4,790 research outputs found
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments
International audienceTHE AUTHORS ARE EXTREMELY GRATEFUL TO GRID5000 for helping in designing and experimenting around Monte-Carlo Tree Search. In order to promote computer Go and stimulate further development and research in the field, the event activities, "Computational Intelligence Forum" and "World 99 Computer Go Championship," were held in Taiwan. This study focuses on the invited games played in the tournament, "Taiwanese Go players versus the computer program MoGo," held at National University of Tainan (NUTN). Several Taiwanese Go players, including one 9-Dan professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines All Moves As First (AMAF)/Rapid Action Value Estimation (RAVE) values, online "UCT-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan with, (1) good skills for fights, (2) weaknesses in corners, in particular for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in artificial intelligence and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future
Machine Learning for k-in-a-row Type Games Using Random Forest and Genetic Algorithm
Antud töö põhieesmärgiks oli uurida kui efektiivne ja mõistlik on kombineerida mitu
erinevat masinõppe meetodit, et treenida tehisintellekti k-ritta tüüpi mängudele. Need
meetodid on järgnevad: geneetiline algoritm, juhumetsad (koos otsustuspuudega) ning
Minimax algoritm. Eriliseks teeb sellise meetodi asjaolu, et kogu intelligents treenitakse ilma
inimese ekspert teadmisteta ning kõik vajaliku informatsiooni peab arvuti ise endale
omandama.The main objective of the thesis is to explore the viability of combination multiple
machine learning techniques in order to train Artificial Intelligence for k-in-a-row type games.
The techniques under observation are following:
- Random Forest
- Minimax Algorithm
- Genetic Algorithm
The main engine for training AI is Genetic Algorithm where a set of individuals are evolved
towards better playing computer intelligence. In the evaluation step, series of games are done
where individuals compete in series of games against each other – the results are recorded and
the evaluation score of the individuals are based on their performance in the games. During a
game, heuristic game tree search algorithm Minimax is used as player move advisor. Each of
the competing individuals has a Random Forest attached that is used as the heuristic function
in Minimax. The key idea of the training is to evolve as good Random Forests as possible. This
is achieved without any help of human expertise by using solely evolutionary training
SAI, a Sensible Artificial Intelligence that plays Go
We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero
paradigm. The winrate as a function of the komi is modeled with a
two-parameters sigmoid function, so that the neural network must predict just
one more variable to assess the winrate for all komi values. A second novel
feature is that training is based on self-play games that occasionally branch
-- with changed komi -- when the position is uneven. With this setting,
reinforcement learning is showed to work on 7x7 Go, obtaining very strong
playing agents. As a useful byproduct, the sigmoid parameters given by the
network allow to estimate the score difference on the board, and to evaluate
how much the game is decided.Comment: Updated for IJCNN 2019 conferenc
On Monte-Carlo tree search for deterministic games with alternate moves and complete information
We consider a deterministic game with alternate moves and complete
information, of which the issue is always the victory of one of the two
opponents. We assume that this game is the realization of a random model
enjoying some independence properties. We consider algorithms in the spirit of
Monte-Carlo Tree Search, to estimate at best the minimax value of a given
position: it consists in simulating, successively, well-chosen matches,
starting from this position. We build an algorithm, which is optimal, step by
step, in some sense: once the first matches are simulated, the algorithm
decides from the statistics furnished by the first matches (and the a
priori we have on the game) how to simulate the -th match in such a way
that the increase of information concerning the minimax value of the position
under study is maximal. This algorithm is remarkably quick. We prove that our
step by step optimal algorithm is not globally optimal and that it always
converges in a finite number of steps, even if the a priori we have on the game
is completely irrelevant. We finally test our algorithm, against MCTS, on
Pearl's game and, with a very simple and universal a priori, on the games
Connect Four and some variants. The numerical results are rather disappointing.
We however exhibit some situations in which our algorithm seems efficient
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
Random Search Algorithms
In this project we designed and developed improvements for the random search algorithm UCT with a focus on improving performance with directed acyclic graphs and groupings. We then performed experiments in order to quantify performance gains with both artificial game trees and computer Go. Finally, we analyzed the outcome of the experiments and presented our findings. Overall, this project represents original work in the area of random search algorithms on directed acyclic graphs and provides several opportunities for further research
Quoridor agent using Monte Carlo Tree Search
This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation
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