The theory and experiments of designing cooperative intelligent systems

Decision Support Systems

Volumn 43, Issue 3, 2007, Pages 1014-1030

  Pendharkar, Parag C ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Cooperative intelligent systems; Distributed artificial intelligence; Genetic algorithms; Learning; Multi agent systems

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; GENETIC ALGORITHMS; LEARNING SYSTEMS; MULTI AGENT SYSTEMS; PROBLEM SOLVING; SCHEDULING;

BUSINESS PROBLEMS; COOPERATIVE INTELLIGENT SYSTEMS; DISTRIBUTED ARTIFICIAL INTELLIGENCE; PRODUCTION SCHEDULING;

INTELLIGENT SYSTEMS;

EID: 33947495776     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2005.05.028     Document Type: Article

References (45)

1. Atlan L., Bonnet J., and Naillon M. Learning distributed reactive strategies by genetic programming for the general job shop problem. Proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium (1993)
2. Haldun A., Bhattacharyya S., Koehler G.J., and Snowdon J.L. A review of machine learning in scheduling. IEEE Transactions on Engineering Management 41 2 (1994)
3. Bhaskar R., and Pendharkar P.C. Wisconsin division of narcotics uses multi-agent information system for drug crime investigation. Interfaces (1999) 77-87
4. Bhattacharyya S., and Koehler G.J. Learning by objectives for adaptive shop-floor learning. Decision Sciences 29 2 (1998) 347-376
5. In: Bond A.H., and Gesser L. (Eds). Readings in Distributed Artificial Intelligence (1988), Morgan Kaufmann, San Mateo, CA
6. Bui T. Building agent-based corporate information systems: an application to telemedicine. European Journal of Operational Research 122 (2000) 242-257
7. Bui H.H., Venkatesh S., and Kieronska D. Learning other agents' preferences in multi-agent negotiation using the bayesian classifier. International Journal of Cooperative Information Systems 8 4 (1999) 275-294
8. Bull L., Fogarty T.C., and Snaith M. Evolution in multi-agent systems: evolving communicating classifier systems for gait in a quadrupedal robot. International Conference on Genetic Algorithms (1995)
9. Ciancarini P. Coordinating multiagent applications on the www: a reference architecture. IEEE Transactions on Software Engineering 24 5 (1998) 362-375
10. Conte R., Miceli M., and Castelfranchi C. Limits and levels of cooperation: disentangling various types of prosocial interaction. In: Demazeau Y., and Muller (Eds). Decentralized Artificial Intelligence (1991), Elsevier Science Publishers
11. Davis R., and Smith R.G. Negotiation as a metaphor for distributed problem solving. Artificial Intelligence 20 1 (1983) 63-109
12. Dennis A.R., George J.F., Jessup L.M., Nunamaker Jr. J.F., and Vogel D.R. Information technology to support electronic meetings. MIS Quarterly 12 4 (1988) 591-624
13. Durfee E.H., Lesser V.R., and Corkill D.D. Trends in cooperative distributed problem solving. IEEE Transactions on Knowledge and Data Engineering (1989) 63-83
14. Farquhar P.H. Applications of utility theory in artificial intelligence research. In: Brown D.E., and White III C.C. (Eds). Operations Research and Artificial Intelligence: The Integration of Problem-Solving Strategies (1990), Kluwer Academic Publishers 197-214
15. Gasser L. Knowledge and action at social and organizational level, American Association Artificial Intelligence. Fall Symposium, Asilomar, CA (1991)
16. Haynes T., Wainwright R., Sen S., and Schoenefeld D. Strongly typed genetic programming in evolving cooperation strategies. International Conference on Genetic Algorithms (1995)
17. Hewitt C. Open information systems semantics for distributed artificial intelligence. Artificial Intelligence 47 (1991) 79-106
18. Jennings N. On being responsible. In: Werner E., and Demazeau Y. (Eds). Decentralized Artificial Intelligence (1992), Elsevier/North-Holland
