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International Joint Conference on Artificial Intelligence

IJCAI 1995: Adaption and Learning in Multi-Agent Systems pp 1–21Cite as

Adaptation and learning in multi-agent systems: Some remarks and a bibliography

  • Introduction
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1042))

Abstract

In the last years the topic of adaptation and learning in multi-agent systems has gained increasing attention in Artificial Intelligence. This article is intended to provide a compact, introductory and motivational guide to this topic. The article consists of two sections. In the first section,“Remarks”, the range and complexity of this topic is outlined by taking a general look at the concept of multi-agent systems and at the notion of adaptation and learning in these systems. This includes a description of key dimensions for classifying multi-agent systems, as well as a description of key criteria for characterizing single-agent and multi-agent learning as the two principal categories of learning in multiagent systems. In the second section, “Bibliography”, an extensive list of pointers to relevant and related work on multi-agent learning done in (Distributed) Artificial Intelligence, economics, and other disciplines is provided.

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References

  1. Bond, A.H., & Gasser, L. (Eds.) (1988). Readings in distributed artificial intelligence. Morgan Kaufmann.

    Google Scholar 

  2. Castelfranchi, C., & Müller, J.-P. (Eds.) (1995). From reaction to cognition. Lecture Notes in Artificial Intelligence, Vol. 957. Springer-Verlag.

    Google Scholar 

  3. Castelfranchi, C., & Werner, E. (Eds.) (1994). Artificial social systems. Lecture Notes in Artificial Intelligence, Vol. 930. Springer-Verlag.

    Google Scholar 

  4. Demazeau, Y., & Müller, J.-P. (Eds.) (1990). Decentralized A.I. North-Holland.

    Google Scholar 

  5. Demazeau, Y., & Müller, J.-P. (Eds.) (1991). Decentralized A.I. 2 North-Holland.

    Google Scholar 

  6. Durfee, E.H., & Rosenschein, J.S. (1994). Distributed problem solving and multi-agent systems: Comparisons and examples. Proceedings of the 13th International Workshop on Distributed Artificial Intelligence (pp. 94–104).

    Google Scholar 

  7. Gasser, L., & Huhns, M. N. (Eds.) (1989). Distributed artificial intelligence, Vol. 2. Pitman.

    Google Scholar 

  8. Huhns, M.N. (Ed.) (1987). Distributed artificial intelligence. Pitman.

    Google Scholar 

  9. ICMAS (1995). Proceedings of the First International Conference on Multiagent Systems. AAAI Press/MIT Press.

    Google Scholar 

  10. Kodratoff, Y., & Michalski, R.S. (Eds.) (1990). Machine learning, Vol. III. Morgan Kaufmann.

    Google Scholar 

  11. Langley, P. (1995). Elements of machine learning. Morgan Kaufmann.

    Google Scholar 

  12. Michalski, R.S., Carbonell, J.G., & Mitchell, T.M. (Eds.) (1983). Machine learning, Vol. I. Morgan Kaufmann.

    Google Scholar 

  13. Michalski, R.S., Carbonell, J.G., & Mitchell, T.M. (Eds.) (1986). Machine learning, Vol. II. Morgan Kaufmann.

    Google Scholar 

  14. Michalski, R.S., & Tecuci, G. (Eds.) (1994). Machine learning, Vol. IV. Morgan Kaufmann.

    Google Scholar 

  15. Minsky, M. (1961). Steps towards artificial intelligence. Proceedings of the IRE (pp. 8–30). Reprinted in E.A. Feigenbaum & J. Feldman (Eds.) (1963), Computers and thought (pp. 406–450), McGraw-Hill.

    Google Scholar 

  16. Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Prentice Hall.

    Google Scholar 

  17. Werner, E., & Demazeau, Y. (Eds.) (1992). Decentralized A.I. 3. North-Holland.

    Google Scholar 

  18. Wooldridge, M.J., & Jennings, N.R. (Eds.) (1995). Intelligent agents. Lecture Notes in Artificial Intelligence, vol. 890. Springer-Verlag.

