Case-based Planning with a High-Performance Parallel Memory
| dc.contributor.author | Kettler, Brian | en_US |
| dc.date.accessioned | 2004-05-31T22:36:02Z | |
| dc.date.available | 2004-05-31T22:36:02Z | |
| dc.date.created | 1995-11 | en_US |
| dc.date.issued | 1998-10-15 | en_US |
| dc.description.abstract | Case-based planning (CBP) systems, like other case-based reasoning systems, can take advantage of previous planning experience by reusing stored cases (plans) in similar situations in the future. Advantages of CBP include speedup over planning from scratch and the ability to function with limited causal domain knowledge. ``Traditional'' CBP systems with the latter advantage typically cannot produce plans from scratch because they lack the more powerful adaptation mechanisms of generative planning systems. These ``reuse-only'' CBP systems rely on retrieving a plan from the casebase that is close to a solution plan. This requires large casebases with good coverage of the problem space and the ability to encode and match cases at fine levels of detail. Many such CBP systems, however, have fallen short of these requirements. They support only small, pre-indexed casebases. Pre-indexing constrains retrieval, as does the use of less expressive feature-based case representation schemes. The encoding and matching of detailed structural relationships in cases is not possible in such systems. These systems often adapt a single plan to the target problem using methods that are ad hoc or heuristic. CAPER is a novel, domain-independent, case-based planning system with improvements over traditional reuse-only CBP systems from its use of techniques that exploit a high-performance parallel memory of cases. CAPER takes a memory-intensive approach by making frequent use of memory during all phases of planning and by using large casebases, which can be automatically seeded. Because the parallel retrieval mechanisms scale to real-world sized casebases of thousands of plans, memory does not have to be pre-indexed and thus retrieval is more flexible. Detailed queries can be used to match cases, which are stored using an expressive, graph-structured case representation scheme. Plan adaptation in CAPER borrows techniques from generative planning, such as the use of plan validations, which capture dependencies in a plan, and plan composition. These techniques are incorporated into a reuse-only CBP framework for a more principled approach to adaptation than in many reuse-only CBP systems. CAPER can also use its flexible retrieval mechanisms and case representations to retrieve patch or substitute plans from memory. (Also cross-referenced as UMIACS-TR-95-112) | en_US |
| dc.format.extent | 2977266 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/776 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-3561 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-95-112 | en_US |
| dc.title | Case-based Planning with a High-Performance Parallel Memory | en_US |
| dc.type | Technical Report | en_US |
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