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Publications Related to: Multi-Agent Systems

By Shaul Markovitch

  1. Asaf Amit and Shaul Markovitch. Learning to Bid in Bridge. Machine Learning, 63:287-327, 2006. [pdf][abstract]
  2. Shaul Markovitch and Ronit Reger. Learning and Exploiting Relative Weaknesses of Opponent Agents. Autonomous Agents and Multi-agent Systems, 10:103-130, 2005. [pdf][abstract]
  3. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. Journal of Artificial Intelligence Research, 19:73-138, 2003. [pdf][abstract]
  4. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 719-724, Edmonton, Alberta, Canada, 2002. [pdf][abstract]
  5. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms. In Proceedings of The AAAI Fall Symposium on Using Uncertainty within Computation, pages 49-56, North Carolina, 2001. [pdf][abstract]
  6. 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. [pdf][abstract]
  7. David Carmel and Shaul Markovitch. Pruning Algorithms for Multi-Model Adversary Search. Artificial Intelligence, 99:325-355, 1998. [pdf][abstract]
  8. 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, pages 606-611, Nagoya, Japan, 1997. [pdf][abstract]
  9. 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, volume 1042 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1996. [pdf][abstract]
  10. David Carmel and Shaul Markovitch. Learning and Using Opponent Models in Adversary Search. Technical Report CIS9609, Technion, 1996. [pdf][abstract]
  11. David Carmel and Shaul Markovitch. Learning Models of Intelligent Agents. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 62-67, Portland, Oregon, 1996. [pdf][abstract]
  12. David Carmel and Shaul Markovitch. Incorporating Opponent Models into Adversary Search. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 120-125, Portland, Oregon, 1996. [pdf][abstract]
  13. David Carmel and Shaul Markovitch. The M* Algorithm: Incorporating Opponent Models Into Adversary Search. Technical Report CIS9402, Computer Science Department, Technion, 1994. [pdf][abstract]
  14. 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, pages 140-147, North Carolina, 1993. [pdf][abstract]