Bridging Theory and Practice: Developing An Interactive Web Interface for MCTS Algorithm Exploration Implementing OCBA Selection Operator in Othello

Abstract

Monte Carlo Tree Search (MCTS) algorithms have become integral to artificial intelligence in complex and sequential decision-making scenarios such as Go and Othello by applying multi-armed bandit selection strategies for tree exploration. MCTS selection operators are essential to determining tree expansion in large decision and state spaces. Traditional implementations utilize Upper Confidence bounds applied to Trees (UCT) as the node selection operator, while recent advances suggest Optimal Computing Budget Allocation (OCBA) selection operators provide superior performance by dynamically allocating computational resources based on statistical variance estimates, balancing exploration alongside exploitation over UCT's tendency toward the latter. Despite theoretical advances in MCTS selection strategies, accessible platforms for experiencing these AI systems remain limited. While comparative studies between UCT and OCBA exist in research literature, user-friendly interfaces for MCTS-based Othello gameplay are scarce. This project addresses this gap by developing a comprehensive web-based interface for an Othello AI system implementing MCTS with OCBA selection operators. Built using HTML, CSS, and JavaScript, the responsive interface allows human players to compete against AI with fully configurable parameters. Users can adjust rollout counts (250-5000), select simulation strategies (random/semi-random), choose opponent types, and determine move ordering preferences. The system features real-time game state visualization, score tracking, and intuitive parameter configuration. This work provides a valuable educational and research tool bridging the gap between theoretical AI advances and practical user interaction with sophisticated game-playing algorithms.