Artificial Intelligence Review

, Volume 6, Issue 1, pp 35–66 | Cite as

Trends in distributed artificial intelligence

  • B. Chaib-Draa
  • B. Moulin
  • R. Mandiau
  • P. Millot
Article

Abstract

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.

Key Words

distributed artificial intelligence reasoning about others organization cooperation communication cohesion coordination negotiation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alder, M. R., Davis, A. B., Weihmayer, R. and Worrest, R. W. (1989). Conflict-resolution strategies for nonhierarchical distributed agents. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 139–161.Google Scholar
  2. Aiello, N. (1986). User-directed control of parallelism: the cage system. Technical Report KSL-86-31, Knowledge Systems Laboratory, Computer Science Department, Stanford University.Google Scholar
  3. Agha, G. and Hewitt, C. (1988). Concurrent programming using actors. In Yonezawa, Y. and Tokoro, M. (Eds.), Object-Oriented Concurrent Programming, MIT Press, MA.Google Scholar
  4. Agree, P. (1988). The Dynamic Structure of Everyday Life. Ph.D. thesis, Department of Computer Science, MIT.Google Scholar
  5. Allen, J. F. and Perrault, C. R. (1980). Analyzing intention in utterances. Artificial Intelligence, 15, 143–180.Google Scholar
  6. Allen, J. F. (1983). Recognizing intentions from natural language utterances. In Brady, M. and Berwick, R. C. (Eds.), Computational Models of Discourse, MIT Press, Cambridge, MA.Google Scholar
  7. Allen, J. F. (1986). Natural Language Understanding, Benjamin/Cummings Publishing, Menlo Park, CA.Google Scholar
  8. Becker, H. S. (1960). Notes on the concept of commitment. American Journal of Sociology, 66, 32–40.Google Scholar
  9. Bond, A. H. and Gasser, L. (Eds.) (1988). Readings in Distributed Artificial Intelligence. Morgan Kaufmann Publishers, Los Altos, CA.Google Scholar
  10. Bond, A. H. (1989). The cooperation of experts in engineering design. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 463–484.Google Scholar
  11. Bond, A. H. (1990). A computational model for organization of cooperating intelligent agents. Proceedings of the Conference on Office Information Systems, Cambridge, MA, pp. 21–30.Google Scholar
  12. Bonissone, P. P. (1986). Plausible reasoning: coping with uncertainty in expert systems. In Shapiro, C. S. (Ed.), Encyclopedia of Artificial Intelligence, New-York: Wiley.Google Scholar
  13. Cammarata, S., McArthur, D. and Steeb, R. (1983). Strategies of cooperation in distributed problem solving. Proceedings of the 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, pp. 767–770.Google Scholar
  14. Chaib-draa, B. (1991). Commitments and intentions in distributed AI systems: preliminary report, Report DIUL-RR-9114, Departement d'informatique, Université Laval, Québec.Google Scholar
  15. Chaib-draa, B. and Millot, P. (1990). A framework for cooperative work: an approach based on the intentionality. Artificial Intelligence in Engineering, 5 (4), 199–205.Google Scholar
  16. Chaib-draa, B. and Millot, P. (1987). Architecture pour les systems d'intelligence artificielle distribuée. Proceedings of the IEEE Compint 1987, Montreal, PQ, CND, pp. 64–69.Google Scholar
  17. Chang, E. (1987). Participant systems for cooperative work. In Huhns, M. N. (Ed.), Distributed Artificial Intelligence, Morgan Kaufmann Publishers, Los Altos, CA, pp. 311–339.Google Scholar
  18. Clinger, W. (1981). Foundations of actors semantics. Technical Report No. 633, MIT Artificial Intelligence Laboratory, Cambridge, MA.Google Scholar
  19. Cohen, P. R. and Levesque, H. (1986). Persistence, intention and commitment. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceeding of the 1986 Workshop, Morgan Kaufmann, Los Altos, CA, pp. 297–340.Google Scholar
  20. Cohen, P. R. and Levesque, H. J. (1987). Rational interaction as the basis for communication. Report No. CSLI-87–89, SRI International, Menlo Park, CA.Google Scholar
  21. Cohen, P. R. and Levesque, H. J. (1990). Intention is choice with commitment. Artificial Intelligence, 42, 213–261.Google Scholar