    Google Scholar 

PART I: Work in distributed artificial intelligence principles, algorithms, applications, tools

  1. Asada, M., Uchibe, E., & Hosoda, K. (1995). Agents that learn from other competitive agents. In (WS-IMLC95).

    Google Scholar 

  2. Boyan, J.A., & Littman, M.L. (1993). Packet routing in dynamically changing networks: A reinforcement learning approach. In J.D. Cowan, G. Tesauro & J. Alspector (Eds.), Advances in Neural Information Processing Systems (Vol. 6, pp. 671–678). San Fransisco: Morgan Kaufmann.

    Google Scholar 

  3. Brazdil, P., Gams, M., Sian, S., Torgo, L., & van de Velde, W. (1991). Learning in distributed systems and multi-agent environments. In Y. Kodratoff (Ed.), Machine learning — EWSL-91 (pp. 412–423). Lecture Notes in Artificial Intelligence, vol. 482. Berlin: Springer-Verlag.

    Google Scholar 

  4. Brazdil, P., & Muggleton, S. (1991). Learning to relate terms in a multiple agent environment. In Y. Kodratoff (Ed.), Machine Learning-EWSL-91 (pp. 424–439). Berlin: Springer-Verlag.

    Google Scholar 

  5. Byrne, C., & Edwards, P. (1995). Collaborating to refine knowledge. In (WS-IMLC95).

    Google Scholar 

  6. Chan, P.K., & Stolfo, S.J. (1993). Toward parallel and distributed learning by meta-learning. Working Notes AAAI Workshop Know. Disc. Databases (pp. 227–240).

    Google Scholar 

  7. Chan, P.K., & Stolfo, S.J. (1993). Meta-learning for multistrategy and parallel learning. Proceedings of the Second International Workshop on Multistrategy Learning (pp. 150–165).

    Google Scholar 

  8. Chan, P.K., & Stolfo, S.J. (1993). Toward multistrategy parallel and distributed learning in sequence analysis. Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (pp. 65–73).

    Google Scholar 

  9. Chan, P.K., & Stolfo, S.J. (1993). Experiments on multistrategy learning by meta-learning. Proceedings of the Second International Conference on Inform. Know. Management (pp. 314–323).

    Google Scholar 

  10. Clouse, J.A. (1995). Learning from an automated training agent. In (WS-IMLC95).

    Google Scholar 

  11. Davies, W., &: Edwards, P. (1995). Distributed learning: An agent-based approach to data-mining. In (WS-IMLC95).

    Google Scholar 

  12. Dorigo, M., & Gambardella, L.M. (1995). Ant-Q. A reinforcement learning approach to combinatorial optimization. Technical Report 95-01. IRIDIA, Université Libre de Bruxelles.

    Google Scholar 

  13. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: Optimization by a colony of cooperating agents. Appears in IEEE Transactions on Systems, Man, and Cybernetics, 26(2).

    Google Scholar 

  14. Dowell, M.L. (1995). Learning in multiagent systems. Dissertation. Department of Electrical and Computer Engineering, University of South Carolina.

    Google Scholar 

  15. Dowell, M.L., & Bonnell, R.D. (1991). Learning for distributed artificial intelligence. Proceedings of the Twenty-Third Southeastern Symposium on System Theory (pp. 218–221).

    Google Scholar 

  16. Dowell, M.L., & Stephens, L.M. (1994). MAGE: Additions to the AGE algorithm for learning in multi-agent systems. Proceedings of the Second International Working Conference on Cooperating Knowledge Based Systems (CKBS94).

    Google Scholar 

  17. Edwards, P., & Davies, W. (1993). A heterogeneous multi-agent learning system. In S.M. Deen (Ed.), Proceedings of the Special Interest Group on Co-operating Knowledge Based Systems (pp. 163–184).

    Google Scholar 

  18. Findler, N.V. (1991). Distributed control of collaborating and learning expert systems for street traffic signals. In Lewis & Stephanon (Eds.), IFAC Distributed Intelligence Systems (pp. 125–130). Pergamon Press.

    Google Scholar 

  19. Gil, Y. (1995). Acquiring knowledge from users in a reflective architecture. In (WS-IMLC95).

    Google Scholar 

  20. Grosof, B. (1995). Conflict resolution in advice taking and instruction for learning agents. In (WS-IMLC95).

    Google Scholar 

  21. Huhns, M.N., Mukhopadhyay, U., Stephens, L.M., & Bonnell, R.D. (1987). DAI for document retrieval: The MINDS project. In M.N. Huhns (Ed.), Distributed Artificial Intelligence (pp. 249–283). Pitman.

