FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning
This work addresses the problem of balancing expressiveness and efficiency in temporal planning for AI researchers, though it appears incremental as it builds on existing ANML modeling and hierarchical methods.
The authors tackled the challenge of maintaining search efficiency in temporal planning while using expressive timeline representations, and their FAPE planner achieved competitive performance with less expressive planners and often superior results when hierarchical control knowledge was provided.
Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency. FAPE's representation coherently integrates flexible timelines with hierarchical refinement methods that can provide efficient control knowledge. A novel reachability analysis technique is proposed and used to develop causal networks to constrain the search space. It is employed for the design of informed heuristics, inference methods and efficient search strategies. Experimental results on common benchmarks in the field permit to assess the components and search strategies of FAPE, and to compare it to IPC planners. The results show the proposed approach to be competitive with less expressive planners and often superior when hierarchical control knowledge is provided. FAPE, a freely available system, provides other features, not covered here, such as the integration of planning with acting, and the handling of sensing actions in partially observable environments.