Cooperative multi-agent learning: The state of the art

Autonomous Agents and Multi-Agent Systems

Volumn 11, Issue 3, 2005, Pages 387-434

  Panait, Liviu ^a   Luke, Sean ^a  

^a George Mason University   (United States)

Author keywords

Cooperation; Machine learning; Multi agent learning; Multi agent systems; Survey

Indexed keywords

EID: 26444601262     PISSN: 13872532     EISSN: 15737454     Source Type: Journal    
DOI: 10.1007/s10458-005-2631-2     Document Type: Article

References (314)

1. D. H. Ackley and M. Littman, "Altruism in the evolution of communication," in Artificial Life IV: Proceedings of the International Workshop on the Synthesis and Simulation of Living Systems, (3rd edn.), MIT Press, 1994.
2. D. Andre, F. Bennett III, and J. Koza, "Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem," in Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, 1996.
3. D. Andre and A. Teller, "Evolving team Darwin United," in M. Asada and H. Kitano, (eds.), RoboCup-98: Robot Soccer World Cup H. Springer Verlag, 1999.
4. P. Angeline and J. Pollack, "Competitive environments evolve better solutions for complex tasks," in S. Forrest, (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA), Morgan Kaufmann: San Mateo, CA, pp. 264-270, 1993.
5. W. Arthur, "Inductive reasoning and bounded rationality," Complex. Econ. Theory, vol. 84, no. 2, pp. 406-411, 1994.
6. T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolutionary Straegies, Evolutionary Programming, and Genetic Algorithms, Oxford Press, 1996.
7. T. Balch, Learning roles: Behavioral diversity in robot teams, Technical Report GIT-CC-97-12, Georgia Institute of Technology, 1997.
8. T. Balch, Behavioral Diversity in Learning Robot Teams, PhD thesis, College of Computing, Georgia Institute of Technology, 1998.
9. T. Balch, "Reward and diversity in multirobot foraging," in IJCAI-99 Workshop on Agents Learning About, From and With other Agents, 1999.
10. B. Banerjee, R. Mukherjee, and S. Sen. "Learning mutual trust," in Working Notes of AGENTS-00 Workshop on Deception, Fraud and Trust in Agent Societies, pp. 9-14, 2000.
11. A. Barto, R. Sutton, and C. Watkins, "Learning and sequential decision making," in M. Gabriel and J. Moore, (eds.), Learning and Computational Neuroscience: Foundations of Adaptive Networks, MIT Press: Cambridge, MA, 1990.
12. J. Bassett and K. De Jong, "Evolving behaviors for cooperating agents," in Z. Ras, (ed.), Proceedings from the Twelfth International Symposium on Methodologies for Intelligent Systems, Springer-Verlag: Charlotte, NC, pp. 157-165, 2000.
13. J. K. Bassett, A study of generalization techniques in evolutionary rule learning, Master's thesis, George Mason University, Fairfax VA, USA, 2002.
14. R. Beckers, O. E. Holland, and J.-L. Deneubourg. "From local actions to global tasks: Stigmergy and collective robotics," in Artificial Life IV: Proceedings of the International Workshop on the Synthesis and Simulation of Living Systems, (3rd edn.), MIT Press, 1994.
15. M. Benda, V. Jagannathan, and R. Dodhiawala, On optimal cooperation of knowledge sources - an empirical investigation, Technical Report BCS-G2010-28, Boeing Advanced Technology Center, Boeing Computer Services, 1986.
16. H. Berenji and D. Vengerov, "Advantages of cooperation between reinforcement learning agents in difficult stochastic problems," in Proceedings of 9th IEEE International Conference on Fuzzy Systems, 2000.
17. H. Berenji and D. Vengerov, Learning, cooperation, and coordination in multi-agent systems, Technical Report IIS-00-10, Intelligent Inference Systems Corp., 333 W. Maude Avennue, Suite 107, Sunnyvale, CA 94085-4367, 2000.
18. D. Bernstein, S. Zilberstein, and N. Immerman, "The complexity of decentralized control of MDPs," in Proceedings of UAI-2000: The Sixteenth Conference on Uncertainty in Artificial Intelligence, 2000.
19. H. J. Blumenthal and G. Parker, "Co-evolving team capture strategies for dissimilar robots," in Proceedings of Artificial Multiagent Learning. Papers from the 2004 AAAI Fall Symposium. Technical Report FS-04-02, 2004.
20. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, SFI Studies in the Sciences of Complexity, Oxford University Press, 1999.
21. J. C. Bongard, "The legion system: A novel approach to evolving heterogeneity for collective problem solving" in R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, (eds.), Genetic Programming: Proceedings of EuroGP-2000. Vol. 1802, Edinburgh, 15-16 2000. Springer-Verlag. ISBN 3-540-67339-3, pp. 16-28.
22. C. Boutilier, "Learning conventions in multiagent stochastic domains using likelihood estimates," in Uncertainty in Artificial Intelligence, pp. 106-114, 1996.
23. C. Boutilier, "Planning, learning and coordination in multiagent decision processes," in Proceedings of the Sixth Conference on Theoretical Aspects of Rationality and Knowledge (TARK96), pp. 195-210, 1996.
24. M. Bowling, "Convergence problems of general-sum multiagent reinforcement learning," in Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann: San Francisco, CA, pp. 89-94, 2000.
25. M. Bowling, Multiagent Learning in the Presence of Agents with Limitations, PhD thesis, Computer Science Department, Carnegie Mellon University, 2003.
26. M. Bowling and M. Veloso, An analysis of stochastic game theory for multiagent reinforcement learning, Technical Report CMU-CS-00-165, Computer Science Department, Carnegie Mellon University, 2000.
27. M. Bowling and M. Veloso, "Rational and convergent learning in stochastic games," in Proceedings of Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 1021-1026, 2001.
28. M. Bowling and M. Veloso, Existence of multiagent equilibria with limited agents, Technical Report CMU-CS-02-104, Computer Science Department, Carnegie Mellon University, 2002.
29. M. Bowling and M. Veloso, "Multiagent learning using a variable learning rate," Artif. Intell., vol. 136, no. 2, pp, 215-250, 2002.
30. J. A. Boyan and M. Littman, "Packet routing in dynamically changing networks: A reinforcement learning approach," in J. D. Cowan, G. Tesauro, and J. Alspector, (eds.), Advances in Neural Information Processing Systems, Vol. 6, Morgan Kaufmann, pp. 671-678, 1994.
31. R. Brafman and M. Tennenholtz, "Efficient learning equilibrium," in Advances in Neural Information Processing Systems (NIPS-2002), 2002.
32. W. Brauer and G. Weiß, "Multi-machine scheduling - a multi-agent learning approach," in Proceedings of the Third International Conference on Multi-Agent Systems, pp. 42-48, 1998.
33. P. Brazdil, M. Gams, S. Sian, L. Torgo, and W. van de Velde, "Learning in distributed systems and multi-agent environments," in Y. Kodratoff, (ed.), Lecture Notes in Artificial Intelligence, Vol. 482, Springer-Verlag, pp. 412-423, 1991.
34. O. Buffet, A. Dutech, and F. Charpillet, "Incremental reinforcement learning for designing multiagent systems," in J. P. Muller, E. Andre, S. Sen, and C. Frasson, (eds.), Proceedings of the Fifth International Conference on Autonomous Agents, ACM Press: Montreal, Canada, pp. 31-32, 2001.
35. O. Buffet, A. Dutech, and F. Charpillet, "Learning to weigh basic behaviors in scalable agents," in Proceedings of the 1st International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS'02), 2002.
