Massively parallel support for a case-based planning system

Case-based planning (CBP), a kind of case-based reasoning, is a technique in which previously generated plans (cases) are stored in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over generative planning, in which a new plan is produced from scratch. CBP thus offers a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory to reduce retrieval times. This approach requires significant domain engineering and complex memory indexing schemes to make these planners efficient. In contrast, our CBP system, 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 case bases can be used; memory can be probed in numerous alternate ways; and queries can be made at several levels, allowing more specific retrieval of stored plans that better fit the target problem with less adaptation. In this paper we describe CaPER's case retrieval techniques and some experimental results showing its good performance, even on large case bases

The Case for Structure-based Representations

Case-based reasoning involves reasoning from {\em cases}: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. As a result, case representation is critical: an incomplete case representation limits the system's reasoning power. In this paper we argue for {\em structure-based} case representations, which express arbitrary relations among objects in a flexible way, over more limited or inflexible methods. We motivate the distinction between these kinds of representations with examples from information retrieval systems, CBR systems, and computational models of human analogical reasoning. Structure-based representations provide the benefits of greater expressivity and economy. We give examples of these benefits from two case-based planning systems we have developed, CaPER and CHIRON, and show how the case matching and case acquisition costs can be reduced through the use of massively parallel techniques. (Also cross-referenced as UMIACS-TR-95-56

Massively Parallel Matching of Knowledge Structures

As knowledge bases used for AI systems increase in size, access to relevant information is the dominant factor in the cost of inference. This is especially true for analogical (or case-based) reasoning, in which the ability of the system to perform inference is dependent on efficient and flexible access to a large base of exemplars (cases) judged likely to be relevant to solving a problem at hand. In this chapter we discuss a novel algorithm for efficient associative matching of relational structures in large semantic networks. The structure matching algorithm uses massively parallel hardware to search memory for knowledge structures matching a given probe structure. The algorithm is built on top of PARKA, a massively parallel knowledge representation system which runs on the Connection Machine. We are currently exploring the utility of this algorithm in CaPER, a case-based planning system. Email: [email protected] y Email: [email protected] z Email: [email protected] x Email:..