  22. Conry, S. E., Meyer, R. A. and Lesser, V. R. (1988). Multistage negotiation in distributed planning. In Bond, A. and Gasser, L. (Eds.), Readings In Distributed Artificial Intelligence, Morgan Kaufmann Publishers, Los Altos, CA, pp. 367–386.Google Scholar
  23. Conry, S. E., Meyer, R. A. and Pope, R. P. (1989). Mechanisms for assessing non-local impact of local decisions in distributed planning. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 245–258.Google Scholar
  24. Corkill, D. D. and Lesser, V. R. (1983). The use of meta-level control for coordination in a distributed problem solving network. Proceeding of the 8th International Joint Conference on Artificial Intelligence, Karlsruhe Germany, pp. 748–756.Google Scholar
  25. Corkill, D. D., Gallaghen, K. O. and Murray, K. E. (1986). GBB: a generic blackboard development system. Proceedings of the 5th National Conference on Artificial Intelligence, Philadelphia, PA, pp. 1008–1014.Google Scholar
  26. Cullingford, R. E. (1981). Integrating knowledge sources for computer understanding tasks. IEEE Transactions on Systems, Man, and Cybernetics, 11 (1), 52–60.Google Scholar
  27. Davis, R. (Ed.) (1980). Report on the workshop on distributed AI. Sigart Newsletter, 73, 42–52.Google Scholar
  28. Davis, R. (Ed.) (1982). Report on the second workshop on distributed AI. Sigart Newsletter, 80, 13–23.Google Scholar
  29. Davis, R. and Smith, R. G. (1983). Negotiation as a metaphor for distributed problem solving. Artificial Intelligence, 20, 63–109.Google Scholar
  30. Dawant, B. M. and Jansen, B. H. (1991). Coupling numerical and symbolic methods for signal interpretation. IEEE Transaction on Systems, Man, and Cybernetics, 21 (1), 115–133.Google Scholar
  31. Decker, K. S. (1987). Distributed problem-solving techniques: a survey. IEEE Transactions on Systems, Man, and Cybernetics, 17 (5), 729–740.Google Scholar
  32. Decker, K. S., Durfee, E. H. and Lesser, V. R. (1989). Evaluating research in cooperative distributed problem solving. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 485–519.Google Scholar
  33. Dijkstra, E. W. (1968). Cooperation sequential process. In Genuys, F. (Ed.), Programming Languages, Academic Press, New York.Google Scholar
  34. Durfee, E. H., Lesser, V. R. and Corkill, D. D. (1985). Increasing coherence in a distributed problem solving network. Proceedings of the 9th International Joint on Conference Artificial Intelligence, Los Angeles, CA, pp. 1025–1030.Google Scholar
  35. Durfee, E. H., Lesser, V. R. and Corkill, D. D. (1987). Coherent cooperation among communicating problem solvers. IEEE Transactions on Computers, 36 (11), 1275–1291.Google Scholar
  36. Durfee, E. H. (1988). Coordination of Distributed Problem Solvers, Kluwer Academic Publishers, Boston.Google Scholar
  37. Durfee, E. H., Lesser, V. R. and Corkill, D. D. (1989). Trends in cooperative distributed problem solving. IEEE Transaction on Knowledge and Data Engineering, 1 (1), 63–83.Google Scholar
  38. Durfee, E. H. and Montgomery, T. A. (1990). A hierarchical protocol for coordination multiagent behaviour. Proceedings of the 8th National Conference on Artificial Intelligence, Boston, MA, pp. 86–93.Google Scholar
  39. Durkheim, E. (1983). The Rules of Sociological Method. Free Press, New York.Google Scholar
  40. Fehling, M. (Ed.) (1983). Report on the third annual workshop on distributed artificial intelligence. Sigart Newsletter. 84, 3–12.Google Scholar
  41. Ferber, J. F. and Ghallab, M. (1988). Problématiques des univers multi-agents intelligents. Proceedings of Actes des Journess Nationales IA PRC-Greco, Toulouse, France, pp. 295–320.Google Scholar
  42. Fikes, R. E. and Nilsson, N. J. (1971). strips: a new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–208.Google Scholar
  43. Fikes, R. E., Hart, P. E. and Nilsson, N. J. (1972). Some new directions in robot problem solving. In Meltzer, B. and Mitchie, B. (Eds.), Machine Intelligence, Vol. 7, Wiley, New York, pp. 405–430.Google Scholar