36. H. Bui, S. Venkatesh, and D. Kieronska, "A framework for coordination and learning among team of agents," in W. Wobcke, M. Pagnucco, and C. Zhang, (eds.), Agents and Multi-Agent Systems: Formalisms, Methodologies and Applications, Lecture Notes in Artificial Intelligence. Vol. 1441, Springer-Verlag, pp. 164-178, 1998.
37. H. Bui, S. Venkatesh, and D. Kieronska, "Learning other agents' preferences in multi-agent negotiation using the Bayesian classifier," Int. J. Coop. Inform. Syst., vol. 8, no. 4, pp. 275-294, 1999.
38. L. Bull, "Evolutionary computing in multi-agent environments: Partners," in T. Back, (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 370-377, 1997.
39. L. Bull, "Evolutionary computing in multi-agent environments: Operators," in D. W. V. W. Porto, N. Saravanan, and A. E. Eiben, (eds.), Proceedings of the Seventh Annual Conference on Evolutionary Programming, Springer Verlag, pp. 43-52, 1998.
40. L. Bull and T. C. Fogarty, "Evolving cooperative communicating classifier systems", in A. V. Sebald and L. J. Fogel, (eds.), Proceedings of the Fourth Annual Conference on Evolutionary Programming (EP94), pp. 308-315, 1994.
41. L. Bull and O. Holland, "Evolutionary computing in multiagent environments: Eusociality", in Proceedings of Seventh Annual Conference on Genetic Algorithms, 1997.
42. A. Cangelosi, "Evolution of communication and language using signals, symbols, and words," IEEE Trans. Evol. Comput., vol. 5, no 2, pp. 93-101, 2001.
43. Y. U. Cao, A. S. Fukunaga, and A. B. Kahng, "Cooperative mobile robotics: Antecedents and directions," Auton. Robots, vol. 4, no. 1, pp. 7-23, 1997.
44. D. Carmel, Model-based Learning of Interaction Strategies in Multi-agent systems, PhD thesis, Technion - Israel Institute of Technology, 1997.
45. D. Carmel and S. Markovitch, The M* algorithm: Incorporating opponent models into adversary search. Technical Report 9402, Technion - Israel Institute of Technology, March 1994.
46. L.-E. Cederman, Emergent Actors in World Politics: How States and Nations Develop and Dissolve, Princeton University Press, 1997.
47. G. Chalkiadakis and C. Boutilier, "Coordination in multiagent reinforcement learning: A Bayesian approach," in Proceedings of The Second International Joint Conference on Autonomous Agents & Multiagent Systems (AAMAS 2003). ACM, 2003. ISBN 1-58113-683-8.
48. H. Chalupsky, Y. Gil, C. A. Knoblock, K. Lerman, J. Oh, D. Pynadath, T. Russ, and M. Tambe, "Electric elves: Agent technology for supporting human organizations," in AI Magazine - Summer 2002, AAAI Press, 2002.
49. Y.-H. Chang, T. Ho, and L. Kaelbling, "All learning is local: Multi-agent learning in global reward games," in Proceedings of Neural Information Processing Systems (NIPS-03), 2003.
50. Y.-H. Chang, T. Ho, and L. Kaelbling, "Multi-agent learning in mobilized ad-hoc networks," in Proceedings of Artificial Multiagent Learning, Papers from the 2004 AAAI Fall Symposium, Technical Report FS-04-02, 2004.
51. C. Claus and C. Boutilier, "The dynamics of reinforcement learning in cooperative multiagent systems," in Proceedings of National Conference on Artificial Intelligence AAAI/IAAI, pp. 746-752, 1998.
52. D. Cliff and G. F. Miller, "Tracking the red queen: Measurements of adaptive progress in coevolutionary simulations", in Proceedings of the Third European Conference on Artificial Life, Springer-Verlag, pp. 200-218, 1995.
53. R. Collins and D. Jefferson, "An artificial neural network representation for artificial organisms", in H.-P. Schwefel and R. Manner, (eds.), Parallel Problem Solving from Nature: 1st Workshop (PPSN 1), Springer-Verlag: Berlin, pp. 259-263, 1991.
54. R. Collins and D. Jefferson, "AntFarm: Towards simulated evolution," in C. Langton, C. Taylor, J. D. Fanner, and S. Rasmussen, (eds.), Artificial Life II, Addison-Wesley: Redwood City, CA, pp. 579-601, 1992.
55. E. Crawford and M. Veloso, "Opportunities for learning in multi-agent meeting scheduling", in Proceedings of Artificial Multiagent Learning, Papers from the 2004 AAAI Fall Symposium. Technical Report FS-04-02, 2004.
56. V. Crespi, G. Cybenko, M. Santini, and D. Rus. Decentralized control for coordinated flow of multiagent systems. Technical Report TR2002-414, Dartmouth College, Computer Science, Hanover, NH, January 2002.
57. R. H. Crites, Large-Scale Dynamic Optimization Using Teams of Reinforcement Learning Agents, PhD thesis, University of Massachusetts Amherst, 1996.
58. M. R. Cutkosky, R. S. Englemore, R. E. Fikes, M. R. Genesereth, T. R. Gruber, W. S. Mark, J. M. Tenenbaum, and J. C. Weber, "PACT: An experiment in integrating concurrent engineering systems", in M. N. Huhns and M. P. Singh, (eds.), Readings in Agents, Morgan Kaufmann: San Francisco, CA, USA, pp. 46-55, 1997.
59. T. Dahl, M. Mataric, and G. Sukhatme, "Adaptive spatio-temporal organization in groups of robots," in Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-02), 2002.
60. R. Das, M. Mitchell, and J. Crutchfield, "A genetic algorithm discovers particle-based computation in cellular automata", in Parallel Problem Solving from Nature III, LNCS 866, Springer-Verlag, pp. 344-353, 1994.
61. J. Davis and G. Kendall, "An investigation, using co-evolution, to evolve an awari player," in Proceedings of 2002 Congress on Evolutionary Computation (CEC2002), 2002.
62. B. de Boer, "Generating vowel systems in a population of agents," in Proceedings of the Fourth European Conference Artificial Life, MIT Press, 1997.
63. K. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems, PhD thesis, University of Michigan, Ann Arbor, MI, 1975.
64. K. De Jong, Evolutionary Computation: A Unified Approach, MIT Press, 2005.
65. K. Decker, E. Durfee, and V. Lesser, "Evaluating research in cooperative distributed problem solving," in L. Gasser and M. Huhns, (eds.), Distributed Artificial Intelligence Volume II, Pitman Publishing and Morgan Kaufmann, pp. 487-519, 1989.
66. K. Decker, M. Fisher, M. Luck, M. Tennenholtz, and UKMAS'98 Contributors, "Continuing research in multi-agent systems," Knowl. Eng. Rev., vol. 14, no. 3, pp. 279-283, 1999.
67. J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, and L. Chretien, "The dynamics of collective sorting: Robot-like ants and ant-like robots," in From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, MIT Press, pp. 356-363, 1991.
68. J. Denzinger and M. Fuchs, "Experiments in learning prototypical situations for variants of the pursuit game," in Proceedings on the International Conference on Multi-Agent Systems (ICMAS-1996), pp. 48-55, 1996.
69. M. Dowell. Learning in Multiagent Systems, PhD thesis, University of South Carolina, 1995.
70. K. Dresner and P. Stone, "Multiagent traffic management: A reservation-based intersection control mechanism," in AAMAS-2004 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
71. G. Dudek, M. Jenkin, R. Milios, and D. Wilkes, "A taxonomy for swarm robots," in Proceedings of IEEE/RSJ Conference on Intelligent Robots and Systems, 1993.
72. E. Durfee, "What your computer really needs to know, you learned in kindergarten," in National Conference on Artificial Intelligence, pp. 858-864, 1992.
