Learning to communicate in a decentralized environment

Autonomous Agents and Multi-Agent Systems

Volumn 15, Issue 1, 2007, Pages 47-90

  Goldman, Claudia V ^a   Allen, Martin ^b   Zilberstein, Shlomo ^b  

^a UNIVERSITY OF HAIFA   (Israel)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Communication; Learning; Multi agent systems; Probabilistic methods

Indexed keywords

EID: 34249732041     PISSN: 13872532     EISSN: 15737454     Source Type: Journal    
DOI: 10.1007/s10458-006-0008-9     Document Type: Article

References (43)

1. Allen, M., Goldman, C. V., & Zilberstein, S. (2005). Learning to communicate in decentralized systems. In Proceedings of the workshop on multiagent learning, twentieth national conference on artificial intelligence (pp. 1-8). (AAAI Tech. Report WS-05-09). PA: Pittsburgh.
2. Balch, T., & Arkin, R. C. (1994). Communication in reactive multiagent robotic systems. Autonomous Robots, 1, 1-25.
3. Becker, R., Zilberstein, S., Lesser, V., & Goldman, C. V. (2004). Solving transition independent decentralized MDPs. Journal of Artificial Intelligence Research, 22, 423-455.
4. Bernstein, D. S., Givan, R., Immerman, N., & Zilberstein, S. (2002). The complexity of decentralized control of Markov decision processes. Mathematics of Operations Research, 27(4), 819-840.
5. Bhattacharjee, Y. (2004). From heofonum to heavens. Science, 303, 1326-1328.
6. Blume, A., & Sobel, J. (1995). Communication-proof equilibria in cheap-talk games. Journal of Economic Theory, 65, 359-382.
7. Bonarini, A., & Sassaroli, P. (1997). Opportunistic multimodel diagnosis with imperfect models. Information Sciences, 103(1-4), 161-185.
8. Boutilier, C. (1999). Sequential optimality and coordination in multiagent systems. In Proceedings of the sixteenth international joint conference on artificial intelligence (pp. 478-485). Sweden: Stockholm.
9. Coradeschi, S., & Saffiotti, A. (2003). An introduction to the anchoring problem. Robotics and Autonomous Systems, 43(2-3), 85-96.
10. Davidson, D. (1984). Inquiries into Truth and Interpretation. Oxford, England: Oxford University Press.
11. Decker, K. S., & Lesser, V. R. (1992). Generalizing the partial global planning algorithm. International Journal of Intelligent Cooperative Information Systems, 1(2), 319-346.
12. Doucet, A. (1998). On sequential simulation-based methods for bayesian filtering. Technical Report CUED/F-INFENG/TR.310 Department of Engineering, University of Cambridge.
13. Durfee, E. H. (1988). Coordination of Distributed Problem Solvers. Boston, MA: Kluwer Academic.
14. Durfee, E. H., & Lesser, V. R. (1988). Using partial global plans to coordinate distributed problem solvers. In A. H. Bond & L. Gasser (Eds.), Readings in Distributed Artificial Intelligence (pp. 285-293). San Mateo, CA: Morgan Kaufmann Publishers, Inc.
15. Finin, T., Labrou, Y., & Mayfield, J. (1997). KQML as an agent communication language. In J. Bradshaw (Ed.), Software Agents, Cambridge, MA: MIT Press.
16. Firoiu, L., Oates, T., & Cohen, P. (1998). Learning regular languages from positive evidence. In Proceedings of the twentieth annual meeting of the cognitive science society (pp. 350-355). WI: Madison.
17. Gmytrasiewicz, P., & Doshi, P. (2005). A framework for sequential planning in multiagent settings. Journal of AI Research, 24, 1-31.
18. Gmytrasiewicz, P. I, Summers, M., & Gopal, D. (2002). Toward automated evolution of agent communication languages. In Proceedings of the thirtyfifth hawaii international conference on system sciences, Hawaii, p. 79.
19. Goldman, C. V., Allen, M., & Zilberstein, S. (2004). Decentralized language learning through acting. In Proceedings of the third international joint conference on autonomous agents and multiagent systems (pp. 1006-1013). New York, NY, 2004.
20. Goldman, C. V., Allen, M., & Zilberstein, S. (2006). Learning to communicate in a decentralized environment. Technical Report UM-CS-2006-16, 2006. Department of Computer Science, University of Massachusetts.
