Application of the Monte-Carlo Tree Search to Multi-Action Turn-Based Games with Hidden Information

Traditional search algorithms struggle when applied to complex multi-action turn-based games. The introduction of hidden information further increases domain complexity. The Monte-Carlo Tree Search (MCTS) algorithm has previously been applied to multi-action turn-based games, but not multi-action turn-based games with hidden information. This thesis compares several Monte Carlo Tree Search (MCTS) extensions (Determinized/Perfect Information Monte Carlo, Multi-Observer Information Set MCTS, and Belief State MCTS) in TUBSTAP, an open-source multi-action turn-based game, modified to include hidden information via fog-of-war

Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

We present the design of a competitive artificial intelligence for Scopone, a popular Italian card game. We compare rule-based players using the most established strategies (one for beginners and two for advanced players) against players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS) with different reward functions and simulation strategies. MCTS requires complete information about the game state and thus implements a cheating player while ISMCTS can deal with incomplete information and thus implements a fair player. Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game

ALTERNATIVE SELECTION FUNCTIONS FOR INFORMATION SET MONTE CARLO TREE SEARCH

We evaluate the performance of various selection methods for the Monte Carlo Tree Search algorithm in two-player zero-sum extensive-form games with imperfect information. We compare the standard Upper Confident Bounds applied to Trees (UCT) along with the less common Exponential Weights for Exploration and Exploitation (Exp3) and novel Regret matching (RM) selection in two distinct imperfect information games: Imperfect Information Goofspiel and Phantom Tic-Tac-Toe. We show that UCT after initial fast convergence towards a Nash equilibrium computes increasingly worse strategies after some point in time. This is not the case with Exp3 and RM, which also show superior performance in head-to-head matches

Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games

ABSTRACT Online search in games has been a core interest of artificial intelligence. Search in imperfect information games (e.g., Poker, Bridge, Skat) is particularly challenging due to the complexities introduced by hidden information. In this paper, we present Online Outcome Sampling, an online search variant of Monte Carlo Counterfactual Regret Minimization, which preserves its convergence to Nash equilibrium. We show that OOS can overcome the problem of non-locality encountered by previous search algorithms and perform well against its worst-case opponents. We show that exploitability of the strategies played by OOS decreases as the amount of search time increases, and that preexisting Information Set Monte Carlo tree search (ISMCTS) can get more exploitable over time. In head-to-head play, OOS outperforms ISMCTS in games where non-locality plays a significant role, given a sufficient computation time per move

Finding Competitive Network Architectures Within a Day Using UCT

The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies. We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform human architectures and our competitors which consider the same types of layers