73. E. Durfee, V. Lesser, and D. Corkill, "Coherent cooperation among communicating problem solvers," IEEE Trans. Comput., vol. C-36, no. 11, pp. 1275-1291, 1987.
74. E. Durfee, V. Lesser, and D. Corkill, Trends in cooperative distributed problem solving, IEEE Trans. Knowl. Data Eng., vol. KDE-1, no. 1, pp. 63-83, March 1989.
75. A. Dutech, O. Buffet, and F. Charpillet, "Multi-agent systems by incremental gradient reinforcement learning," in Proceedings of Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 833-838, 2001.
76. F. Fernandez and L. Parker, "Learning in large cooperative multi-robot domains," Int. J. Robot. Autom. vol. 16, no. 4, pp. 217-226, 2001.
77. S. Ficici and J. Pollack, "Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states," in C. Adami et al., (ed.), Proceedings of the Sixth International Conference on Artificial Life, MIT Press: Cambridge, MA, pp. 238-247, 1998.
78. S. Ficici and J. Pollack, "A game-theoretic approach to the simple coevolutionary algorithm", in Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI). Springer Verlag, 2000.
79. K. Fischer, N. Kuhn, H. J. Muller, J. P. Muller, and M. Pischcl, "Sophisticated and distributed: The transportation domain," in Proceedings of the Fifth European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW'93), 1993.
80. D. Fogel, Blondie24: Playing at the Edge of Artificial Intelligence, Morgan Kaufmann, 2001. ISBN 1-55860-783-8.
81. L. Fogel, Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming, Wiley Series on Intelligent Systems, 1999.
82. D. Fudenberg and D. Levine, The Theory of Learning in Games, MIT Press, 1998.
83. A. Garland and R. Alterman, "Autonomous agents that learn to better coordinate," Auton. Agents Multi-Agent Syst., vol. 8, pp. 267-301, 2004.
84. M. Ghavamzadeh and S. Mahadevan, "Learning to communicate and act using hierarchical reinforcement learning," in AAMAS-2004 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
85. N. Glance and B. Huberman, "The dynamics of social dilemmas," Sci. Am., vol. 270, no. 3, March, pp. 76-81, 1994.
86. P. Gmytrasiewicz, A Decision-Theoretic Model of Coordination and Communication in Autonomous Systems (Reasoning Systems), PhD thesis, University of Michigan, 1992.
87. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley: Reading, MA, 1989.
88. C. Goldman and J. Rosenschein, "Mutually supervised learning in multiagent systems", in G. Weiß and S. Sen, (eds.), Adaptation and Learning in Multi-Agent Systems, Springer-Verlag: Heidelberg, Germany, Berlin, pp. 85-96, 1996.
89. B. M. Good, Evolving multi-agent systems: Comparing existing approaches and suggesting new directions, Master's thesis, University of Sussex, 2000.
90. M. Gordin, S. Sen, and N. Puppala, "Evolving cooperative groups: Preliminary results", in Working Papers of the AAAI-97 Workshop on Multiagent Learning, pp. 31-35, 1997.
91. S. Grand and D. Cliff, "Creatures: Entertainment software agents with artificial life", Auton. Agents Multi-Agent Syst., vol. 1, no. 1, pp. 39-57, 1998.
92. S. Grand, D. Cliff, and A. Malhotra, "Creatures: Artificial life autonomous software agents for home entertainment", in Proceedings of the First International Conference on Autonomous Agents (Agents-97), pp. 22-29, 1997.
93. D. L. Grecu, Using Learning to Improve Multi-Agent Systems for Design. PhD thesis, Worcester Polytechnic Institute, 1997.
94. A. Greenwald, J. Farago, and K. Hall, "Fair and efficient solutions to the Santa Fe bar problem," in Proceedings of the Grace Hopper Celebration of Women in Computing 2002, 2002.
95. A. Greenwald and K. Hall, "Correlated Q-learning," in Proceedings of the Twentieth International Conference on Machine Learning, 2003.
96. J. Grefenstette, "Lamarckian learning in multi-agent environments," in R. Belew and L. Booker, (eds.), Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufman: San Mateo, CA, pp. 303-310, 1991.
97. J. Grefenstette, C. L. Ramsey, and A. Schultz, "Learning sequential decision rules using simulation models and competition," Machine Learn., vol. 5, pp. 355-381, 1990.
98. C. Guestrin, M. Lagoudakis, and R. Parr, "Coordinated reinforcement learning," in Proceedings of the 2002 AAAI Symposium Series: Collaborative Learning Agents, 2002.
99. S. M. Gustafson, Layered learning in genetic programming for a co-operative robot soccer problem, Master's thesis, Kansas State University, Manhattan, KS, USA, 2000.
100. S. M. Gustafson and W. H. Hsu, "Layered learning in genetic programming for a co-operative robot soccer problem," in J. F. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. G. B. Tettamanzi, and W. B. Langdon, (eds.), Genetic Programming: Proceedings of EuroGP-2001. Vol. 2038, Springer-Verlag: Lake Como, Italy, 18-20 2001. ISBN 3-540-41899-7, pp. 291-301.
101. A. Hara and T. Nagao, "Emergence of cooperative behavior using ADG; Automatically Defined Groups," in Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO-99), pp. 1038-1046, 1999.
102. I. Harvey, P. Husbands, D. Cliff, A. Thompson, and N. Jakobi, "Evolutionary robotics: The Sussex approach," Robot. Auton. Syst., 1996.
103. T. Haynes, K. Lau, and S. Sen, "Learning cases to compliment rules for conflict resolution in multiagent systems," in S. Sen, (ed.), AAAI Spring Symposium on Adaptation, Coevolution, and Learning in Multiagent Systems, pp. 51-56, 1996.
104. T. Haynes and S. Sen, "Evolving behavioral strategies in predators and prey," in G. Weiß and S. Sen, (eds.), Adaptation and Learning in Multiagent Systems, Lecture Notes in Artificial Intelligence. Springer Verlag: Berlin, Germany, 1995.
105. T. Haynes and S. Sen, "Adaptation using cases in cooperative groups," in I. Imam (ed.), Working Notes of the AAAI-96 Workshop on Intelligent Adaptive Agents, Portland, OR, 1996.
106. T. Haynes and S. Sen, Cooperation of the fittest, Technical Report UTULSA-MCS-96-09, The University of Tulsa, Apr. 12, 1996.
107. T. Haynes and S. Sen, "Learning cases to resolve conflicts and improve group behavior," in M. Tambe and P. Gmytrasiewicz, (eds.), Working Notes of the AAAI-96 Workshop on Agent Modeling, Portland, OR, pp. 46-52, 1996.
108. T. Haynes and S. Sen, "Crossover operators for evolving a team," in J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, (eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann: Stanford University, CA, USA, pp. 162-167, 13-16 July 1997.
109. T. Haynes, S. Sen, D. Schoenefeld, and R. Wainwright, "Evolving a team," in E. V. Siegel and J. R. Koza, (eds.), Working Notes for the AAAI Symposium on Genetic Programming, AAAI: MIT, Cambridge, MA, USA, pp. 23-30, 10-12 Nov. 1995.
110. T. Haynes, S. Sen, D. Schoenefeld, and R. Wainwright, Evolving multiagent coordination strategies with genetic programming, Technical Report UTULSA-MCS-95-04, The University of Tulsa, May 31, 1995.
111. T. Haynes, R. Wainwright, S. Sen, and D. Schoenefeld, "Strongly typed genetic programming in evolving cooperation strategies," in L. Eshelman, (ed.), Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Morgan Kaufmann: Pittsburgh, PA, USA, pp. 271-278, ISBN 1-55860-370-0, 15-19 July 1995.
