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Publications Related to: Learning in Games

By Shaul Markovitch

  1. Asaf Amit and Shaul Markovitch. Learning to Bid in Bridge. Machine Learning, 63:287-327 2006.[abstract][pdf]
  2. Shaul Markovitch and Ronit Reger. Learning and Exploiting Relative Weaknesses of Opponent Agents. Autonomous Agents and Multi-agent Systems, 10:103-130 2005.[abstract][pdf]
  3. David Carmel and Shaul Markovitch. Model-based Learning of Interaction Strategies in Multi-Agent Systems. Journal of Experimental and Theoretical Artificial Intelligence, 10:309-332 1998.[abstract][pdf]
  4. Lev Finkelstein and Shaul Markovitch. Learning to Play Chess Selectively by Acquiring Move Patterns. ICCA Journal, 21:100-119 1998.[abstract][pdf]
  5. David Carmel and Shaul Markovitch. How to explore your opponent's strategy (almost) optimally. In Proceedings of the Third International Conference on Multi-Agent Systems, 64-71 Paris, France, 1998.[abstract][pdf]
  6. David Carmel and Shaul Markovitch. Exploration and Adaptation in Multiagent Systems: A Model-Based Approach. In Proceedings of The Fifteenth International Joint Conference for Artificial Intelligence, 606-611 Nagoya, Japan, 1997.[abstract][pdf]
  7. David Carmel and Shaul Markovitch. Learning and Using Opponent Models in Adversary Search. Technical Report CIS9609, Technion, 1996.[abstract][pdf]
  8. David Carmel and Shaul Markovitch. Opponent Modeling in Multi-agent Systems. In Gerhard Weiss and Sandip Sen, Editors, Adaption And Learning In Multi-Agent Systems. Springer-Verlag, 1996.[abstract][pdf]
  9. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence, 12:88-105 1996.[abstract][pdf]
  10. David Carmel and Shaul Markovitch. The M* Algorithm: Incorporating Opponent Models Into Adversary Search. Technical Report CIS9402, Computer Science Department, Technion, 1994.[abstract][pdf]
  11. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for game Playing. In Proceedings of The Thirteenth International Joint Conference for Artificial Intelligence, 974-979 Chambery, France, 1993.[abstract][pdf]
  12. David Carmel and Shaul Markovitch. Learning Models of the Opponent's Strategy in Game Playing. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, 140-147 North Carolina, 1993.[abstract][pdf]
  13. David Lorenz and Shaul Markovitch. Derivative Evaluation Function Learning Using Genetic Operators. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, 106-114 New Carolina, 1993.[abstract][pdf]
  14. Reuven Hasson, Shaul Markovitch and Yaron Sella. Using Filters to Improve Efficiency of Game-playing Learning Procedures. In Proceedings of Eleventh International Conference of the Chilean Computer Science Society, 125-137 Santiago, Chile, 1991.[abstract][pdf]