    Google Scholar 

  22. Humphrys, M. (1995). W-learning: Competition among selfish Q-learners. Technical Report no. 362. Computer Laboratory, University of Cambridge.

    Google Scholar 

  23. Kinney, M., & Tsatsoulis, C. (1993). Learning communication strategies in distributed agent environments. Working Paper WP-93-4. Intelligent Design Laboratory, University of Kansas.

    Google Scholar 

  24. Littman, M.L. (1994). Markov games as a framework for multi-agent reinforcement learning. Proceedings of the 1994 International Conference on Machine Learning (pp. 157–163).

    Google Scholar 

  25. Littman, M.L., & Boyan, J.A. (1993). A distributed reinforcement learning scheme for network routing. Report CMU-CS-93-165. School of Computer Science, Carnegie Mellon University.

    Google Scholar 

  26. Maes, P. (1994). Social interface agents: Acquiring competence by learning from users and other agents. In O. Etzioni (Ed.), Working Notes of the 1994 AAAI Spring Symposium on Software Agents.

    Google Scholar 

  27. Markey, K.L. (1993). Efficient learning of multiple degree-of-freedom control problems with quasi-independent Q-agents. Proceedings of the 1993 Connectionist Models Summer School. NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  28. Matarić, M.J. (1994). Learning to behave socially. Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior — From animals to animats (pp. 453–462).

    Google Scholar 

  29. Michalski, R., & Tecuci, G. (1995). Machine learning. A multistrategy approach. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  30. Nagendra Prasad, M.V., Lander, S., & Lesser, V.R. (1995). Experiences with a Multi-agent Design System. Technical Report. Department of Computer Science, University of Massachusetts.

    Google Scholar 

  31. Nagendra Prasad, M.V., Lesser, V., & Lander, S. (1995). Learning organizational roles in a heterogeneous multi-agent system. Technical Report TR-95-35. Department of Computer Science, University of Massachusetts.

    Google Scholar 

  32. Nagendra Prasad, M.V., Lesser, V., & Lander, S. (1995). Learning experiments in a heterogeneous multi-agent system. In Working Notes of the IJ-CAI95 Workshop on Adaptation and Learning in Multiagent Systems (pp. 59–64).

    Google Scholar 

  33. Ohko, T., Hiraki, K., & Anzai, Y. (1995). LEMMING: A learning system for multi-robot environments. Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots vand Systems (Vol. 2, pp. 1131–1146).

    Google Scholar 

  34. Parker, L. (1993). Adaptive action selection for cooperative agent teams. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 442–450).

    Google Scholar 

  35. Parker, L. (1993). Learning in cooperative robot teams. Paper presented at IJCAI93 Workshop on Dynamically Interacting Robots.

    Google Scholar 

  36. Payne, T.R., Edwards, P., & Green, C.L. (1995). Experience with rule induction and k-nearest neighbour methods for interface agents that learn. In (WS-IMLC95).

    Google Scholar 

  37. Pearson, D., & Huffman, S. (1995). Combining learning from instruction with recovery from incorrect knowledge. In (WS-IMLC95).

    Google Scholar 

  38. Provost, F.J. (1995). Scaling up inductive learning with massive parallelism. Machine Learning.

    Google Scholar 

  39. Provost, F.J., & Hennessy, D. (1994). Distributed machine learning: Scaling up with coarse-grained parallelism. Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology.

    Google Scholar 

  40. Sandholm, T.W., & Crites, R.H. (1995). Multiagent reinforcement learning in the iterated prisoner's dilemma. Biosystems, Special Issue on the prisoner's dilemma.

    Google Scholar 

  41. Schaerf, A., Shoham, Y., & Tennenholtz, M. (1995). Adaptive load balancing: A study in multi-agent-learning. Journal of Artificial Intelligence Research, 2, 475–500.

    Google Scholar 

  42. Sekaran, M., & Sen, S. (1994). Multi-agent learning in non-cooperative domains. Proceedings of the 12th National Conference on Artificial Intelligence (Vol. 2, pp. 1489). Menlo Park, CA: AAAI Press/MIT Press.