  44. Galliers, J. R. (1988). A strategic framework for multi-agent dialogue. Proceedings of the 8th European Conference on Artificial Intelligence, Munich, Germany, pp. 415–420.Google Scholar
  45. Galliers, J. R. (1991). Modelling autonomous belief revision in dialogue. In Demazeau, Y. and Muller, J.-P. (Eds.), Decentralized Artificial Intelligence 2: Proceedings of the Second European Workshop on Autonomous Agents in a Multi-Agents World, Elsevier Science Pub./North Holland.Google Scholar
  46. Garret, D. W. (1986). NASA selects small business proposals. NASA News.Google Scholar
  47. Gasser, L. (1986). The integration of computing and routine work. ACM Transaction on Office Information Systems, 4 (3), 205–225.Google Scholar
  48. Gasser, L., Braganza, C. and Herman, H. (1987). mace: a flexible testbed for distributed AI research. In Huhns, M. N. (Ed.), Distributed Artificial Intelligence, Morgan Kaufmann Publishers, Los Altos, CA, pp. 119–152.Google Scholar
  49. Gasser, L., Roquette, N. F., Hill, R. W. and Lieb, J. (1989a). Representing and using organizational knowledge in distributed AI systems. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 55–78.Google Scholar
  50. Gasser, L. and Huhns, M. N. (Eds.) (1989b). Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers/Pitman (London), 520 pp.Google Scholar
  51. Gasser, L. (1991). Social conceptions of knowledge and action: DAI foundations and open systems semantics. Artificial Intelligence, 47, 107–138.Google Scholar
  52. Genesereth, M. R., Ginsberg, M. L. and Rosenschein, J. S. (1986). Cooperation without communication. Proceedings of the 5th National Conference on Artificial Intelligence, Philadelphia, PA, pp. 51–57.Google Scholar
  53. Georgeff, M. (1983). Communication and interaction in multi-agent planning. Proceedings of the 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, pp. 125–129.Google Scholar
  54. Georgeff, M. (1986). The representation of events in multiagent domains. Proceedings of the 5th National Conference on Artificial Intelligence, Philadelphia, Pennsylvania, pp. 70–75.Google Scholar
  55. Georgeff, M. (1987), Actions, process and causality. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceedings of the 1986 Workshop, Morgan Kaufmann Publishers, Los Altos, CA, pp. 99–122.Google Scholar
  56. Grosz, B. and Sidner, C. (1990). Plans for discourse. In Cohen, P. et al. (Eds.), Intentions in Communication, MIT Press, pp. 417–444.Google Scholar
  57. Halpern, J. Y. (Ed.) (1986). Theoretical Aspects of Reasoning About Knowledge: Proceedings of the 1986 Conference, Morgan Kaufmann Publishers, Los Altos, CA.Google Scholar
  58. Harmon, S. Y., Aviles, W. A. and Gage, D. W. (1986). A technique for coordinating autonomous robots. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, pp. 2029–2034.Google Scholar
  59. Hayes-Roth, B. (1985). A blackboard architecture for control. Artificial Intelligence, 26 (3), 251–321.Google Scholar
  60. Hayes-Roth, B. (1987a). A multi-processor interrupt-driven architecture for adaptative intelligent systems. Technical Report KSL-87-31, Stanford University.Google Scholar
  61. Hayes-Roth, B. (1987b). Dynamic control planning in adaptative intelligent systems. Proceedings of DARPA Knowledge-Based Planning Workshop.Google Scholar
  62. Hayes-Roth, B. (1988). Making intelligent systems adaptative. In Vanlehn, K. (Ed.), Architectures for Intelligence, Lawrence Erlbaum.Google Scholar
  63. Hayes-Roth, B., Hewett, M., Washington, R., Hewett, R. and Seiver, A. (1989). Distributing intelligence within an individual. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 385–412.Google Scholar
  64. Hayes-Roth, F. (1980). Towards a framework for distributed AI. Sigart Newsletter, 51–52.Google Scholar
  65. Hayes, P. (1973). The frame problem and related problems in artificial intelligence. In Elithorn, A. and Jones, D. (Eds.), Artificial and Human Thinking, Jossey-Bass, pp. 45–59.Google Scholar