112. T. D. Haynes and S. Sen, "Co-adaptation in a team," Int. J. Comput. Intell. Org. (IJCIO), 1997.
113. D. Hillis, "Co-evolving parasites improve simulated evolution as an optimization procedure," Artif. Life II, SFI Stud. Sci. Complex., vol. 10, pp. 313-324, 1991.
114. J. Holland, Adaptation in Natural and Artificial Systems, The MIT Press: Cambridge, MA, 1975.
115. J. Holland, "Properties of the bucket brigade," in Proceedings of an International Conference on Genetic Algorithms, 1985.
116. B. Hölldobler and E. O. Wilson, The Ants, Harvard University Press, 1990.
117. W. H. Hsu and S. M. Gustafson, "Genetic programming and multi-agent layered learning by reinforcements," in W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, (eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers: New York, 9-13 July 2002, ISBN 1-55860-878-8, pp. 764-771.
118. J. Hu and M. Wellman, "Self-fulfilling bias in multiagent learning," in Proceedings of the Second International Conference on Multi-Agent Systems, 1996.
119. J. Hu and M. Wellman, "Multiagent reinforcement learning: Theoretical framework and an algorithm," in Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann: San Francisco, CA, pp. 242-250, 1998.
120. J. Hu and M. Wellman, "Online learning about other agents in a dynamic multiagent system," in K. P. Sycara and M. Wooldridge, (eds.), Proceedings of the Second International Conference on Autonomous Agents (Agents'98), ACM Press: New York, 1998, pp. 239-246, ISBN 0-89791-983-1.
121. J. Hu and M. Wellman, "Nash Q-learning for general-sum stochastic games", J. Machine Learn. Res., vol. 4, pp. 1039-1069, 2003.
122. J. Huang, N. R. Jennings, and J. Fox, "An agent architecture for distributed medical care," in M. Wooldridge and N. R. Jennings, (eds.), Intelligent Agents: Theories, Architectures, and Languages (LNAI Volume 890), Springer-Verlag: Heidelberg, Germany, pp. 219-232, 1995.
123. M. Huhns and M. Singh, "Agents and multiagent systems: Themes, approaches and challenges," in M. Huhns and M. Singh, (eds.), Readings in Agents, Morgan Kaufmann, pp. 1-23, 1998.
124. M. Huhns and G. Weiß, "Special issue on multiagent learning," Machine Learn. J., vol. 33, nos. 2-3, 1998.
125. H. Iba, "Emergent cooperation for multiple agents using genetic programming," in H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, (eds.), Parallel Problem Solving from Nature IV: Proceedings of the International Conference on Evolutionary Computation, Vol. 1141 of LNCS, Springer Verlag: Berlin, Germany, 1996, pp. 32-41, ISBN 3-540-61723-X.
126. H. Iba, "Evolutionary learning of communicating agents," Inform. Sci., vol. 108, 1998.
127. H. Iba, "Evolving multiple agents by genetic programming," in L. Spector, W. Langdon, U.-M. O'Reilly, and P. Angeline, (eds.), Advances in Genetic Programming 3, The MIT Press: Cambridge, MA, pp. 447-466, 1999.
128. I. Imam, (ed.), Intelligent Adaptive Agents. Papers from the 1996 AAAI Workshop. Technical Report WS-96-04, AAAI Press, 1996.
129. A. Ito, "How do selfish agents learn to cooperate?," in Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems, MIT Press, pp. 185-192, 1997.
130. T. Jansen and R. P. Wiegand, "Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA," in E. Cantu-Paz et al., (ed.), Prooceedings of the Genetic and Evolutionary Computation Conference (GECCO), Springer-Verlag, 2003.
131. N. Jennings, K. Sycara, and M. Wooldridge, "A roadmap of agents research and development," Auton Agents Multi-Agent Syst., vol. 1, pp. 7-38, 1998.
132. N. Jennings, L. Varga, R. Aarnts, J. Fuchs, and P. Skarek, "Transforming standalone expert systems into a community of cooperating agents," Int. J. Eng. Appl. Artif. Intell., vol. 6, no. 4, pp. 317-331, 1993.
133. K.-C. Jim and C. L. Giles, "Talking helps: Evolving communicating agents for the predator-prey pursuit problem," Artif. Life, vol. 6, no. 3, pp. 237-254, 2000.
134. H. Juille and J. Pollack, "Coevolving the "ideal" trainer: Application to the discovery of cellular automata rules", in Proceedings of the Third Annual Genetic Programming Conference (GP-98), 1998.
135. L. Kaelbling, M. Littman, and A. Moore, "Reinforcement learning: A survey," J. Artif. Intell. Res., vol. 4, pp. 237-285, 1996.
136. S. Kapetanakis and D. Kudenko, Improving on the reinforcement learning of coordination in cooperative multi-agent systems, in Proceedings of the Second Symposium on Adaptive Agents and Multi-agent Systems (AISB02), 2002.
137. S. Kapetanakis and D. Kudenko, "Reinforcement learning of coordination in cooperative multiagent systems", in Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI02), 2002.
138. G. Kendall and G. Whitwell, "An evolutionary approach for the tuning of a chess evaluation function using population dynamics," in Proceedings of the 2001 Congress on Evolutionary Computation (CEC-2001), IEEE Press, pp. 995-1002, 27-30, 2001.
139. G. Kendall and M. Willdig, "An investigation of an adaptive poker player", in Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01), 2001.
140. H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa, "RoboCup: The robot world cup initiative," in W. L. Johnson and B. Hayes-Roth, (eds.), Proceedings of the First International Conference on Autonomous Agents (Agents'97), ACM Press: New York, 5-8, ISBN 0-89791-877-0, pp. 340-347, 1997.
141. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
142. M. Lauer and M. Riedmiller, "An algorithm for distributed reinforcement learning in cooperative multi-agent systems," in Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann: San Francisco, CA, pp. 535-542, 2000.
143. L. R. Leerink, S. R. Schultz, and M. A. Jabri, "A reinforcement learning exploration strategy based on ant foraging mechanisms," in Proceedings of the Sixth Australian Conference on Neural Networks, Sydney, Australia, 1995.
144. V. Lesser, "Cooperative multiagent systems: A personal view of the state of the art," IEEE Trans. Knowl. Data Eng., vol. 11, no. 1, pp. 133-142, 1999.
145. V. Lesser, D. Corkill, and E. Durfee, An update on the distributed vehicle monitoring testbed, Technical Report UM-CS-1987-111, University of Massachessets Amherst, 1987.
146. M. I. Lichbach, The Cooperator's Dilemma, University of Michigan Press, 1996. ISBN 0472105728.
147. M. Littman, "Markov games as a framework for multi-agent reinforcement learning", in Proceedings of the 11th International Conference on Machine Learning (ML-94), Morgan Kaufmann: New Brunswick, NJ, pp. 157-163, 1994.
148. M. Littman, "Friend-or-foc Q-learning in general-sum games," in Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann, pp. 322-328, 2001.
149. A. Lubberts and R. Miikkulainen, "Co-evolving a go-playing neural network," in Coevolution: Turning Adaptive Algorithms upon Themselves, (Birds-on-a-Feather Workshop, Genetic and Evolutionary Computation Conference), 2001.
150. M. Luck, M. d'Inverno, M. Fisher, and FoMAS'97 Contributors, "Foundations of multi-agent systems: Techniques, tools and theory," Knowl. Eng. Rev., vol. 13, no. 3, pp. 297-302, 1998.
151. S. Luke, "Genetic programming produced competitive soccer softbot teams for RoboCup97," in J. R. Koza et al, (ed.), Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, pp. 214-222, 1998.
152. S. Luke, C. Hohn, J. Farris, G. Jackson, and J. Hendler, "Co-evolving soccer softbot team coordination with genetic programming," in Proceedings of the First International Workshop on RoboCup, at the International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.
153. S. Luke and L. Spector, "Evolving teamwork and coordination with genetic programming," in J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press: Stanford University, CA, USA, pp. 150-156, 28-31 1996.
154. S. Luke, K. Sullivan, G. C. Balan, and L. Panait, Tunably decentralized algorithms for cooperative target observation, Technical Report GMU-CS-TR-2004-1, Department of Computer Science, George Mason University, 2004.
155. S. Luke and R. P. Wiegand, "Guaranteeing coevolutionary objective measures", in Poli et al. [201], pp. 237-251.
156. S. Mahadevan and J. Connell, "Automatic programming of behavior-based robots using reinforcement learning," in National Conference on Artificial Intelligence, pp. 768-773, 1991.
157. R. Makar, S. Mahadevan, and M. Ghavamzadeh, "Hierarchical multi-agent reinforcement learning," in J. P. Muller, E. Andre, S. Sen, and C. Frasson, (eds.), Proceedings of the Fifth International Conference on Autonomous Agents, ACM Press: Montreal, Canada, pp. 246-253, 2001.
158. M. Mataric, Interaction and Intelligent Behavior, PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 1994.
159. Also Technical Report AITR-1495.
160. M. Mataric, "Learning to behave socially," in Third International Conference on Simulation of Adaptive Behavior, 1994.
161. M. Mataric, "Reward functions for accelerated learning," in International Conference on Machine Learning, pp. 181-189, 1994.
162. M. Mataric, "Reinforcement learning in the multi-robot domain," Anton. Robots, vol. 4, no. 1, pp. 73-83, 1997.
163. M. Mataric, "Using communication to reduce locality in distributed multi-agent learning," Joint Special Issue on Learn Auton. Robots, Machine Learn, vol. 31, nos. 1-3, pp. 141-167,
164. and Auton. Robots, vol. 5, nos. 3-4, pp. 335-354, Jul/Aug 1998.
165. M. Mataric, M. Nilsson, and K. Simsarian, "Cooperative multi-robot box-pushing," in Proceedings of IEEE/RSJ Conference on Intelligent Robots and Systems, pp. 556-561, 1995.
166. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (3rd edn.), Springer-Verlag: Berlin, 1996.
167. T. Miconi, "A collective genetic algorithm", in E. Cantu-Paz et al., (ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 876-883, 2001.
168. T. Miconi, "When evolving populations is better than coevolving individuals: The blind mice problem," in Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), 2003.
169. M. Mitchell, J. Crutchfield, and R. Das, "Evolving cellular automata with genetic algorithms: A review of recent work," in Proceedings of the First International Conference on Evolutionary Computation and its Applications (EvCA '96), 1996.
170. N. Monekosso, P. Remagnino, and A. Szarowicz, "An improved Q-learning algorithm using synthetic pheromones," in E. N. B. Dunin-Keplicz, (ed.), From Theory to Practice in Multi-Agent Systems, Second International Workshop of Central and Eastern Europe on Multi-Agent Systems, CEEMAS 2001 Cracow, Poland, September 26-29, 2001. Revised Papers, Lecture Notes in Artificial Intelligence LNAI-2296, Springer-Verlag, 2002.
171. N. D. Monekosso and P. Remagnino, "Phe-Q: A pheromone based Q-learning," in Australian Joint Conference on Artificial Intelligence, pp. 345-355, 2001.
172. N. D. Monekosso and P. Remagnino, "An analysis of the pheromone Q-learning algorithm," in Proceedings of the VIII Iberoamerican Conference on Artificial Intelligence IBERAMIA-02, pp. 224-232, 2002.
173. N. D. Monekosso, P. Remagnino, and A. Szarowicz, "An improved Q-learning algorithm using synthetic pheromones," in Proceedings of the Second Workshop of Central and Eastern Europe on Multi-Agent Systems CEEMAS-01, pp. 197-206, 2001.
174. J. Moody, Y. Liu, M. Saffell, and K. Youn, "Stochastic direct reinforcement: Application to simple games with recurrence," in Proceedings of Artificial Multiagent Learning, Papers front the 2004 AAAI Fall Symposium. Technical Report FS-04-02, 2004.
175. R. Mukherjee and S. Sen, "Towards a pareto-optimal solution in general-sum games," in Agents-2001 Workshop on Learning Agents, 2001.
176. U. Mukhopadjyay, L. Stephens, and M. Huhns, "An intelligent system for document retrieval in distributed office environment," J. Am. Soc. Inform Sci., vol. 37, 1986.
177. J. Muller and M. Pischel, "An architecture for dynamically interacting agents," J. Intell. Coop. Inform. Syst., vol. 3, no. 1. pp. 25-45, 1994.
178. M. Mundhe and S. Sen, "Evaluating concurrent reinforcement learners," in Proceedings of the International Conference on Multiagent System, 2000.
179. M. Mundhe and S. Sen, "Evolving agent societies that avoid social dilemmas," in D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee, and H.-G. Beyer, (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Morgan Kaufmann: Las Vegas, Nevada, USA, 10-12 2000, pp. 809-816, ISBN 1-55860-708-0.
180. Y. Nagayuki, S. Ishii, and K. Doya, "Multi-agent reinforcement learning: An approach based on the other agent's internal model," in Proceedings of the International Conference on Multi-Agent Systems (ICMAS-00), 2000.
181. M. V. Nagendra-Prasad, Learning Situation-Specific Control in Multi-Agent Systems, PhD thesis, University of Massachusetts Amherst, 1997.
182. R. Nair, D. Pynadath, M. Yokoo, M. Tambe, and S. Marsella, "Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings," in Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), 2003.
183. M. Nowak and K. Sigmund, "Evolution of indirect reciprocity by image scoring/the dynamics of indirect reciprocity," Nature, vol. 393, pp. 573-577, 1998.
184. A. Nowe, K. Verbeeck, and T. Lenaerts, Learning agents in a homo egualis society, Technical report, Computational Modeling Lab - VUB, March 2001.
185. L. Nunes and E. Oliveira, "Learning from multiple sources," in AAMAS-2004- Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
186. T. Ohko, K. Hiraki, and Y. Arzai, "Addressee learning and message interception for communication load reduction in multiple robots environments," in G. Weiß, (ed.), Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, Lecture Notes in Artificial Intelligence 1221, Springer-Verlag, 1997.
187. E. Ostergaard, G. Sukhatme, and M. Mataric, "Emergent bucket brigading - a simple mechanism for improving performance in multi-robot constrainedspace foraging tasks," in Proceedings of the Fifth International Conference on Autonomous Agents, 2001.
188. L. Pagie and M. Mitchell, "A comparison of evolutionary and revolutionary search," in R. K. Belew and H. Juill, (eds.), Coevolution: Turning Adaptive Algorithms upon Themselves, San Francisco, California, USA, pp. 20-25, 7 2001.
189. L. Panait and S. Luke, "Ant foraging revisited," in Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), 2004.
190. L. Panait and S. Luke, "Learning ant foraging behaviors," in Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), 2004.
191. L. Panait and S. Luke, "A pheromone-based utility model for collaborative foraging," in AAMAS-2004 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
192. L. Panait, R. P. Wiegand, and S. Luke, "A sensitivity analysis of a cooperative coevolutionary algorithm biased for optimization," in Genetic and Evolutionary Computation Conference - GECCO-2004, Springer, 2004.
193. L. Panait, R. P. Wiegand, and S. Luke, "A visual demonstration of convergence properties of cooperative coevolution," in Parallel Problem Solving from Nature - PPSN-2004, Springer, 2004.
194. L. A. Panait, R. P. Wiegand, and S. Luke, "Improving coevolutionary search for optimal multiagent behaviors", in Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), 2003.
195. C. Papadimitriou and J. Tsitsiklis, "Complexity of markov decision processes," Math. Operat. Res., vol. 12, no. 3, pp. 441-450, 1987.
196. L. Parker, "Current state of the art in distributed autonomous mobile robotics," in L. Parker, G. Bekey, and J. Barhen, (eds.), Distributed Autonomous Robotic Systems 4, Springer-Verlag, pp. 3-12, 2000.
197. L. Parker, "Multi-robot learning in a cooperative observation task," in Proceedings of Fifth International Symposium on Distributed Autonomous Robotic Systems (DARS 2000), 2000.
198. L. Parker, "Distributed algorithms for multi-robot observation of multiple moving targets," Auton Robots, vol. 12 no. 3, 2002.
199. L. Parker, C. Touzet, and F. Fernandez, "Techniques for learning in multi-robot teams," in T. Balch and L. Parker, (eds.), Robot Teams: From Diversity to Polymorphism, AK Peters, 2001.
200. M. Peceny, G. Weiß, and W. Brauer, Verteiltes maschinelles lernen in fertigungsumgebungen, Technical Report FKI-218-96, Institut für Informatik, Technische Universitat Munchen, 1996.
201. M. Peeters, K. Verbeeck, and A. Nowe, "Multi-agent learning in conflicting multi-level games with incomplete information," in Proceedings of Artificial Multiagent Learning, Papers from the 2004 AAAI Fall Symposium. Technical Report FS-04-02, 2004.
202. L. Peshkin, K.-E. Kim, N. Meuleau, and L. Kaelbling, "Learning to cooperate via policy search," in Sixteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 2000, pp. 307-314.
203. R. Poli, J. Rowe, and K. D. Jong, (eds.), Foundations of Genetic Algorithms (FOGA) VII, 2002, Morgan Kaufmann.
204. J. Pollack and A. Blair, "Coevolution in the successful learning of backgammon strategy", Machine Learn., vol. 32, no. 3, pp. 225-240, 1998.
205. J. Pollack, A. Blair, and M. Land, "Coevolution of a backgammon player", in C. O. Langton and K. Shimohara, (eds.), Artificial Life V: Proc. of the Fifth Int. Workshop on the Synthesis and Simulation of Living Systems, The MIT Press: Cambridge, MA, pp. 92-98, 1997.
206. E. Popovici and K. DeJong, "Understanding competitive co-evolutionary dynamics via fitness landscapes," in Artificial Multiagent Symposium, Part of the 2004 AAAI Fall Symposium on Artificial Intelligence, 2004.
207. M. Potter, The Design and Analysis of a Computational Model of Cooperative Coevolution, PhD thesis, George Mason University, Fairfax, Virginia, 1997.
208. M. Potter and K. De Jong, "A cooperative coevolutionary approach to function optimization," in Y. Davidor and H.-P. Schwefel, (eds.), Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN III), Springer-Verlag, pp. 249-257, 1994.
209. M. Potter and K. De Jong, "Cooperative Coevolution: An architecture for evolving coadapted subcomponents," Evol. Comput., vol. 8, no. 1, pp. 1-29, 2000.
210. M. Potter, K. DC Jong, and J. J. Grefenstette, "A coevolutionary approach to learning sequential decision rules," in Proceedings from the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 366-372, 1995.
211. M. Potter, L. Meeden, and A. Schultz, "Heterogeneity in the coevolved behaviors of mobile robots: The emergence of specialists," in Proceedings of The Seventeenth International Conference on Artificial Intelligence (IJCAI-2001), 2001.
212. N. Puppala, S. Sen, and M. Gordin, "Shared memory based cooperative Coevolution," in Proceedings of the 1998 IEEE World Congress on Computational Intelligence, IEEE Press: Anchorage, Alaska, USA, pp. 570-574, 1998.
213. M. Quinn, "A comparison of approaches to the evolution of homogeneous multi-robot teams," in Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), IEEE Press: COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, 27-30 2001, pp. 128-135. ISBN 0-7803-6658-1.
214. M. Quinn, "Evolving communication without dedicated communication channels," in Advances in Artificial Life: Sixth European Conference on Artificial Life (ECAL01), 2001.
215. M. Quinn, L. Smith, G. Mayley, and P. Husbands, Evolving formation movement for a homogeneous multi-robot system: Teamwork and role-allocation with real robots, Cognitive Science Research Paper 515. School of Cognitive and Computing Sciences, University of Sussex, Brighton, BN1 9QG. ISSN 1350-3162, 2002.
216. C. Reynolds, "An evolved, vision-based behavioral model of coordinated group motion," in From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), pp. 384-392, 1993.
217. C. Reynolds, "Competition, Coevolution and the game of tag," in R. A. Brooks and P. Maes, (eds.), Artificial Life IV, Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems., MIT Press, pp. 59-69, 1994.
218. C. W. Reynolds, "Flocks, herds, and schools: a distributed behavioral model," Comput. Graph., vol. 21, no. 4, pp. 25-34, 1987.
219. P. Riley and M. Veloso, "On behavior classification in adversarial environments," in L. Parker, G. Bekey, and J. Barhen (eds.), Distributed Autonomous Robotic Systems 4, Springer-Verlag, pp. 371-380, 2000.
220. A. Robinson and L. Spector, "Using genetic programming with multiple data types and automatic modularization to evolve decentralized and coordinated navigation in multi-agent systems," in In Late-Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO-2002), The International Society for Genetic and Evolutionary Computation, 2002.
221. C. Rosin and R. Belew, "New methods for competitive coevolution," Evol. Comput., vol. 5, no. 1, pp. 1-29, 1997.
222. R. Salustowicz, M. Wiering, and J. Schmidhuber, Learning team strategies with multiple policy-sharing agents: A soccer case study, Technical report, ISDIA, Corso Elvezia 36, 6900 Lugano, Switzerland, 1997.
223. R. Salustowicz, M. Wiering, and J. Schmidhuber, "Learning team strategies: Soccer case studies," Machine Learn., vol. 33, nos. 2-3, pp. 263-282, 1998.
224. A. Samuel, "Some studies in machine learning using the game of checkers," IBM J. Res. Develop., vol. 3, no. 3, pp. 210-229, 1959.
225. T. Sandholm and R. H. Crites, "On multiagent Q-learning in a semi-competitive domain," in Adaption and Learning in Multi-Agent Systems, pp. 191-205, 1995.
226. H. Santana, G. Ramalho, V. Corruble, and B. Ratitch, "Multi-agent patrolling with reinforcement learning," in AAMAS-2004- Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
227. G. Saunders and J. Pollack, "The evolution of communication schemes over continuous channels," in From Animals to Animats 4 - Proceedings of the Fourth International Conference on Adaptive Behaviour, 1996.
228. J. Sauter, R. S. Matthews, H. Van Dyke Parunak, and S. Brueckner, "Evolving adaptive pheromone path planning mechanisms," in Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-02), pp. 434-440, 2002.
229. J. Sauter, H. Van Dyke Parunak, S. Brueckner, and R. Matthews, "Tuning synthetic pheromones with evolutionary computing," in R. E. Smith, C. Bonacina, C. Hoile, and P. Marrow, (eds.), Evolutionary Computation and Multi-Agent Systems (ECOMAS), San Francisco, California, USA, 7 pp. 321-324, 2001.
230. J. Schmidhuber, "Realistic multi-agent reinforcement learning," in Learning in Distributed Artificial Intelligence Systems, Working Notes of the 1996 ECAI Workshop, 1996.
231. J. Schmidhuber and J. Zhao, "Multi-agent learning with the success-story algorithm," in ECAI Workshop LDAIS/ICMAS Workshop LIOME, pp. 82-93, 1996.
232. J. Schneider, W.-K. Wong, A. Moore, and M. Riedmiller, "Distributed value functions," in Proceedings of the Sixteenth International Conference on Machine Learning, pp. 371-378, 1999.
233. A. Schultz, J. Grefenstette, and W. Adams, "Robo-shepherd: Learning complex robotic behaviors," in Robotics and Manufacturing: Recent Trends in Research and Applications. Vol. 6, ASME Press, pp. 763-768, 1996.
234. U. M. Schwuttke and A. G. Quan, "Enhancing performance of cooperating agents in realtime diagnostic systems", in Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), 1993.
235. M. Sekaran and S. Sen, "To help or not to help", in Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society, Pittsburgh, PA, pp. 736-741, 1995.
236. S. Sen, "Multiagent systems: Milestones and new horizons", Trends Cognitive Sci., vol. 1, no. 9, pp. 334-339, 1997.
237. S. Sen, "Special issue on evolution and learning in multiagent systems," Int. J. Human-Comput. Stud., vol.48, no. 1, 1998.
238. S. Sen and M. Sekaran, "Using reciprocity to adapt to others", in G. Weiß and S. Sen (eds.), International Joint Conference on Artificial Intelligence Workshop on Adaptation and Learning in Multiagent Sytems, Lecture Notes in Artificial Intelligence, Springer-Verlag, pp. 206-217, 1995.
239. S. Sen and M. Sekaran, "Multiagent coordination with learning classifier systems", in G. Weiß and S. Sen, (eds.), Proceedings of the IJCAI Workshop on Adaption and Learning in Multi-Agent Systems, Volume 1042, Springer Verlag, pp. 218-233, 1996. ISBN 3-540-60923-7.
240. S. Sen and M. Sekaran, "Individual learning of coordination knowledge", J. Exp. Theo. Artif. Intel., vol. 10, no. 3, pp. 333-356, 1998.
241. S. Sen, M. Sekaran, and J. Hale, "Learning to coordinate without sharing information", in Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 426-431, 1994.
242. Y. Shoham, R. Powers, and T. Grenager, "On the agenda(s) of research on multi-agent learning," in Proceedings of Artificial Multiagent Learning, Papers from the 2004 AAAI Fall Symposium. Technical Report FS-04-02, 2004.
243. R. Smith and B. Gray, Co-adaptive genetic algorithms: An example in othello strategy, Technical Report TCGA 94002, University of Alabama, Department of Engineering Science and Mechanics, 1993.
244. L. Spector and J. Klein, "Evolutionary dynamics discovered via visualization in the breve simulation environment," in Workshop Proceedings of the 8th International Conference on the Simulation and Synthesis of Living Systems, pp. 163-170, 2002.
245. L. Spector, J. Klein, C. Perry, and M. Feinstein, "Emergence of collective behavior in evolving populations of flying agents," in E. Cantu-Paz et al., (ed.), Prooceedings of the Genetic and Evolutionary Computation Conference (GECCO). Springer-Verlag, 2003.
246. R. Steeb, S. Cammarata, F. Hayes-Roth, P. Thorndyke, and R. Wesson, "Distributed intelligence for air fleet control," in A. Bond and L. Gasser (eds.), Readings in Distributed Artificial Intelligence, Morgan Kaufmann Publishers, pp. 90-101, 1988.
247. L. Steels, "A self-organizing spatial vocabulary," Artif. Life. vol. 2, no. 3, pp. 319-332, 1995.
248. L. Steels, "Emergent adaptive lexicons," in P. Macs, (ed.), Proceedings of the Simulation of Adaptive Behavior Conference, MIT Press, 1996.
249. L. Steels, "Self-organising vocabularies," in Proceedings of Artificial Life V, 1996.
250. L. Steels, "The spontaneous self-organization of an adaptive language," in S. Muggleton (ed.), Machine Intelligence 15, Oxford University Press: Oxford, UK, 1996.
251. L. Steels, "Synthesising the origins of language and meaning using co-evolution, self-organisation and level formation," in J. Hurford, C. Knight, and M. Studdert-Kennedy (eds.), Approaches to the Evolution of Language: Social and Cognitive Bases, Edinburgh University Press, 1997.
252. L. Steels, "The puzzle of language evolution," Kognitionswissenschaft, vol. 8, no. 4, pp. 143-150, 2000.
253. L. Steels and F. Kaplan, "Collective learning and semiotic dynamics," in Proceedings of the European Conference on Artificial Life, pp. 679-688, 1999.
254. P. Stone, "Layered learning in multiagent systems," in Proceedings of National Conference on Artificial Intelligence AAAI/IAAI, 1997.
255. P. Stone, "Layered Learning in Multi-Agent Systems," PhD thesis, Carnegie Mellon University, 1998.
256. P. Stone and R. Sutton, "Keepaway soccer: A machine learning testbed," in A. Birk, S. Coradeschi, and S. Tadokoro, (eds.), RoboCup 2001: Robot Soccer World Cup V, volume 2377 of Lecture Notes in Computer Science, Springer, pp. 214-223, 2002. ISBN 3-540-43912-9.
257. P. Stone and M. M. Veloso, "Multiagent systems: A survey from a machine learning perspective," Auton. Robots, vol. 8, no. 3, pp. 345-383, 2000.
258. N. Sturtevant and R. Korf, "On pruning techniques for multi-player games," in Proceedings of National Conference on Artificial Intelligence (AAAI), pp. 201-207, 2000.
259. D. Subramanian, P. Druschel, and J. Chen, "Ants and reinforcement learning: A case study in routing in dynamic networks," in Proceedings of Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 832-839, 1997.
260. N. Suematsu and A. Hayashi, "A multiagent reinforcement learning algorithm using extended optimal response," in Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-02), pp. 370-377, 2002.
261. D. Suryadi and P. J. Gmytrasiewicz, "Learning models of other agents using influence diagrams," in Preceedings of the 1999 International Conference on User Modeling, pp. 223-232, 1999.
262. R. Sutton, "Learning to predict by the methods of temporal differences," Machine Learn., vol. 3, pp. 9-44, 1988.
263. R. Sutton and A. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998.
264. J. Svennebring and S. Koenig, "Trail-laying robots for robust terrain coverage," in Proceedings of the International Conference on Robotics and Automation (ICRA-03), 2003.
265. P. 't Hoen and K. Tuyls, "Analyzing multi-agent reinforcement learning using evolutionary dynamics," in Proceedings of the 15th European Conference on Machine Learning (ECML), 2004.
266. M. Tambe, "Recursive agent and agent-group tracking in a real-time dynamic environment," in V. Lesser and L. Gasser (eds.), Proceedings of the First International Conference on Multiagent Systems (KMAS-95). AAAI Press, 1995.
267. M. Tan, "Multi-agent reinforcement learning: Independent vs. cooperative learning," in M. N. Huhns and M. P. Singh (eds.), Readings in Agents, Morgan Kaufmann: San Francisco, CA, USA, pp. 487-494, 1993.
268. P. Tangamchit, J. Dolan, and P. Khosla, "The necessity of average rewards in cooperative multirobot learning," in Proceedings of IEEE Conference on Robotics and Automation, 2002.
269. G. Tesauro, "Temporal difference learning and TD-gammon," Commun. ACM, vol. 38, no. 3, pp. 58-68, 1995.
270. G. Tesauro and J. O. Kephart, "Pricing in agent economies using multi-agent Q-learning," Auton. Agents Multi-Agent Syst., vol. 8, pp. 289-304, 2002.
271. S. Thrun, "Learning to play the game of chess," in G. Tesauro, D. Touretzky, and T. Leen, (eds.), Advances in Neural Information Processing Systems 7, The MIT Press: Cambridge, MA, pp. 1069-1076, 1995.
272. K. Turner, A. K. Agogino, and D. H. Wolpert, "Learning sequences of actions in collectives of autonomous agents," in Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-02), pp. 378-385, 2002.
