 没有提供一个计算框架， 选择. 特别是，决策理论没有提供任何建议， 生成或修改计划。 决策理论与符号人工智能 因此，规划算法提供了互补的优势： 前者提供了一个丰富的表示来表达选择， 以及如何做出这些选择的理论 合理;后者提供了一种计算方法， 制定和改进计划，但在限制性条件下， 假设。 这两种方法将结合起来， 实用的决策理论规划者。 该研究包含 理论和计算;方面，前者面向 协调决策理论和人工智能问题 代表，后者面向开发一个 高效构建、评估和选择的算法 计划替代方案。

As AI planning systems are applied to more realistic problems, the issue of choosing among alternative plans arises. The planner typically cannot prove that any alternative will or will not be successful, and so it much choose among plans on the basis of how likely they are to achieve their aims and how efficiently they do so. Decision theory supplies a normative model for making these choices, but is not directly applicable to the planning problem as AI has posed it, and more importantly does not provide a computational framework within which to make the choices. In particular, decision theory offers no advice on how to generate or modify plans. Decision theory and symbolic AI planning algorithms therefore offer complementary strengths: the former provides a rich representation for expressing choice among alternatives and a theory of how to make those choices rationally; the latter provides a computational method for generating and improving plans, but under restrictive assumptions. The two approaches will be integrated to produce a practical decision-theoretic planner. The research contains theoretical and computational; aspects, the former oriented toward reconciling the decision-theoretic and AI problem representations, the latter oriented toward developing an algorithm that efficiently builds, evaluated, and chooses among plan alternatives.