An algorithm based on valuation forecasting for game tree search

Abstract

This study proposes a novel best-first search algorithm, called Valuation Increment Search Algorithm (VIS), which applies the increment of exploratory value change to predict the valuation and guide the selection of the game tree. The proposed method can effectively use node valuation and game tree size information to solve the game tree by fewer exploration steps. The relevant assumptions and search principles of the proposed algorithm are detailed. Moreover, comparative experiments with Alpha–Beta Search (α–β search), Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) in the domain of Dou Dizhu has been performed to verify the performance. Result demonstrate that VIS is superior to α–β search, PNS and MCTS algorithm in solving the game tree and finding the best move for some game scenarios by consuming less time and few memory resources. The improvement effect of the average Increment, magnitude of Increment, and the number of visits to node on the original VIS was validated. Also, a new selection strategy is defined for the improved VIS algorithm combined with these factors. The experimental comparison results show that improved VIS has a better performance in solving game tree with less nodes generated and expanded than original VIS.