    Google Scholar 

  43. Sen, S., Sekaran, M., & Hale, J. (1994). Learning to coordinate without sharing information. Proceedings of the 12th National Conference on Artificial Intelligence (Vol. 1, pp. 426–431). Menlo Park, CA: AAAI Press/MIT Press.

    Google Scholar 

  44. Shavlik, J., & Maclin, R. (1995). Learning from instruction and experience in competitive situations. In (WS-IMLC95).

    Google Scholar 

  45. Shoham, Y., & Tennenholtz, M. (1992). Emergent conventions in multi-agent systems: initial experimental results and observations. Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (pp. 225–231).

    Google Scholar 

  46. Shoham, Y., & Tennenholtz, M. (1994). Co-learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511. Department of Computer Science, Stanford University.

    Google Scholar 

  47. Shoham, Y., & Tennenholtz, M. (1995). Social Laws for Artificial Agent Societies: Off-line Design. Appears in Artificial Intelligence, 73.

    Google Scholar 

  48. Sian, S.S. (1990). The role of cooperation in multi-agent learning. Proceedings of the International Working Conference on Cooperating Knowledge Based Systems. (pp. 164–177). Springer-Verlag.

    Google Scholar 

  49. Sian, S.S. (1991). Adaptation based on cooperative learning in multi-agent systems. In Y. Demazeau, & J.-P. Müller (Eds.), Decentralized A.I. 2 (pp. 257–272). Amsterdam: North-Holland.

    Google Scholar 

  50. Sian, S.S. (1991). Extending learning to multiple agents: issues and a model for multi-agent machine learning (MA-ML). In Y. Kodratoff (Ed.), Machine learning — EWSL-91 (pp. 440–456). Berlin: Springer-Verlag.

    Google Scholar 

  51. Sikora, R., & Shaw, M.J. (1990). A double-layered learning approach to acquiring rules for financial classification. Faculty Working Paper No. 90-1693. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign.

    Google Scholar 

  52. Sikora, R., & Shaw, M.J. (1991). A distributed problem-solving approach to inductive learning. Faculty Working Paper 91-0109. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, Illinois.

    Google Scholar 

  53. Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents. Proceedings of the Tenth International Conference on Machine Learning (pp. 330–337).

    Google Scholar 

  54. Tennenholtz, M. (1995). On Computational Social Laws for Dynamic Non-Homogeneous Social Structures. Appears in Journal of Experimental and Theoretical Artificial Intelligence.

    Google Scholar 

  55. Weiß, G. (1993). Collective learning and action coordination. Proceedings of the 13th International Conference on Distributed Computing Systems (pp. 203–209).

    Google Scholar 

  56. Weiß, G. (1993). Learning to coordinate actions in multi-agent systems. Proceedings of the 13th International Conference on Artificial Intelligence (Vol. 1, pp. 311–316).

    Google Scholar 

  57. Weiß, G. (1993). Action selection and learning in multi-agent environments. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 502–510).

    Google Scholar 

  58. Weiß, G. (1993). Lernen und Aktionskoordinierung in Mehragentensystemen. In J. Müller (Ed.), Verteilte Künstliche Intelligenz — Methoden und Anwendungen (pp. 122–132). Mannheim: BI Verlag.

    Google Scholar 

  59. Weiß, G. (1994). Some studies in distributed machine learning and organizational design. Techn. Rep. FKI-189-94. Institut für Informatik, TU München.

    Google Scholar 

  60. Weiß, G. (1995). Distributed reinforcement learning. Robotics and Autonomous Systems, 15, 135–142.

    Google Scholar 

  61. Weiß, G. (1995). Distributed machine learning. Sankt Augustin: Infix-Verlag.

    Google Scholar 

  62. WS-IMLC95 (1995). Workshop “Agents that learn from other agents” held at the 1995 International Machine Learning Conference. (Proceedings are ALSO available at http://www.cs.wisc.edu/∼shavlik/ml95w1/pubs.html.)

