A Comparative Study of IDA*-Style Searches

In this paper, we study the performance of various IDA*-style searches and investigate methods to improve their performance by predicting in each stage the threshold to use for pruning. We rst present three models to approximate the distribution of number of search nodes by lower bounds: exponential, geometric, and linear, and illustrate these distributions based on some well-known combinatorial search problems. We then show the performance of an ideal IDA* algorithm and identify reasons why existing IDA*-style algorithms perform well. In practice, we will be able to know from previous experience the distribution for a given problem instance but will not be able to determine the parameters of the distribution. Hence, we develop RIDA*, a method that estimates dynamically the parameters of the distribution, and predicts the best threshold to use. Finally, we compare the performance of several IDA*-style algorithms | Korf's IDA*, RI-DA*, IDA* CR and DFS* | on several application problems, and identify conditions under which each of these algorithms will perform well.