OBDDs in Heuristic Search

The use of a lower bound estimate in the search has a tremendous impact on the size of the resulting search trees, whereas OBDDs can be used to e ciently describe sets of states based on their binary encoding. This paper combines these two ideas to a new algorithm BDDA . It challenges both, the breadthrst search using OBDDs and the traditional A algorithm. The problem with A is that in many applications areas the set of states is too huge to be kept in main memory. In contrary, brute-force breadthrst search using OBDDs unnecessarily expands several nodes. Therefore, we exhibit a new trade-o between time and space requirements and tackle the most important problem in heuristic search to overcome space limitations while avoiding a strong penalty in time. We evaluate our approach in the (n 1)-Puzzle and within Sokoban.