Search: a Generalization of Ao

Problem reduction is a basic concept behind much of AI search. The standard approach is to view problem reduction in terms of AND/OR graphs and then to concentrate on graph search techniques to nd a solution to the original problem. However these techniques depend upon global graph properties. By taking a more object-oriented approach we can ignore the graphs as a whole and instead need only deal with the local properties of individual problem reduction steps. This paper presents the GAO algorithm, which is a generalization of the AO algorithm based on this shift in viewpoint. As well as encompassing the behaviours of existing algorithms (such as A , AO , MINIMAX , , SSS , and SCOUT ), GAO goes further. It allows the seamless integration of di erent problem domains, di erent search strategies, and even non-search problem-solving techniques, within the same algorithm.