Implementation of Adversarial Search and Decision Tree in PopOut Game AI: Practice of MCTS and ID3 Algorithms

This article introduces a PopOut game AI project based on adversarial search strategies, which implements the Monte Carlo Tree Search (MCTS) and ID3 decision tree algorithms to solve intelligent decision-making problems and demonstrate the application of classic AI algorithms in the game domain. The project covers content such as PopOut game rule analysis, principles and implementation details of the two algorithms, performance comparison, and exploration of hybrid strategies.

Artificial intelligence is widely applied in the game domain, and adversarial search is the core technology for two-player zero-sum games. PopOut is a Connect Four variant that allows players to pop out their own pieces from the bottom; this rule change increases the strategic depth. Its state space is more complex than traditional Connect Four (with cyclic states), so simple exhaustive search is not feasible, requiring heuristic evaluation and sampling techniques.

MCTS combines tree search with random sampling and is suitable for complex state spaces. Its four core stages are: Selection (UCT algorithm balances exploration and exploitation), Expansion, Simulation (random + heuristic strategy), and Backpropagation. The UCT formula is UCT(v) = Q(v)/N(v) + C*sqrt(ln(N(parent))/N(v)). Project optimizations include action representation (column index + operation type), fast simulation, parallelization, and time control.

ID3 is a top-down greedy decision tree algorithm that uses information gain to select features. The project implements it from scratch: recursive tree construction (handling leaf nodes, feature selection), continuous value processing, and pre-pruning to prevent overfitting. Feature engineering includes basic statistics, threat analysis, position features, and PopOut-specific features.

Comparison between MCTS and ID3: MCTS is a sampling-based search that does not require training but has high computational cost; ID3 is pattern matching that requires training data but has strong interpretability. Hybrid strategies include: using MCTS to generate data for training decision trees, using decision trees to guide MCTS simulation, and hierarchical decision-making (using decision trees for opening moves, MCTS for mid-game and end-game).

Battle tests show: MCTS's win rate increases with the number of simulations (10,000 simulations can beat amateur players); decision trees alone perform worse than MCTS but are faster; hybrid strategies balance efficiency and performance. Parameter tuning: UCT constant C=1.2-1.6, MCTS iterations 5000-10000 times, decision tree depth 8-12 layers, and threat features are the most critical.

Learning value of the project: Implementing algorithms from scratch deepens understanding, modular architecture design, debugging and performance optimization skills. It can be extended to other board games such as Tic-Tac-Toe, Gomoku, Othello, and Go, with strong transferability.

The project comprehensively demonstrates the application of adversarial search and decision trees in game AI. MCTS verifies the effectiveness of sampling-based search, and ID3 shows interpretable strategies through data learning. It is recommended that developers consolidate theoretical foundations, practice projects, participate in open source, and follow cutting-edge research. The game AI field continues to develop, and more innovations are expected.