21. Goldman, C. V., & Rosenschein, J. S. (1994). Emergent coordination through the use of cooperative state-changing rules. In Proceedings of the twelfth national conference on artificial intelligence (pp. 408-413). WA: Seattle.
22. Goldman, C. V., & Zilberstein, S. (2003). Optimizing information exchange in cooperative multi-agent systems. In Proceedings of the second international joint conference on autonomous agents and multi-agent systems (pp. 137-144). Melbourne, Australia.
23. Goldman, C. V., & Zilberstein, S. (2004). Decentralized control of cooperative systems: Categorization and complexity analysis. Journal of Artificial Intelligence Research, 22, 143-174.
24. Goldman, C. V., & Zilberstein, S. (2004). Goal-oriented Dec-MDPs with direct communication. Technical Report UM-CS-2004-44, Department of Computer Science, University of Massachusetts.
25. Grosz, B. J., & Kraus, S. (1996). Collaborative plans for complex group action. Artificial Intelligence, 86(2), 269-357.
26. Kaelbling, L. P. (1993). Learning in Embedded Systems. Cambridge, MA: MIT Press.
27. Komarova, N., & Niyogi, P. (2004). Optimizing the mutual intelligibility of linguistic agents in a shared world. Artificial Intelligence, 154, 1-42.
28. MacLennan, B. (1990). Evolution of communication in a population of simple machines. Knoxville, Technical Report CS90-99, Department of Computer Science, University of Tennessee.
29. NASA (1999). Mars climate orbiter failure board report. Available at: ftp://ftp.hq.nasa.gov/pub/pao/reports/1999/MCO_report.pdf
30. Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY: Wiley.
31. Putnam, H. (1975). The meaning of 'meaning'. In Mind, Language and Reality: Philosophical Papers (pp. 215-271). Vol. 2 Cambridge, England: Cambridge University Press.
32. Quine, W. V. O. (1960). Word and Object. Cambridge, MA: MIT Press.
33. Serafini, L., & Bouquet, P. (2004). Comparing formal theories of context in AI. Artificial Intelligence, 155, 41-61.
34. Sharygina, N., & Peled, D. (2001). A combined testing and verification approach for software reliability. Formal Methods for Increasing Software Productivity, LNCS, 2021, 611-628.
35. Smith, R. G. (1988). The contract net protocol: High level communication and control in a distributed problem solver. In A. H. Bond & L. Gasser (Eds.), Readings in Distributed Artificial Intelligence (pp. 357-366). San Mateo, California: Morgan Kaufmann Publishers, Inc.
36. Steels, L., & Vogt, P. (1997). Grounding adaptive language games in robotic agents. In Proceedings of the fourth european conference on artificial life (pp. 474-482). UK: Brighton.
37. Sutton, R. S., & Barto, A. G. (2000). Reinforcement Learning: An Introduction, Cambridge, MA: MIT Press.
38. Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128, 99-141.
39. Vogt, P., & Coumans, H. (2003). Investigating social interaction strategies for bootstrapping lexicon development, Journal of Artificial Societies and Social Simulation, 6(1). http://jasss.soc.surrey.ac.Uk/6/1/4. html
40. Wang, J., & Gasser, L. (2002). Mutual online concept learning for multiple agents. In Proceedings of the first international joint conference on autonomous agents and multi-agent systems (pp. 362-369). Bologna, Italy.
41. Wärneryd, K. (1993). Cheap talk, coordination, and evolutionary stability. Games and Economic Behavior, 5, 532-546.
42. Yanco, H. A. (1994). Robot communication: Issues and implementation. Master's thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.
43. Yanco, H. A., & Stein, L. A. (1993). An adaptive communication protocol for cooperating mobile robots. In J. A. Meyer, H. L. Roitblat & S. W. Wilson (Eds.), From Animals to Animats: Proceedings of the second international conference on the simulation of adaptive behavior (pp. 478-485). Cambridge, MA: MIT Press.