Two Algorithms for Decision Tree Search

In this paper two algorithms for decision tree search are presented. The basic idea behind these algorithms is to make use of domain dependent information, in the form of an evaluation function as that used by AO*, along with a search mechanism similar to the alpha-beta technique for minimax trees. One of the advantages of our algorithms over AO* is that our algorithms need only linear space. The solution computed by the first algorithm can be either optimal or sub-optimal, depending on the admissibility of the evaluation function. The second algorithm is an approximate algorithm which can case a tradeoff between computational efficiency and solution quality. Some results are presented on the correctness of the algorithms and on the quality of the solutions computed by the algorithms.