High Performance Case-based Planning

Parallel Understanding Systems Group
Computer Science Department, University of Maryland at College Park

Our Work:

In case-based planning (CBP), previously generated plans are stored as cases in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over planning from scratch (generative planning), thus offering a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory that requires significant domain engineering and complex memory indexing schemes to enable efficient case retrieval. We are investigating the use of massively parallelism to access large memories of hundreds or more cases. These memories are structure-based and lack special-purpose indexing structures. Case memories are implemented using Parka, a massively parallel frame system.

Our Systems:

o CaPER, a domain-independent case-based planning system being tested in the domain of transport logistics planning.
o CHIRON, a case-based planning system for the domain of legal reasoning using open-textured concepts.

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