An admissible heuristic search algorithm

Abstract

This paper introduces an admissible heuristic search algorithm — Search and Learning Algorithm (SLA^*). SLA^* is developed from the work presented by Korf in the Learning-Real-Time-Algorithm (LRTA^*). We retain the major elements of Korfs work in LRTA^*, and improve its performance by incorporating a review component to fully reflect the effect the learning of new heuristic from front states has upon the previous states. The combined strategy of search, learning, and review has enabled this algorithm to accumulate knowledge continuously through guided expansion, and to identify better search directions in any stage of nodes expansion. With the assumption of non-overestimating initial estimates for all nodes to the goal, this algorithm is able to find an optimal solution in a single problem solving trial with good efficiency. We provide a proof for the optimality of the solution.