19. Kaelbling L.P., Littman M.L., and Moore A.W. Reinforcement learning: a survey. Journal of Artificial Intelligence 4 (1996) 237-285
20. Lesser V.R. Cooperative multiagent systems: a personal view of the state of the art. IEEE Transactions on Knowledge and Data Engineering 11 1 (1999) 133-142
21. Luo Y., Davis D.N., and Liu K. A multi-agent decision support for stock trading. IEEE Network 16 1 (2002) 20-27
22. Madni A., Samet M., and Purcell. Adaptive models in information management. In: Andriole S.J. (Ed). Applications in Artificial Intelligence (1985), Petrocelli, Princeton, NJ 279-294
23. Malone T.W., and Crowston K. The interdisciplinary study of coordination. ACM Computing Surveys 26 1 (1994) 87-119
24. Malone T.W., Crowston K., Lee J., Pentland B., Dellarocas C., Wyner G., Quimby J., Osborn C.S., Bernstein A., Herman G., Klein M., and O'Donnell E. Tools for inventing organizations: toward a handbook of organizational processes. Management Science 45 3 (1999) 425-443
25. Moulin B., and Chaib-Draa B. An overview of distributed artificial intelligence. In: O'Hare, and Jennings (Eds). Foundations of Distributed Artificial Intelligence (1996), John Wiley and Sons 3-55
26. Nour M.A., and Yen D. Group, decision support systems towards a conceptual foundation. Decision Sciences 23 (1992) 55-64
27. O'Hare G.N., and Jennings N.R. Foundations of Distributed Artificial Intelligence (1996), John Wiley and Sons, New York
28. Ravindran A., Phillips D.T., and Solberg J.J. Operations Research (1987), John Wiley and Sons, New York
29. Sarkar S., and Ramaswamy M. Knowledge base decomposition to facilitate verification. Information Systems Research 11 3 (2000) 260-283
30. Schaerf A., Shoham Y., and Tennenholtz M. Adaptive load balancing: a study in multi-agent learning. Journal of Artificial Intelligence Research (1995) 475-500
31. Schneeweiss C. Distributed decision making-a unified approach. European Journal of Operational Research 150 (2003) 237-252
32. Sen S., and Durfee E.H. A formal study of distributed meeting scheduling. Group Decision and Negotiation 7 3 (1998) 265-289
33. Shaw M.J. Cooperative problem-solving and learning in multi-agent information systems. International Journal of Computational Intelligence and Organizations 1 1 (1996) 21-34
34. Shaw M.J., and Whinston A.B. Learning and adaptation in distributed artificial intelligence. In: Gasser L., and Huhns M.N. (Eds). Research Notes in Artificial Intelligence vol. 2 (1989), Pitman, London 413-429
35. R. Sikora, M.J. Shaw, 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, 1991.
36. Sikora R., and Shaw M.J. A multi-agent framework for the coordination and integration of information systems. Management Science 44 11 (1998) S65-S78
37. S.F. Smith, A learning system based on genetic adaptive algorithms, PhD Dissertation, University of Pittsburgh, 1980.
38. Sun R., and Peterson T. Multi-agent reinforcement learning: weighting and partitioning. Neural Networks 12 (1999) 727-753
39. Sycara K., Rothm S., Sadeh N., and Fox M. An investigation into distributed constraint-directed factory scheduling. Proceedings of Sixth IEEE Conference on AI Applications, Santa Barbara, CA. March (1990)
40. Vonderembse M.A., and White G.P. Operations Management Concepts, Methods and Strategies (1991), West Publishing Company
41. Weiss G. Action selection and learning in multi-agent environments. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (1992)
42. Weiss G. Learning to coordinate actions in multi-agent systems. Distributed Artificial Intelligence (1994) 311-316
43. Weiss G. Adaptation and learning in multi-agent systems: some remarks and a bibliography. Proceedings of IJCAI' 95 Workshop, Montreal, Canada (1995)
44. Wellman M.P., and Hu J. Conjectural equilibrium in multiagent learning. Machine Learning 33 (1998) 179-200
45. Yen B.P. Communication infrastructure in distributed scheduling. Computers & Industrial Engineering 42 (2002) 149-161