  66. Hendrix, G. G. (1973). Modeling simultaneous actions and continuous processes. Artificial Intelligence, 4, 143–180.Google Scholar
  67. Hern, L. E. C. (1988). On distributed artificial intelligence. The Knowledge Engineering Review, 3 (1), 21–57.Google Scholar
  68. Hewitt, C. (1977). Viewing control structures on patterns of passing messages. Artificial Intelligence, 8, 323–364.Google Scholar
  69. Hewitt, C and Kornfeld, B. (1980). Message passing semantics, Sigart Newsletter, 48-48.Google Scholar
  70. Hewitt, C. and De Jong, P. (1983). Analysing the roles of descriptions and actions in open systems. Proceedings of the 3rd National Conference on Artificial Intelligence, Washington DC.Google Scholar
  71. Hewitt, C. (1986). Offices are open systems. ACM Transactions on Office Information Systems, 4 (3), 270–287.Google Scholar
  72. Hewitt, C. (1991). Open information systems semantics for distributed artificial intelligence. Artificial Intelligence, 47, 79–106.Google Scholar
  73. Hoare, C. A. R. (1978). Communicating sequential processes. Communications of the ACM, 21, 666–677.Google Scholar
  74. Huhns M. N. (Ed.) Distributed Artificial Intelligence, Morgan Kaufmann/Pitman, London, 390 pp.Google Scholar
  75. Kamel, M. and Sayed, A. (1989). An object-oriented multiple agent planning system. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 259–290.Google Scholar
  76. Katz, M. J. and Rosenschein, J. S. (1989). Plans for multiple agents. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 147–228.Google Scholar
  77. Kaultz, H. A. (1990). A circumscriptive theory of plan recognition. In Cohen, P. et al. (Eds.), Intentions in Communication, MIT Press, pp. 105–133.Google Scholar
  78. Konolige, K. and Pollack, M. E. (1989). Ascribing plans to agents: preliminary report. Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, MI, pp. 924–930.Google Scholar
  79. Kornfeld, W. A. and Hewitt, C. (1981). The scientific community metaphor. IEEE Transactions on Systems, Man and Cybernetics, 11 (1), 24–33.Google Scholar
  80. Laasri, H., Maitre, B. and Haton, J. P. (1988). Organisation, cooperation et exploitation des connaissances dan les architectures de blackboard: atome. Proceedings of the 8th International Workshop on Expert Systems and their Applications, Avignon, France, pp. 371–390.Google Scholar
  81. Laasri, H., Maitre, B. (1990). Cooperating expert problem solving in blackboard systems: atome case study. In Demazeau, Y. and Muller, J.-P. (Eds.), Decentralized Artificial Intelligence 1: Proceedings of the First European Workshop on Autonomous Agents in a Multi-Agents World, Elsevier Science Pub./North Holland.Google Scholar
  82. Lane, D. M., Chantler, M. J., Robertson, E. W. and Mc-Fadzean, A. G. (1989). A distributed problem solving architecture for knowledge based vision. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 433–462.Google Scholar
  83. Lansky, A. L. (1987). A representation of parallel activity based on events, structure and causality. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceedings of the 1986 Workshop, Morgan Kaufmann Publishers, Los Altos, CA, pp. 123–159.Google Scholar
  84. Leblanc, T. J. (1986). Shared memory versus message-passing in tightly-coupled multiprocessors: a case study. University of Rochester Computer Science Department, Rochester, NY, Tech. Report Butterfly Project Report 3.Google Scholar
  85. Lesser, V. R. and Corkill, D. D. (1981). Functionally-accurate, cooperative distributed systems. IEEE Transaction on Systems, Man and Cybernetics, 11(1), 81–96.Google Scholar
  86. Lesser, V. R., Pavlin, J. and Durfee, E. (1988). Approximate processing in real-time problem solving. AI Magazine, 9(1), 49–61.Google Scholar
  87. Levesque, H. J., Cohen, P. R. and Nunes, J. H. T. (1990). On acting together. Proceedings of the 8th National Conference on Artificial Intelligence, Boston, MA, pp. 94–99.Google Scholar
  88. Litman, D. J. and Allen, J. F. (1987). A plan recognition model for subdialogues in conversations. Cognitive Science, 11(2), 163–200.Google Scholar