    Google Scholar 

PART I: Work in distributed artificial intelligence theory

  1. Daley, R.P., Kalyanasundaram, B., &: Velauthapillai, M. (1992). Breaking the probability 1/2 barrier in fin-type learning. Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 203–217), Pittsburgh, Pennsylvania. ACM Press.

    Google Scholar 

  2. Daley, R.P., Kalyanasundaram, B., & Velauthapillai, M. (1992). The power of probabilism in popperian finite learning. Proceedings of the Third International Workshop on Analogical and Inductive Inference (pp. 151–169). Dagstuhl Castle, Germany.

    Google Scholar 

  3. Daley, R.P., Kalyanasundaram, B., & Velauthapillai, M. (1993). Capabilities of fallible finite learning. Proceedings of the Sixth Annual Conference on Computational Learning Theory (pp. 199–208), Santa Cruz, CA. ACM Press.

    Google Scholar 

  4. Daley, R.P., Pitt, L., Velauthapillai, M., Will, T. (1991). Relations between probabilistic and team one-shot learners. In L. Valiant & M. Warmuth (Eds.), Proceedings of the Workshop on Computational Learning Theory (pp. 228–239). Morgan Kaufmann.

    Google Scholar 

  5. Jain, S., & Sharma, A. (1990). Finite learning by a team. In M. Fulk & J. Case (Eds.), Proceedings of the Third Annual Workshop on Computational Learning Theory (pp. 163–177). Morgan Kaufmann.

    Google Scholar 

  6. Jain, S., & Sharma, A. (1990). Language learning by a team. In M.S. Paterson (Ed.), Proceedings of the 17th International Colloquium on Automata, Languages and Programming (pp. 153–166). Springer-Verlag.

    Google Scholar 

  7. Jain, S., & Sharma, A. (1993). Computational limits on team identification of languages. Technical Report 9301. School of Computer Science and Engineering, University of New South Wales.

    Google Scholar 

  8. Jain, S., & Sharma, A. (1993). Probability is more powerful than team for language identification. Proceedings of the Sixth Annual Conference on Computational Learning Theory (pp. 192–198). ACM Press.

    Google Scholar 

  9. Jain, S., & Sharma, A. (1994). On aggregation teams of learning machines. SCS&E Report no. 9405. School of Computer Science and Engineering, University of New South Wales.

    Google Scholar 

  10. Jain, S., & Sharma, A. (1995). Team learning of formal languages. In (WS-IMLC95).

    Google Scholar 

  11. Jain, S., Sharma, A., & Velauthapillai, M. (1994). Finite identification of functions by teams with success ratio 1/2 and above. Journal of Computer and System Sciences.

    Google Scholar 

  12. Pitt, L. (1984). A characterization of probabilistic inference. Proceedings of the 25th Symposium on the Foundations of Computer Science.

    Google Scholar 

  13. Pitt, L. (1989). Probabilistic inductive inference. Journal of the ACM, 36, 383–433.

    Google Scholar 

  14. Pitt, L., & Smith, C. (1988). Probability and plurality for aggregations of learning machines. Information and Computation, 77, 77–92.

    Google Scholar 

  15. Smith, C. (1982). The power of pluralism for automatic program synthesis. Journal of the ACM, 29, 1144–1165.

    Google Scholar 

  16. Velauthapillai, M. (1989). Inductive inference with bounded number of mind changes. Proceedings of the Workshop on Computational Learning Theory (pp. 200–213).

    Google Scholar 

PART II: Work in economics

  1. Adler, P. (1990). Shared learning. Management Science, 36/8, 938–957.

    Google Scholar 

  2. Argyris, C. (1982). Reasoning, learning and action: individual and organizational. San Fransisco: Jossey-Bass.

    Google Scholar 

  3. Argyris, C. (1993). On organizational learning. Cambridge, Mass: Blackwell.

    Google Scholar 

  4. Argyris, C., & Schön, D.A. (1978). Organizational learning. Reading, MA: Addison-Wesley.

    Google Scholar 

  5. Arrow, K. (1962). The implications of learning by doing. Review of Economic Studies, 29, 166–170.

    Google Scholar 

  6. Bennis, W., & Nanus, B. (1985). Organizational learning: The management of the collective self. New Management, 3, 6–13.

