Massively parallel support for case-based planning

摘要

In case-based planning (CBP), previously generated plans are stored in memory and can be reused to solve similar planning problems in the future. CaPER system is a case-based planner that is being developed to take advantage of the efficiencies of plan re-use while addressing some of the problems and limitations of case-based planners that use serial retrieval procedures on an indexed memory. CaPER uses a massively parallel frame-based AI language (PARKA) and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large casebases can be used; and memory can be probed in numerous alternate ways, allowing more specific retrieval of stored plans that better fit the target problem with less adaptation. Empirical results for case retrieval, and some other systems that make use of massive parallelism for memory retrieval are discussed.