  89. Litman, D. J. and Allen, J. F. (1990). Discourse processing and commensense plans. In Cohen, P. et al. (Eds.), Intentions in Communication, MIT Press, pp. 365–388.Google Scholar
  90. Lifschitz, V. (1987). On the semantics of strips. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceedings of the 1986 Workshop, Morgan Kaufmann, Los Altos, CA, pp. 1–9.Google Scholar
  91. Lizotte, M. and Moulin, B. (1989). sairvo: a planning system which implements the actem concept. Knowledge-Based Systems, 2(4), 210–218.Google Scholar
  92. Lizotte, M. and Moulin, B. (1990). A temporal planner for modelling autonomous agents. In Demazeau, Y. and Muller, J.-P. (Eds.), Decentralized Artificial Intelligence 1: Proceedings of the First European Workshop on Autonomous Agents in a Multi-Agents World, Elsevier Science Pub./North Holland.Google Scholar
  93. Lochbaum, K. E., Grosz, B. J. and Sidner, C. L. (1990). Models of plans to support communication: an initial report. Proceedings of the 8th National Conference on Artificial Intelligence, Boston, MA, pp. 485–490.Google Scholar
  94. Lozano-Perez, T. (1983). Robot-programming. Proceedings IEEE, 71(7), 821–841.Google Scholar
  95. Maines, D. (Ed.) (1984). Urban Life. Special issue on negotiated order theory.Google Scholar
  96. Malone, T. W., Fikes, R. E. and Howard, M. T. (1988). Enterprise: a market-like task scheduler for distributed computing environments. In Huberman, B. A. (Ed.), Ecology of Computation, North-Holland Publishing Company, Amsterdam.Google Scholar
  97. Malone, T. W. (1990). Organizing information processing systems: parallels between human organizations and computer systems. In Robertson, S. P., Zachary, W. and Black, J. B. (Eds.), Cognition, Computing, and Cooperation, Ablex Publishing Corporation Norwood, New Jersey.Google Scholar
  98. Mason, C. L. and Johnson, R. R. (1989). datms: a framework for distributed assumption based reasoning. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 293–317.Google Scholar
  99. McCarty, J. and Hayes, P. (1969). some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B. and Mitchie, D. (Eds.), Machine Intelligence, Vol. 4, Edinburgh University Press, pp. 463–502.Google Scholar
  100. McDermott, D. (1982). A temporal logic for reasoning about processes and plans. Cognitive Science, 6, 105–155.Google Scholar
  101. McDermott, D. (1983). Generalizing problem reduction: a logical analysis. Proceedings of the 8th International Conference on Artificial Intelligence. Karlsruhe, West Germany, pp. 302–308.Google Scholar
  102. McDermott, D. (1985). Reasoning about plans. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commensense Worlds, Ablex Publishing, Norwood, pp. 269–317.Google Scholar
  103. Mead, G. H. (1934). Mind, Self, and Society. University of Chicago Press, Chicago, IL.Google Scholar
  104. Moore, R. C. (1980). Reasoning about knowledge and action. Technical Note 191, Artificial Intelligence Center, SRI International, Menlo Park, CA.Google Scholar
  105. Nii, H. P. and Aiello, N. (1979). age: a knowledge-based program for building knowledge-based programs. Proceedings of the 6th International Joint Conference on Artificial Intelligence, Tokyo, Japan, pp. 645–655.Google Scholar
  106. Nii, H. P. (1986a). Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architectures (Part I). Al Magazine, 38–53.Google Scholar
  107. Nii, H. P. (1986b). Blackboard systems: blackboard application systems, blackboard systems from a knowledge engineering perspective (Part II). AI Magazine, 82–106.Google Scholar
  108. Nii, H. P., Aiello, N. and Rice, J. (1989). Experiments on cage and poligon: measuring the performance of parallel blackboard systems. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2. Morgan Kaufmann Publishers, Los Altos, CA, pp. 319–383.Google Scholar
  109. Nilsson, N. J. (1980). Two heads are better than one. Sigart Newsletter, 43-43.Google Scholar
  110. O'Hare, G. M. P. (1986). New-directions in distributed artificial intelligence. Proceedings of the 2nd International Expert Systems Conference. London, England, pp. 137–148.Google Scholar
