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» On using multi-agent systems in playing board games
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AAI
2010
195views more  AAI 2010»
14 years 6 months ago
Automatic Extraction of Go Game Positions from Images: a Multi-Strategical Approach to Constrained Multi-Object Recognition
Here, we present a constrained object recognition task that has been robustly solved largely with simple machine learning methods, using a small corpus of about 100 images taken u...
Alexander K. Seewald
JCP
2007
118views more  JCP 2007»
14 years 9 months ago
Tutoring an Entire Game with Dynamic Strategy Graphs: The Mixed-Initiative Sudoku Tutor
Abstract— In this paper, we develop a mixed-initiative intelligent tutor for the game of Sudoku called MITS. We begin by developing a characterization of the strategies used in S...
Allan Caine, Robin Cohen
AIIDE
2009
14 years 10 months ago
Improving Offensive Performance Through Opponent Modeling
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...
ICRA
2007
IEEE
128views Robotics» more  ICRA 2007»
15 years 3 months ago
Adaptive Play Q-Learning with Initial Heuristic Approximation
Abstract— The problem of an effective coordination of multiple autonomous robots is one of the most important tasks of the modern robotics. In turn, it is well known that the lea...
Andriy Burkov, Brahim Chaib-draa
96
Voted
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 2 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein