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References
Albus, J.S. (1981). Brain, behavior, and robotics. Byte Book, Subsidiary of McGraw-Hill, Chapter 6, pp. 139–179.
Brooks, R.A. (1990). Elephants don't play chess. Robotics and Autonomous Systems, 6, 3–15.
CoopIS (1996). Proceedings if the First IFCIS International Conference on Cooperative Information Systems, IEEE-CS Press.
Bull, L., & Fogarty, T. (1996). Evolutionary computing in cooperative multiagent environments. In (Sen, 1996, pp. 22–27).
Carley, K.M., & Prietula, M.J. (Eds.) (1994). Computational organization theory. Lawrence Erlbaum Associates.
Drogoul, A., Ferber, J., Corbara, B., & Fresneau, D. (1991). A behavioral simulation model for the study of emergent social structures. In Proceedings of the First European Conference on Artificial Life.
Finin, T., McKay, D., Fritzson, R., & McEntire, R. (1993). KQML: An information and knowledge exchange protocol. In International Conference on Building and Sharing of Very Large-Scale Knowledge Bases.
Galliers, J.R. (1991). Modeling autonomous belief revision in dialogue. In Y. Demazeau & J.-P. Müller (Eds.), Decentralized Artificial Intelligence 2. Elsevier.
Gaspar, G. (1991). Communication and belief changes in a society of agents: Towards a formal model of an autonomous agent. In Y. Demazeau & J.-P. Müller (Eds.), Decentralized Artificial Intelligence 2. Elsevier.
Grefenstette, J., & Daley, R. (1991). Methods for competitive and cooperative co-evolution. In (Sen, 1996, pp. 45–50).
Harnad, S. (1990). The symbol grounding problem. In Physica D, 42, 335–346.
Haynes, T., Lau, K., & Sen, S. (1996). Learning cases to compliment rules for conflict resolution in multiagent systems. In (Sen, 1996, pp. 51–56).
Haynes, T., & Sen, S. (1996). Evolving behavioral strategies in predators and prey. In (Weiß & Sen, 1996, pp. 113–126).
Ishida, T., & Korf, R.E. (1991). Moving target search. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 204–210).
Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8, pp. 293f.
Jennings, N.R., Malheiro B., & Oliveira, E. (1994). Belief revision in multiagent systems. In Proceedings of the 11th European Conference on Artificial Intelligence (pp. 294–298).
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., & Osawa, E. (1995). Robocup: The robot world cup initiative. Working Notes on the IJCAI-95 Workshop on Entertainment and AI/Alife (pp. 19–24).
Maes, P., & Kozierok, R. (1990). Learning interface agents. In Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. 459–465).
Minsky, M. (1961). Steps towards artificial intelligence. In Proceedings of the IRE (pp. 8–30). Reprinted in E.A. Feigenbaum & J. Feldman (Eds.) (1963), Computers and thought (pp. 406–450), McGraw-Hill.
Mitchell, T.M. (1978). Version spaces: An approach to concept learning. Ph.D. Thesis. Computer Science Department, Stanford University.
Nagendra Prasad, M.V., Lesser, V.R., & Lander, S. (1995). Learning organizational roles in a heterogeneous multi-agent system. In Proceedings of the Second International Conference on Multiagent Systems (pp. 291–298).
Nagendra Prasad, M.V., Lesser, V.R., & Lander, S. (1996). On reasoning and retrieval in distributed case bases. Journal of Visual and Image Representation (Special Issue on Digital Libraries), 7(1), 74–87.
Newell, A., & Simon, H.A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126.
Pollack, M.E., & Ringuette, M. (1990). Introducing the tileworld: experimentally evaluating agent architectures. In Proceedings of the National Conference on Artificial Intelligence (pp. 183–189).
Rosen, R. (1985). Anticipatory systems — Philosophical, mathematical and methodological foundations. Pergamon Press.
Sen, S. (Ed.) (1996). Adaptation, coevolution and learning in multiagent systems. Papers from the 1996 AAAI Symposium. Technical Report SS-96-01. AAAI Press.
Smith, R.G. (1980). The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers, C-29(12), 357–366.
Stone, P., & Veloso, M. (1996). Towards collaborative and adversarial learning: A case study in robotic soccer. In (Sen, 1996, pp. 88–92).
Sullivan, J.W., & Tylor, S.W. (Eds.) (1990). Intelligent user interfaces. ACM Press.
Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the Tenth International Conference on Machine Learning (pp. 330–337).
Weiß, G. (1994). Some studies in distributed machine learning and organizational design. Technical Report FKI-189-94. Institut für Informatik, Technische Universität München.
Weiß, G. (1996). Adaptation and learning in multi-agent systems: Some remarks and a bibliography. In (Weiß & Sen, 1996, pp. 1–21).
Weiß, G., & Sen, S. (Eds.) (1996). Adaption and learning in multi-agent systems. Lecture Notes in Artificial Intelligence, Vol. 1042. Springer-Verlag.
Whitehead, S., et al. (1993). Learning multiple goal behavior via task decomposition and dynamic policy merging. In J. H. Connell et al. (Eds.), Robot learning. Academic Press.
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Weiß, G. (1997). Reader's guide. In: Weiß, G. (eds) Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. LDAIS LIOME 1996 1996. Lecture Notes in Computer Science, vol 1221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62934-3_37
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DOI: https://doi.org/10.1007/3-540-62934-3_37
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