    Google Scholar 

  7. Brown, J.S., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40–57.

    Google Scholar 

  8. Cangelosi, V.E., & Dill, W.R. (1965). Organizational learning: Observations toward a theory. Administrative Science Quaterly, 10, 175–203.

    Google Scholar 

  9. Cohen, M.D. (1986). Artificial intelligence and the dynamic performance of organizational designs. In J.G. March & R. Weissinger-Baylon (eds.), Ambiguity and command (pp. 53–71). Marshfield, Mass.: Pitman.

    Google Scholar 

  10. Cohen, M.D. (1991). Individual learning and organizational routine: Emerging connections. Organization Science, 2(1), 135–139.

    Google Scholar 

  11. Cohen, M.D. (1992). When can two heads learn better than one? Results from a computer model of organizational learning. In M. Masuch & M. Warglien (Eds.), Artificial intelligence in organization and management theory (pp. 175–188), Amsterdam: North-Holland.

    Google Scholar 

  12. Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quaterly, 35, 128–152.

    Google Scholar 

  13. Cohen, M.D., March, J.G., & Olsen, J.P. (1972). A garbage can model of organizational choice. Administrative Science Quaterly, 17(1), 1–25.

    Google Scholar 

  14. Cohen, M.D., & Sproull, L.S. (1991) (Eds.). Organization Science, 2(1) [Special Issue on Organizational Learning].

    Google Scholar 

  15. Daft, R.L., & Huber, G.P. (1987). How organizations learn: A communication framework. Research in the Sociology of Organizations, 5, 1–36.

    Google Scholar 

  16. Derry, D. (1983). Decision-making, problem-solving, and organizational learning. Omega, 11, 321–328.

    Google Scholar 

  17. Dixon, N.M. (1990). Action learning, action science and learning new skills. Industrial & Commercial Training, 22(4), 1–16.

    Google Scholar 

  18. Dixon, N.M. (1992). Organizational learning: A review of the literature with implications for HRD professionals. Human Resource Development Quaterly, 3(1), Spring, 29–49.

    Google Scholar 

  19. Dodgson, M. (1993). Organizational learning: A review of some literatures. Organization Studies, 14/4, 375–394.

    Google Scholar 

  20. Duncan, R., & Weiss, A. (1979). Organizational learning: Implications for organizational design. In B.M. Staw (Ed.), Research in organizational behavior (Vol. 1, pp. 75–123). Greenwich, Conn.: JAI Press.

    Google Scholar 

  21. Easterby-Smith, M. (1990). Creating a learning organisation. Personal Review, 19(5), 24–28.

    Google Scholar 

  22. Epple, D., Argote, L., & Devadas, R. (1991). Organizational learning curves: A method for investigating intra-plant transfer of knowledge acquired through learning by doing. Organization Science, 2(1), 58–70.

    Google Scholar 

  23. Fiol, M., & Lyles, M.A. (1993). Organizational learning. Academy of Management Review, 10, 803–813.

    Google Scholar 

  24. Friedlander, F. (1983). Patterns of individual and organizational learning. In Shrivastava and Associates (Eds.), The executive mind: New insights on managerial thought and action (pp. 192–220). San Fransisco: Jossey-Bass.

    Google Scholar 

  25. Garratt, B. (1990). Creating a learning organization: A guide to leadership, learning and development.

    Google Scholar 

  26. Garratt, B., & Burgoyne, J.G. (1987). The learning organization. London: Fontana/Collins.

    Google Scholar 

  27. Grantham, C. (1994). The learning organization. In S.A. Katsikides (Ed.), Informatics, organization and society (pp. 228–250). Wien: Oldenbourg.

    Google Scholar 

  28. Hall, D.T., & Fukami, C.V. (1979). Organization design and adult learning. In B.M. Staw (Ed.), Research in organizational behavior (Vol. 1, pp. 125–167). Greenwich, Conn.: JAI Press.

    Google Scholar 

  29. Hedberg, B. (1981). How organizations learn and unlearn. In P.C. Nystrom & W.H. Starbuck (Eds.), Handbook of organizational design (Vol. 1, pp. 1–27). New York: Oxford University Press.

    Google Scholar 

  30. Herriott, S. R., Levinthal, D., & March, J. G. (1985). Learning from experience in organizations. The American Economic Review, 75(2), 298–302.

