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.
In contrast, our CBP system, CaPER, is based on a massively 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 a target problem with less adaptation. The flexible nature of case retrieval is also being exploited by treating the retrieval task itself as a planning problem, distinct from the overall planning task in which it is embedded. This "planning to retrieve" approach was motivated by the results of psychologists' studies of human long-term memory.
The CaPER prototype is being tested in a transport logistics planning domain, UM Translog.