  111. Parunak, H. V. D. (1987). Manufacturing experience with the contract-net. In Huhns, M. N. (Ed.), Distributed Artificial Intelligence. Morgan Kaufmann Publishers. Los Altos, CA, pp. 285–310.Google Scholar
  112. Pednault, E. F. D. (1987). Formulating multiagent dynamic world problems in the classical planning framework. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceedings of the 1986 Workshop. Morgan Kaufmann Publishers, Los Altos, CA, pp. 47–82.Google Scholar
  113. Pollack, M. E. (1987). A model of plan inference that distinguishes between the beliefs of actors and observers. In Georgeff, M. P. and Lansky, A. L. (Eds.), Proceedings of the 1986 Workshop on the Reasoning About Actions and Plans, Morgan Kaufmann Publishers, Los Altos, CA, pp. 279–295.Google Scholar
  114. Pollack, M. E. (1990). Plans as complex mental attitudes. In Cohen, P. et al. (Eds.), Intentions in Communication, MIT Press, pp. 77–103.Google Scholar
  115. Pratt, V. R. (1976). Semantical considerations on Floyd-Hoare logic. Proceedings of the 17th FOCS, IEEE, 109–121.Google Scholar
  116. Rice, J. (1986). Poligon: a system for parallel problem solving. Technical Report KSL-86-19, Knowledge Systems Laboratory, Computer Science Department, Standard University.Google Scholar
  117. Robertson, S. P., Zachary, W., and Black, J. B. (Eds.) (1990). Cognition, Computing, and Cooperation. Ablex Publishing Corporation, Norwood, New Jersey.Google Scholar
  118. Rosenschein, J. S. and Genesereth, M. R. (1985). Deals among rational agents. Proceedings of 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp. 91–99.Google Scholar
  119. Rosenschein, J. S. (1986). Rational Interaction: Cooperating Among Intelligent Agents. Ph.D. Thesis, Computer Science Department, Stanford University.Google Scholar
  120. Rosenschein, J. S. and Breese, J. S. (1989). Communication-free interactions among rational agents. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence. Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 99–118.Google Scholar
  121. Sathi, A. (1988). Cooperation Through Constraint Directed Negotiation: Study of Resource Reallocation Problems, Ph.D. Thesis, Graduate School of Industrial Administration, Carnegie-Mellon University.Google Scholar
  122. Sathi, A. and Fox, M. S. (1989). Constraint-directed negotiation of resource reallocations. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 129–162.Google Scholar
  123. Schelling, T. C. (1960). The Strategy of Conflict. Harvard University Press, Cambridge, MA.Google Scholar
  124. Schmidt, C. F., Sridharan, N. S. and Goodson, J. L. (1978). The plan recognition problem: an intersection of artificial intelligence and psychology. Artificial Intelligence. 10(1), 45–83.Google Scholar
  125. Shaw, M. J. and Whinston, A. B. (1989). Applying distributed artificial intelligence to flexible manufacturing. In Moody, X. X. and Nof, Q. Q. (Eds.), Advanced Information Technologies for Industrial Material Flow System, Springer-Verlag, NY, pp. 81–93.Google Scholar
  126. Shoham, Y. (1989a). Time for action: on the relation between time, knowledge and action. Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI, pp. 954–959.Google Scholar
  127. Shoham, Y. (1989b). Beliefs as defeasible knowledge. Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI, pp. 1168–1173.Google Scholar
  128. Sidner, C. L. (1985). Plan parsing for intended response recognition in discourse. Computational Intelligence, 1(1), 1–10.Google Scholar
  129. Silverman, B. G. (1986). Facility advisor: a distributed expert system testbed for spacecraft ground facilities. Expert Systems in Government Symposium, Proceedings of the IEEE-Cs Order No. 738, pp. 23–32.Google Scholar
  130. Silverman, B. G. (1987a). Distributed inference and fusion algorithms for real-time control of satellite ground systems. IEEE Transaction on Systems, Man, and Cybernetics, 17(2), 230–239.Google Scholar