    Google Scholar 

  31. Huber, G.P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), February, 88–115.

    Google Scholar 

  32. Hutchins, E. (1991). Organizing work by adaptation. Organization Science, 2(1), 14–39.

    Google Scholar 

  33. Jelinek, M. (1979). Institutionalizing innovations: A study of organizational learning systems. New York: Praeger.

    Google Scholar 

  34. Kim, D. (1993). The link between individual and organizational learning. Sloan Management Review, Fall, 37–50.

    Google Scholar 

  35. Lant, T. (1994). Computer simulations of organizations as experiential learning systems: Implications for organization theory. In K.M. Carley & M.J. Prietula (Eds.), Computational organization theory, Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  36. Lant, T., & Mezias, S. (1990). Managing discontinuous change: a simulation study of organizational learning and entrepreneurship. Strategic Management Journal, 11, 147–179.

    Google Scholar 

  37. Lant, T., & Mezias, S. (1990). An organizational learning model of convergence and reorientation. Strategic Management Journal, 11, 147–179.

    Google Scholar 

  38. Levinthal, D.A. (1991). Organizational adaptation and environmental selection-Interrelated processes of change. Organization Science, 2(1), 140–145.

    Google Scholar 

  39. Levitt, B., & March, J.G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.

    Google Scholar 

  40. Lounamaa, P.H., & March, J.G. (1987). Adaptive coordination of a learning team. Management Science, 33, 107–123.

    Google Scholar 

  41. Lundberg, C. (1989). On organizational learning: Implications and opportunities for expanding organizational development. Research in Organizational Change and Development, 3(6), 126–182.

    Google Scholar 

  42. March, J.G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

    Google Scholar 

  43. March, J.G., & Olsen, J.P. (1975). The uncertainty of the past: Organizational learning under ambiguity. European Journal of Political Research, 3, 147–171.

    Google Scholar 

  44. March, J.G., Sproull, L.S., & Tamuz, M. (1991). Learning from samples of one or fewer. Organization Science, 2(1), 1–13.

    Google Scholar 

  45. Marsick, V. (Ed.) (1987). Learning in the workplace. New York: Croom Helm.

    Google Scholar 

  46. Masuch, M., & Lapotin, P. (1989). Beyond garbage cans: An AI model of organizational choice. Administrative Science Quaterly, 34(1), 38–67.

    Google Scholar 

  47. Miles, R.H., & Randolph, W.A. (1980). Influence of organizational learning styles on early development. In J.R. Kimberly & R.H. Miles (Eds.), The organizational life cycle: Issues in the creation, transformation, and decline of organizations (pp. 44–82). San Francisco: Jossey-Bass.

    Google Scholar 

  48. Mills, D.Q., & Friesen, B. (1992). The learning organization. European Management Journal, 10(2), 146–156.

    Google Scholar 

  49. Mody, A. (1990). Learning through allianes. Washington: The World Bank.

    Google Scholar 

  50. Normann, R. (1985). Developing capabilities for organizational learning. In J.M. Pennings & Associates (Eds.), Organizational strategy and change: New views on formulating and implementing strategic decisions (pp. 217–248). San Francisco: Jossey-Bass.

    Google Scholar 

  51. Nystrom, P.C., & Starbuck, W.H. (1984). To avoid organizational crises, unlearn. Organizational Dynamics, 53–65.

    Google Scholar 

  52. Pautzke, G. (1989). Die Evolution der organisatorischen Wissensbasis: Bausteine zu einer Theorie des organisatorischen Lernens. Herrsching: Verlag Barbara Kirsch.

    Google Scholar 

  53. Pedler, M., Boydell, T., & Burgoyne, J. (1989). Towards the learning company. Management Education and Development, 20, 1–8.

    Google Scholar 

  54. Perelman, L. (1984). The learning enterprise: Adult learning, human capital and economic development. Washington, DC: Council of State Planning Agencies.

    Google Scholar 

  55. Probst, G.J.B., & Büchel, B.S.T. (1994). Organisationales Lernen. Wettbewerbsvorteil der Zukunft. Wiesbaden: Gabler.

    Google Scholar 

  56. Pucik, V. (1988). Strategic alliances, organizational learning, and competitive advantage: The HRD agenda. Human Resource Management, 27(1), 77–93.

