An Instance-Based Learning Approach for Available-Memory Non-minimal Cost-Bounded Search

Iterative-deepening cost-bounded tree search algorithms that either require memory on the order of the search tree depth (e. g., IDA*, DFS*, etc.) or that use the entire available memory (e. g., MREC) have been proposed as viable means to solve discrete optimization problems (DOP) on which best-first branch-and-bound (BFBB) may potentially run out of memory. But, such limited-memory cost-bounded search algorithms can bear a huge overhead relative to BFBB due to re-expanding nodes with cost less than C* (essential nodes), equal to C* (boundary nodes) and/or exceeding C* (non-essential nodes) where C* denotes the cost of an optimal solution. Our contributions in this paper are two fold. First, we propose a novel available-memory non-minimal cost-bounded search method (AM-NCBS KBEST) that tries to minimize the search overhead on a single problem instance by striking a trade-off between essential node re-expansion and non-essential node expansion depending upon the value of a cost bound choice parameter ´ K´. We show that this new strategy reduces the search overhead of the previous best available-memory costbounded search strategy by at least half even for the most conservative values of ´ K´. Next, we present a learning-based approach (AM-NCBS KLEARN) to intelligently predict nonminimal cost-bounds for solving a new problem instance by analyzing various cost bound choices that when used over the course of the search, on a number of previously solved instances, result in the least cumulative overhead on an average. We demonstrate on a set of 100 problem instances that the learning based approach further reduces AM-NCBS KBEST´s overhead that may ensue due to bad choices of ´K´.