TRANSFORMING RECURSIVE PROCESSES INTO EFFICIENT ALGORITHMS: CALL-GRAPH CACHING FOR ARTIFICIAL INTELLIGENCE
Mark W. Perlin · International Journal of Artificial Intelligence Tools · 1992
There are a large number of highly complex, apparently disparate, Artificial Intelligence (AI) algorithms for planning and learning. These entail the (often tedious) construction of specialized data and control structures. In this article, we present Call-Graph Caching (CGC) as an organizing principle for many of these methods. CGC is the preservation of the trace of a computational process for subsequent reuse; CGC allows the operation of highly efficient, but unintuitive, AI algorithms to be recast as far simpler recursive processes. Thus, we shall describe simple recursive constructions that, together with CGC, provide new motivations and derivations of certain classical AI planning and learning algorithms.