    Google Scholar 

  57. Reinhardt, R. (1993). Das Modell organisationaler Lernfähigkeit und die Gestaltung lernfähiger Organisationen. Frankfurt am Main: Peter Lang.

    Google Scholar 

  58. Reber, G. (1992). Lernen, organisationales. In E. Frese (Ed.), Handwörterbuch der Organisation (pp. 1240–1255).

    Google Scholar 

  59. Revans, R.W. (1980). Action learning: New techniques for management. London: Blond & Briggs.

    Google Scholar 

  60. Sattelberger, T. (Ed.) (1994). Die lernende Organisation. Konzepte für eine neue Qualität der Unternehmensführung. Wiesbaden: Gabler.

    Google Scholar 

  61. Schein, E.H. (1993). How can organizations learn faster? The challenge of entering the green room. Sloan Management Review, Winter, 85–92.

    Google Scholar 

  62. Senge, P. (1990). The Fifth Discipline: The art and practice of the learning organization. New York: Doubleday.

    Google Scholar 

  63. Simon, H.A. (1991). Bounded rationality and organizational learning. Organization Science, 2(1), February, 125–134.

    Google Scholar 

  64. Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20, 7–28.

    Google Scholar 

  65. Starbuck, W.H., & Dutton, J.M. (1973). Designing adaptive organizations. Journal of Business Policy, 3, 21–28.

    Google Scholar 

  66. Stata, R. (1989). Organizational learning — The key to management innovation. Sloan Management Review, 30(3), 63–74.

    Google Scholar 

  67. Warglien, M. (1992). Exit, voice, and learning: Adaptive behavior and competition in a hotelling world. In M. Masuch & M. Warglien (Eds.), Artificial intelligence in organization and management theory (pp. 189–214), Amsterdam: North-Holland.

    Google Scholar 

  68. Weick, K.E. (1991). The nontraditional quality of organizational learning. Organization Science, 2(1), 116–124.

    Google Scholar 

  69. Wolff, R. (1982). Der Prozeβ des Organisierens: Zu einer Theorie des organisationeilen Lernens. Spardorf: Wilfer.

    Google Scholar 

  70. Yelle, L.E. (1990). The learning curve: Historical review and comprehensive survey. Decision Sciences, 10, 302–328.

    Google Scholar 

PART III: Others

  1. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall

    Google Scholar 

  2. Dillenbourg, P., Mendelsohn, P., & Schneider, D. (1994). The distribution of pedagogical roles in a multi-agent learning environment. In R. Lewis & P. Mendelsohn (Eds.), Lessons from Learning (pp. 199–216). Amsterdam: North-Holland.

    Google Scholar 

  3. Dillenbourg, P., Baker, M., Blaye, A., & O'Malley, C. (1995, to appear). The evolution of research on collaborative learning. In H. Spada & P. Reimann (Eds.), Learning in Humans and Machines.

    Google Scholar 

  4. Laughlin, P.R. (1988). Collective induction: group performance, social combination processes, and mutual majority and minority influence. Journal of Personality and Social Psychology, 54(2), 254–267.

    Google Scholar 

  5. Mandl, H., & Renkl, A. (1992). A plea for “more local” theories of cooperative learning. Learning and Instruction, 2, 281–285.

    Google Scholar 

  6. Roschelle, J. (1992). Learning by collaboration: Convergent conceptual change. Journal of the Learning Sciences, 2, 235–276.

    Google Scholar 

  7. Slavin, R.E. (1983). Cooperative learning. New York: Longman.

    Google Scholar 

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  1. Institut für Informatik, Technische Universität München, D-80290, München, Germany

    Gerhard Weiß

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Gerhard Weiß Sandip Sen

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Weiß, G. (1996). Adaptation and learning in multi-agent systems: Some remarks and a bibliography. In: Weiß, G., Sen, S. (eds) Adaption and Learning in Multi-Agent Systems. IJCAI 1995. Lecture Notes in Computer Science, vol 1042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60923-7_16

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  • DOI: https://doi.org/10.1007/3-540-60923-7_16

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