Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper introduces a board game, Tetris Link, that is yet unexplored and appears to be highly challenging. Tetris Link has a large branching factor and lines of play that can be very deceptive, that search has a hard time uncovering. Finding good moves is very difficult for a computer player, our experiments show. We explore heuristic planning and two other approaches: Reinforcement Learning and Monte Carlo tree search. Curiously, a naive heuristic approach that is fueled by expert knowledge is still stronger than the planning and learning approaches. We, therefore, presume that Tetris Link is more difficult than expected. We offer ...
Using neural networks to play Tetris is a classical implementation. Tetris is a game where there are...
In this thesis, we study how reinforcement learning algorithms can tackle classical board games with...
Computer Go programs have surpassed top-level human players by using deep learning and reinforcement...
In this paper, we propose to use evolution- nary algorithms more specifically the covariance matrix ...
Tetris is a hard game to learn due to its random environment, large state space, and need for a long...
International audienceThis article has two purposes: a review on the problem of building a controlle...
Tetris is one of the most famous tile-matching videogames, and has been used as a test bed for artif...
In Artificial Intelligence (AI), there exist formalised approaches and algorithms for general proble...
https://scholarworks.moreheadstate.edu/student_scholarship_posters/1214/thumbnail.jp
Research in Artificial Intelligence has shown that machines can be programmed to perform as well as,...
Blokus (officially pronounced as “Block us”) is an abstract strategy board game with transparent Tet...
Researchers in the field of computer games interest in creating not only strong game-playing program...
AbstractThis work is motivated by one of the important characteristics of an intelligent system: the...
Summarization: Game playing has always been considered an intellectual activity requiring a good lev...
Despite the recent successful application of Artificial Intelligence (AI) to games, the performance ...
Using neural networks to play Tetris is a classical implementation. Tetris is a game where there are...
In this thesis, we study how reinforcement learning algorithms can tackle classical board games with...
Computer Go programs have surpassed top-level human players by using deep learning and reinforcement...
In this paper, we propose to use evolution- nary algorithms more specifically the covariance matrix ...
Tetris is a hard game to learn due to its random environment, large state space, and need for a long...
International audienceThis article has two purposes: a review on the problem of building a controlle...
Tetris is one of the most famous tile-matching videogames, and has been used as a test bed for artif...
In Artificial Intelligence (AI), there exist formalised approaches and algorithms for general proble...
https://scholarworks.moreheadstate.edu/student_scholarship_posters/1214/thumbnail.jp
Research in Artificial Intelligence has shown that machines can be programmed to perform as well as,...
Blokus (officially pronounced as “Block us”) is an abstract strategy board game with transparent Tet...
Researchers in the field of computer games interest in creating not only strong game-playing program...
AbstractThis work is motivated by one of the important characteristics of an intelligent system: the...
Summarization: Game playing has always been considered an intellectual activity requiring a good lev...
Despite the recent successful application of Artificial Intelligence (AI) to games, the performance ...
Using neural networks to play Tetris is a classical implementation. Tetris is a game where there are...
In this thesis, we study how reinforcement learning algorithms can tackle classical board games with...
Computer Go programs have surpassed top-level human players by using deep learning and reinforcement...