COMPARISON AND EVALUATION OF A CLASS OF IDA* ALGORITHMS
Benjamin Wan-Sang Wah, Yi Shang · International Journal of Artificial Intelligence Tools · 1994
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 be used for pruning. Without loss of generality, we consider minimization problems in this paper. We first present three models to approximate the distribution of the number of search nodes by lower bounds: exponential, geometric, and linear, and illustrate these distributions based on some well-known combinatorial search problems. Based on these distributions, we 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 experience the type of distribution for a given problem instance, but will not be able to know the parameters of this distribution until the instance is solved. Hence, we develop RIDA*, a method that estimates dynamically the parameters of the distribution, and predicts the best threshold to be used in each stage. Finally, we compare the performance of several IDA*-style algorithms—Korf’s IDA* and RBFS, RIDA*, IDA*_CR and DFS*—on several application problems, and identify conditions under which each of these algorithms will perform well.