2011/04/27 by Tuan Nguyen, Tuan Anh Nguyen, Subbarao Kambhampati +5
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Multi-Agent Systems and Negotiation #cs.AI
paper · pdf · doi:10.48550/arxiv.1104.5069
arxiv created 2011/04/27 · openalex publication_date 2011/04/27 · arxiv updated 2011/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to generate plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness, and formalize the notion of plan robustness with respect to an incomplete domain model. We then propose an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem. We present experimental results with Probabilistic-FF, a state-of-the-art planner, showing the promise of our approach.