Strategic Advice for Hierarchical Planners

AI planning systems have traditionally operated as stand-alone blackboxes, taking a description of a domain and a set of goals, and automatically synthesizing a plan for achieving those goals. Such designs severely restrict the in uence that users can have on the resultant plans. This paper describes an Advisable Planner framework that marries an advice-taking interface to AI planning technology. The framework is designed to enable users to interact with planning systems at high levels of abstraction in order to in uence the plan generation process in terms that are meaningful to them. Advice consists of taskspeci c constraints on both the desired solution and the re nement decisions that underlie the planning process. The paper emphasizes strategic advice, which expresses recommendations on how goals and actions are to be accomplished. The main contributions are a formal language and semantics for strategic advice, and a sound and complete HTNstyle algorithm for generating plans that satisfy advice.