Abstract— We present an integrated vision and robotic system that plays, and learns to play, simple physically-instantiated board games that are variants of TIC TAC TOE and HEXAP...
Andrei Barbu, Siddharth Narayanaswamy, Jeffrey Mar...
Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In thi...
Kennard Laviers, Gita Sukthankar, David W. Aha, Ma...
In recent years, science education has been the focus of study and development of new gamebased learning environments. It has been argued that active and critical learning about r...
In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a loc...
In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. ...