General Game Playing (GGP) is a challenge in artificial intelligence where researchers aim to create an autonomous game-playing agent that can play any game without human intervention. One approach to achieve this goal is through Monte Carlo Tree Search (MCTS) methods, which have been widely studied and used in various games. In this article, we will provide a survey of MCTS techniques specifically designed for GGP.
MCTS: The Basics
MCTS is a heuristic search algorithm that combines the ideas of Monte Carlo simulations and tree search methods to find the best move or policy in a game. The basic steps of MCTS are as follows:
- Sample a set of possible moves from the current state of the game.
- Play out each of these moves recursively, simulating the outcome of the game.
- Evaluate the outcome of each simulation and assign a score to each move based on its outcome.
- Select the best move based on the scores calculated in step 3.
- Repeat steps 1-4 until a termination condition is met or a satisfactory solution is found.
MCTS Techniques for GGP
Several MCTS techniques have been developed and applied to GGP, aiming to improve the efficiency and effectiveness of the search process. Some of these techniques include:
- Upper Confidence Bound (UCT): This technique selects the move with the highest estimated value in the current state, based on a combination of the immediate reward and the uncertainty of the estimate (Browne et al., 2012). UCT balances exploration and exploitation by considering both the potential reward of a move and its uncertainty.
- Increasing Difficulty: This technique adapts the difficulty of the game state according to the progress of the search, increasing the difficulty for harder states to encourage more exploration (Cohen-Solal & Cazenave, 2023). Increasing difficulty helps to maintain an optimal balance between exploration and exploitation.
- Epsilon-Max: This technique introduces a probability distribution over the child nodes of a tree, allowing for some randomness in the search process (Kowalski et al., 2019). By introducing randomness, epsilon-max can explore more parts of the game state space, leading to a more comprehensive understanding of the game.
- Split Moves: This technique divides moves into smaller sub-moves and evaluates each sub-move independently, allowing for faster and more efficient search (Kowalski et al., 2022). By breaking down moves into smaller parts, split moves can explore different aspects of the game state simultaneously.
Conclusion
In conclusion, MCTS techniques have shown great promise in solving GGP problems. By providing a flexible and adaptive approach to search, these techniques enable AI agents to efficiently explore complex game states and find optimal policies. As the field of GGP continues to evolve, it is likely that new MCTS techniques will arise, further improving the performance and efficiency of game-playing agents.