273. K. Tuyls, K. Vcrbeeck, and T. Lenaerts, "A selection-mutation model for Q-learning in multiagent systems," in AAMAS-2003 - Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, 2003.
274. W. Uther and M. Veloso, "Adversarial reinforcement learning. Technical Report CMU-CS-03-107, School of Computer Science, Carnegie Mellon University, 2003.
275. H. Van Dyke Parunak, "Applications of distributed artificial intelligence in industry," in G. M. P. O'Hare and N. R. Jennings, (eds.), Foundations of Distributed AI. John Wiley & Sons, 1996.
276. L. Z. Varga, N. R. Jennings, and D. Cockburn, "Integrating intelligent systems into a cooperating community for electricity distribution management," Int. J. Expert Syst. Appl., vol. 7, no. 4, pp. 563-579, 1994.
277. J. Vidal and E. Durfee, "Agents learning about agents: A framework and analysis," in Working Notes of AAAI-97 Workshop on Multiagent Learning, 1997.
278. J. Vidal and E. Durfee, "The moving target function problem in multiagent learning," in Proceedings of the Third Annual Conference on Multi-Agent Systems, 1998.
279. J. Vidal and E. Durfee, "Predicting the expected behavior of agents that learn about agents: The CLRI framework," Autonomous Agents and Multi-Agent Systems, January 2003.
280. K. Wagner, "Cooperative strategies and the evolution of communication," Artif. Life, vol. 6, no. 2, pp. 149-179, Spring 2000.
281. X. Wang and T. Sandholm, "Reinforcement learning to play an optimal Nash equilibrium in team Markov games," in Advances in Neural Information Processing Systems (NIPS-2002), 2002.
282. R. Watson and J. Pollack, "Cocvolutionary dynamics in a minimal substrate," in E. Cantu-Paz et al, (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2001.
283. R. Weihmayer and H. Velthuijsen, "Application of distributed AI and cooperative problem solving to telecommunications," in J. Liebowitz and D. Prercau, (eds.), AI Approaches to Telecommunications and Network Management, IOS Press, 1994.
284. M. Weinberg and J. Rosenschein, "Best-response multiagent learning in non-stationary environments," in AAMAS-2004 - Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, 2004.
285. G. Weiß, Some studies in distributed machine learning and organizational design. Technical Report FKI-189-94, Institut fur Informatik, TU München, 1994.
286. G. Weiß, Distributed Machine Learning, Sankt Augustin: Infix Verlag, 1995.
287. G. Weiß, ed., Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, Number 1221 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1997.
288. G. Weiß "Special issue on learning in distributed artificial intelligence systems," J. Exp. Theo. Artif. Intell., vol. 10, no. 3, 1998.
289. G. Weiß, ed., Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, 1999.
290. G. Weiß and P. Dillenbourg, "What is 'multi' in multi-agent learning?" in P. Dillenbourg, (ed.), Collaborative Learning, Cognitive and Computational Approaches, Pergamon Press, pp. 64-80, 1999.
291. G. Weiß and S. Sen (eds.), Adaptation and Learning in Multiagent Systems," Lecture Notes in Artificial Intelligence Vol. 1042, Springer-Verlag, 1996.
292. M. Wellman and J. Hu, "Conjectural equilibrium in multiagent learning," Machine Learn., vol. 33, nos. 2-3, pp. 179-200, 1998.
293. J. Werfel, M. Mitchell, and J. P. Crutchfield, "Resource sharing and coevolution in evolving cellular automata," IEEE Trans. Evol. Comput., vol. 4, no. 4, pp. 388, November, 2000.
294. B. B. Werger and M. Mataric, "Exploiting embodiment in multi-robot teams, Technical Report IRIS-99-378, University of Southern California, Institute for Robotics and Intelligent Systems, 1999.
295. G. M. Werner and M. G. Dyer, "Evolution of herding behavior in artificial animals," in From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), 1993.
296. T. White, B. Pagurek, and F. Oppacher, "ASGA: Improving the ant system by integration with genetic algorithms," in J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. Riolo, (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann: University of Wisconsin, Madison, Wisconsin, USA, 22-25, pp. 610-617, 1998.
297. S. Whiteson and P. Stone, "Concurrent layered learning," in AAMAS-2003 - Proceedings of the Second International Joint Conference on Autonomous Agents and Multi Agent Systems, 2003.
298. R. P. Wiegand, Analysis of Cooperative Revolutionary Algorithms, PhD thesis, Department of Computer Science, George Mason University, 2003.
299. R. P. Wiegand, W. Liles, and K. De Jong, "An empirical analysis of collaboration methods in cooperative revolutionary algorithms," in E. Cantu-Paz et al., (ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1235-1242, 2001.
300. R. P. Wiegand, W. Liles, and K. De Jong, "Analyzing cooperative coevolution with evolutionary game theory," in D. Fogel, (ed.), Proceedings of Congress on Evolutionary Computation (CEC-02), IEEE Press, pp. 1600-1605, 2002.
301. R. P. Wiegand, W. Liles, and K. De Jong, "Modeling variation in cooperative coevolution using evolutionary game theory," in Poli et al. [201], pp. 231-248.
302. R. P. Wiegand and J. Sarma, "Spatial embedding and loss of gradient in cooperative coevolutionary algorithms," in Parallel Problem Solving from Nature - PPSN-2004, Springer, 2004.
303. M. Wiering, R. Salustowicz, and J. Schmidhuber, "Reinforcement learning soccer teams with incomplete world models," J. Auton. Robots, vol. 7, no. 1, pp. 77-88, 1999.
304. A. Williams, "Learning to share meaning in a multi-agent system," Auton. Agents Multi-Agent Syst., vol. 8, pp. 165-193, 2004.
305. E. Wilson, Sociobiology: The New Synthesis, Belknap Press, 1975.
306. D. H. Wolpert and K. Turner, "Optimal payoff functions for members of collectives," Adv. Complex Syst., vol. 4, nos. 2-3, pp. 265-279, 2001.
307. D. H. Wolpert, K. Turner, and J. Frank, "Using collective intelligence to route internet traffic," in Advances in Neural Information Processing Systems-11, Denver, pp. 952-958, 1998.
308. D. H. Wolpert, K. R. Wheller, and K. Turner, "General principles of learning-based multi-agent systems," in O. Etzioni, J. P. Müller, and J. M. Bradshaw, (eds.), Proceedings of the Third International Conference on Autonomous Agents (Agents'99), ACM Press: Seattle, WA, USA, pp. 77-83, 1999.
309. M. Wooldridge, S. Bussmann, and M. Klosterberg, "Production sequencing as negotiation," in Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM-96), 1996.
310. A. Wu, A. Schultz, and A. Agah, "Evolving control for distributed micro air vehicles," in IEEE Computational Intelligence in Robotics and Automation Engineers Conference, 1999.
311. H. Yanco and L. Stein, "An adaptive communication protocol for cooperating mobile robots," in From Animats to Animats: International Conference on Simulation of Adaptive Behavior, pp. 478-485, 1993.
312. N. Zaera, D. Cliff, and J. Bruten, (Not) Evolving collective behaviours in synthetic fish, Technical Report HPL-96-04, Hewlett-Packard Laboratories, 1996.
313. B. Zhang and D. Cho, "Coevolutionary fitness switching: Learning complex collective behaviors using genetic programming," in Advances in Genetic Programming III, MIT Press, 1998, pp. 425-445.
314. J. Zhao and J. Schmidhuber, "Incremental self-improvement for life-time multi-agent reinforcement learning," in P. Maes, M. Mataric, J.-A. Meyer, J. Pollack, and S. W. Wilson, (eds.), Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From Animals to Animats 4, MIT Press: Cape Code, USA, 9-13 pp. 516-525, 1996, ISBN 0-262-63178-4.