COllective INtelligence with sequences of actions coordinating actions in multi-agent systems

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2837, Issue , 2003, Pages 181-192

  'T Hoen, Pieter Jan ^a   Bohte, Sander M ^a  

^a CWI   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTIONS; HEURISTIC METHODS; LEARNING SYSTEMS; MULTI AGENT SYSTEMS; PARAMETER ESTIMATION; PROBLEM SOLVING; ARTIFICIAL INTELLIGENCE; INTELLIGENT AGENTS; REINFORCEMENT LEARNING;

COLLECTIVE INTELLIGENCE (COIN) FRAMEWORK; JOINT SEQUENCES; OPTIMAL FITNESS; REINFORCEMENT LEARNING (RL);

ARTIFICIAL INTELLIGENCE; MULTI AGENT SYSTEMS;

COLLECTIVE INTELLIGENCES; ENGINEERING SOLUTIONS; IMPROVE PERFORMANCE; MODELING-BASED LEARNING; NON-TRIVIAL; SUBOPTIMAL SOLUTION;

EID: 9444227681     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. A. Barto and S. Mahadevan. Recent advances in hierarchical reinforcement learning. Discrete-Event Systems journal, 2003. to appear.
2. C. Guestrin, M. Lagoudakis, and R. Parr. Coordinated reinforcement learning. In Proceedings of the ICML-2002 The Nineteenth International Conference on Machine Learning, 2002.
3. G. Hardin. The tragedy of the commons. Science, 162:1243-1248, 1968.
4. M. Lauer and M. Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In Proc. 17th International Conf. on Machine Learning, pages 535-542. Morgan Kaufmann, San Francisco, CA, 2000.
5. T. Mitchell. Machine Learning. McGraw-Hill, 1997.
6. Personal communication with A. Agogino.
7. shai Menache, S. Mannor, and N. Shimkin. Q-cut - dynamic discovery of sub-goals in Reinforcement Learning. In T. Elomaa, H. Mannila, and H. Toivonen, editors, Machine Learning: ECML 2002, 13th European Conference on Machine Learning, volume 2430 of LectureNotes in Computer Science, pages 295-306. Springer, 2002.
8. R. Sutton and A. Barto. Reinforcement learning: An introduction. MIT-press, Cambridge, MA, 1998.
9. S. B. Thrun. Efficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Carnegie Mellon University, Pittsburgh, Pennsylvania, 1992.
10. K. Turner, A. Agogino, and D. Wolpert. Learning sequences of actions in collectives of autonomous agents. In Autonomous Agents & Multiagent Systems, pages 378-385, part 1. ACM press, 2002.
11. K. Turner and D. Wolpert. Collective INtelligence and Braess' paradox. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 104-109, Austin, Aug. 2000.
12. Watkins and Dayan. Q-learning. Machine Learning, 8:279-292, 1992.
13. G. Weiss. A multiagent framework for planning, reacting, and learning. Technical Report FKI-233-99, Institut für Informatik, Technische Universität München, 1999.
14. M. P. Wellman. The economic approach to artificial intelligence. ACM Computing Surveys, 28(4es):14-15, 1996.
15. M. P. Wellman. Market-oriented programming: Some early lessons. In S. Clearwater, editor, Market-Based Control: A Paradigm for Distributed Resource Allocation. World Scientific, River Edge, New Jersey, 1996.
16. M. Wiering. Explorations in Efficient Reinforcement Learning. PhD thesis, University of Amsterdam, 1999.
17. D. Wolpert and K. Tumer. An introduction to COllective INtelligence. Technical Report NASA-ARC-IC-99-63, NASA Ames Research Center, 1999. A shorter version of this paper is to appear in: Jeffrey M. Bradshaw, editor, Handbook of Agent Technology, AAAI Press/MIT Press, 1999.
18. D. Wolpert and K. Tumer. An introduction to COllective INtelligence. Technical Report NASA-ARC-IC-99-63, NASA Ames Research Center, 1999. A shorter version of this paper is to appear in: Jeffrey M. Bradshaw, editor, Handbook of Agent Technology, AAAI Press/MIT Press, 1999.
19. D. Wolpert and K. Turner. Optimal payoff functions for members of collectives. Advances in Complex Systems, 2001. in press.
20. D. H. Wolpert, K. Turner, and J. Frank. Using collective intelligence to route internet traffic. In Advances in Neural Information Processing Systems-11, pages 952-958, Denver, Dec. 1998.
21. D. H. Wolpert, K. Turner, and J. Frank. Using collective intelligence to route internet traffic. In Advances in Neural Information Processing Systems-11, pages 952-958, Denver, 1998.
22. D. H. Wolpert, K. R. Wheeler, and K. Tumer. General principles of learning-based multi-agent systems. In O. Etzioni, J. P. Müller, and J. M. Bradshaw, editors, Proceedings of the Third Annual Conference on Autonomous Agents (AGENTS-99), pages 77-83, New York, May 1-5 1999. ACM Press.