A New Efficient In Situ Sampling Model for Heuristic Selection in Optimal Search

Techniques exist that enable problem-solvers to automatically generate an almost unlimited number of heuristics for any given problem. Since they are generated for a specific problem, the cost of selecting a heuristic must be included in the cost of solving the problem. This involves a tradeoff between the cost of selecting the heuristic and the benefits of using that specific heuristic over using a default heuristic. The question we investigate in this paper is how many heuristics can we handle when selecting from a large number of heuristics and still have the benefits outweigh the costs. The techniques we present in this paper allow our system to handle several million candidate heuristics.