A New Action-Based Reasoning Approach for Playing Chess

Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans. The Case-Based Reasoning (CBR) strategy tries to simulate human thinking regarding solving problems based on constructed knowledge. This paper suggests a new Action-Based Reasoning (ABR) strategy for a chess engine. This strategy mimics human experts’ approaches when playing chess, with the help of the CBR phases. This proposed engine consists of the following processes. Firstly, an action library compiled by parsing many grandmasters’ cases with their actions from different games is built. Secondly, this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes. Thirdly, the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search. The proposed evaluation function estimates the retrievably reactive moves. Finally, the best move will be selected, played on the board, and stored in the action library for future use. Many experiments were conducted to evaluate the performance of the proposed engine. Moreover, the engine played 200 games against Rybka 2.3.2a scoring 2500, 2300, 2100, and 1900 rating points. Moreover, they used the Bayeselo tool to estimate these rating points of the engine. The results illustrated that the proposed approach achieved high rating points, reaching as high as 2483 points.