  131. Simkol, J., Wenig, G. and Silverman, B. G. (1986). jams: a computer aided electronic warfare vulnerability assessment (CA-EWVA) technique. Symposium Aerospace Applications of AI.Google Scholar
  132. Smith, R. G. (1980). The contract-net protocol: high-level communication and control in a distributed problem solver. IEEE Transactions on Computers, 29(12), 1104–1113.Google Scholar
  133. Smith, R. G. and Davis, R. (1981). Framework for cooperation in distributed problem solving. IEEE Transactions on System, Man, and Cybernetics, 11(1), 61–70.Google Scholar
  134. Smith, R. G. (Ed.) (1985). Report on the 1984 workshop on distributed AI. AI Magazine, 234–243.Google Scholar
  135. Star, S. L. (1989). The structure of iII-structured solutions: boundary objects and heterogenous distributed problem solving. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 37–54.Google Scholar
  136. Strauss, A. (1978). Negotiations: Varieties, Processes, Contexts and Social Order, Jossey-Bass, San Francisco, CA.Google Scholar
  137. Stuart, C. (1985). An implementation of a multi-agent plan synchronizer. Proceedings of the 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp. 1031–1033.Google Scholar
  138. Stuart, C. (1987). Branching regular expressions and multi-agent plans. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning About Actions and Plans: Proceedings of the 1986 Workshop, Morgan Kaufmann Publishers, Los Altos, CA, pp. 161–187.Google Scholar
  139. Suchman, L. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication, Cambridge University Press, New York.Google Scholar
  140. Sycara, K. (1987). Resolving Adversial Conflicts: An Approach Integrating Case-Based and Analytic Methods. Ph.D. Thesis, School of Information and Computer Science, Georgia Institute of Technology, Atlanta, GA.Google Scholar
  141. Sycara, K. (1988a). Resolving goal conflicts via negotiation. Proceedings of the 7th National Conference on Artificial Intelligence, St Paul, MI, pp. 245–250.Google Scholar
  142. Sycara, K. (1988b). Utility theory in conflict resolution. Annals of Operations Research, 12, 65–84.Google Scholar
  143. Sycara, K. R. (1989a). Argumentation: planning other agent's plans, Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI, pp. 517–523.Google Scholar
  144. Sycara, K. R. (1989b). Multiagent compromise via negotiation. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 119–137.Google Scholar
  145. Tubbs, S. L. (Ed.) (1984). A System Approach to Small Group Interaction, 2nd edition, Addison Wesley, Reading, MA.Google Scholar
  146. Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433–460.Google Scholar
  147. Werner, E. (1989). Cooperating agents: a unified theory of communication and social structure. In Gasser, L. and Huhns, M. N. (Eds.), Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Los Altos, CA, pp. 3–36.Google Scholar
  148. Werner, E. (1991). An unified view of information, intention and ability. In Demazeau, Y. and Muller, J.-P. (Eds.), Decentralized Artificial Intelligence 2: Proceedings of the Second European Workshop on Autonomous Agents in a Multi-Agents World, Elsevier Science Pub./North Holland.Google Scholar
  149. Wittgenstein, L. (1953). Philosophical Investigations, Oxford, Basil Blackwell.Google Scholar
  150. Zlotkin, G. and Rosenschein, J. S. (1990). Negotiation and conflict resolution in non-cooperative domains. Proceedings of the 8th National Conference on Artificial Intelligence, Boston, MA, pp. 100–105.Google Scholar
  151. Zlotkin, G. and Rosenschein, J. S. (1989). Negotiation and task sharing among autonomous agents in cooperative domains. Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI, pp. 912–917.Google Scholar

Copyright information

© Kluwer Academic Publishers 1992

Authors and Affiliations

  • B. Chaib-Draa
    • 1
  • B. Moulin
    • 1
  • R. Mandiau
    • 2
  • P. Millot
    • 2
  1. 1.Département d'InformatiqueFaculté des Sciences Université LavalSainte-FoyCanada
  2. 2.Laboratoire d'Automatique Industrielle et Humaine, URA CNRS No. 1118Université de ValenciennesValenciennes CedexFrance

Personalised recommendations