Abstract
Conjunctive match is often used in Artificial Intelligence as the kernel of a pattern-directed inference [37] engine. Conjunctive match entails generating and testing all possible combinations of objects against a pattern of constraints. While simple to program, it is an expensive, exponential cost computation. To reduce this average match cost in production system engines, the RETE match algorithm [8] was devised. RETE compiles each rule's pattern of constraints into a network, and then incrementally updates partial matches as objects are inserted and deleted. RETE, however, has its own cost: conceptual and implementational complexity. Call-graph caching (CGC) [20] is a mechanism for transforming recursive specifications into highly optimized networks. In this paper, we describe CGC, and use it to transform a family of recursive conjunctive match formulations into their corresponding RETE networks. Our approach illustrates the ideas behind RETE, and shows their application to other algorithms.
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