The paradigm of advisable planning, in which a user provides guidance to influence the content of solutions produced by an underlying planning system, holds much promise for improved usability of planning technology. The success of this approach, however, requires that a planner respond appropriately when presented with conflicting advice. This paper introduces two contrasting methods for planning with conflicting advice, suited to different user requirements. Soft enforcement embodies a heuristic approach that prefers planning choices that are consistent with specified advice but will disregard advice that introduces conflicts. Soft enforcement enables rapid generation of solutions but with suboptimal results. Local maxima search navigates through the space of advice subsets, using strict enforcement techniques to identify satisfiable subsets of advice. As more time is allocated, the search will yield increasingly better results. The paper presents specific algorithms for soft enforcement and local maxima search, along with experimental results that illustrate their relative strengths and weaknesses in trading computation time for advice satisfaction.
展开▼
