Cooperative learning using advice exchange

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2636, Issue , 2003, Pages 33-48

  Nunes, Luís ^a,b   Oliveira, Eugénio ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b INSTITUTO UNIVERSITÁRIO DE LISBOA ISCTE IUL   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; INTELLIGENT AGENTS; LEARNING SYSTEMS; SUPERVISED LEARNING; TRAFFIC CONTROL;

COOPERATIVE LEARNING; EXCHANGE MECHANISM; LEARNING PERFORMANCE; LEARNING PROCESS; LEARNING TECHNIQUES; MUTUAL INTERACTION; TRAFFIC SIMULATIONS; TRAFFIC-CONTROL SIMULATION;

MULTI AGENT SYSTEMS;

EID: 4544316833     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44826-8     Document Type: Conference Paper

References (39)

1. M. Tan. Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents. Proc. of the Tenth International Conference on Machine Learning, Amherst, MA, 330-337, 1993
2. R. S. Sutton and A. G. Barto. A Temporal-Difference Model of Classical Conditioning. Tech Report GTE Labs. TR 87-509.2, 1987
3. S. D. Whitehead. A complexity Analisys of Cooperative Mechanisms in Reinforcement Learning. Proc. of the 9th National Conference on Artificial Inteligence (AAAI-91), 607-613, 1991
4. L.-J. Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning 8: 293-321, Kluwer Academic publishers, 1992
5. C. J. C. H. Watkins, P. D. Dayan. Technical note: Q-learning. Machine Learning 8, 3: 279-292, Kluwer Academic publishers, 1992
6. S. D. Whitehead, D. H. Ballard. A study of cooperative mechanisms for faster reinforcement learning. TR 365, Computer Science Department, University of Rochester, 1991
7. M. J. Mataric. Using Communication to Reduce Locality in Distributed Multi-agent learning. Technical Report CS-96-190, Brandeis University, Dept. of Computer Science, 1996
8. C. Baroglio. Teaching by shaping. Proc. of ICML-95. Workshop on Learning by Induction vs. Learning by Demonstration, Tahoe City, CA, USA, 1995
9. J. A. Clouse. Learning from an automated training agent. Gerhard Weiß and Sandip Sen, editors, Adaptation and Learning in Multiagent Systems, Springer Verlag, Berlin, 1996
10. R. I. Brafman, M. Tennenholtz. On partially controlled multi-agent systems. Journal of Artificial Intelligence Research, 4: 477-507, 1996
11. B. Price, C. Boutilier. Implicit imitation in Multiagent Reinforcement Learning. Proc. of the Sixteenth International Conference on Machine Learning, pp. 325-334. Bled, SI, 1999
12. H. R. Berenji, D. Vengerov. Advantages of Cooperation Between Reinforcement Learning Agents in Difficult Stochastic Problems. Proc. Of the Ninth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '00), 2000
13. C. Claus, C. Boutilier. The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems. Proc. of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 746-752, July 1998
14. S. Kapetanakis, D. Kudenko. Reinforcement learning of coordination in cooperative multiagent systems. Proc. of the Eighteenth National Conference on Artificial Intelligence, (AAAI02), 326-331, American Association for Artificial Intelligence 2002
15. R. Maclin, J. Shavlik. Creating advicetaking reinforcement learners. Machine Learning 22: 251-281, 1997
16. M. J. Mataric. Learning in behaviour-based multi-robot systems: Policies, models and other agents. Journal of Cognitive Systems Research 2: 81-93, Elsvier, 2001
17. O. C. Jenkins, M. J. Mataric, S. Weber. Primitive-based movement classification for humanoid imitation. Proc. of the First International Conference on Humanoid Robotics (IEEE-RAS), Cambridge, MA, MIT, 2000
18. M. Nicoluescu, M. J. Mataric. Learning and interacting in human-robot domains. K. Dau-tenhahn (Ed.), IEEE Transactions on systems, Man Cybernetics, special issue on Socially Intelligent Agents - The Human In The Loop, 2001
19. M. J. Mataric. Sensory-motor primitives as a basis for imitation: Linking perception to action and biology to robotics. C. Nehaniv & K. Dautenhahn (Eds.), Imitation in animals and artifacts, MIT Press, 2001
20. F. J. Provost, D. N. Hennessy. Scaling Up: Distributed Machine Learning with Cooperation. Proc. of the Thirteenth National Conference on Artificial Intelligence, 1996
21. J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975
22. J. R. Koza. Genetic programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge MA, 1992
23. D. E. Rumelhart, G. E. Hinton, R. J. Wlliams. Learning internal representations by error propagation. Parallel Distributed Processing: Exploration in the Microstructure of Cognition, vol. 1: Foundations, 318-362, Cambridge MA: MIT Press, 1986
24. R. Salustowicz. A Genetic Algorithm for the Topological Optimization of Neural Networks. PhD Thesis, Tech. Univ. Berlin, 1995
25. X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423-1447, 1999
26. A.P. Topchy, O.A. Lebedko, V.V. Miagkikh. Fast learning in multilayered neural networks by means of hybrid evolutionary and gradient algorithms. Proc. of the International Conference on Evolutionary Computation and Its Applications, Moscow, 1996
27. K. W. C. Ku, M. W. Mak. Exploring the effects of Lamarckian and Baldwinian learning in evolving recurrent neural networks. Proc. of the IEEE International Conference on Evolutionary Computation, 617-621, 1997.
28. W. Erhard, T. Fink, M. M. Gutzmann, C. Rahn, A. Doering, M. Galicki, The Improvement and Comparison of different Algorithms for Optimizing Neural Networks on the MasPar {MP}-2. Neural Computation {NC}'98, ICSC Academic Press, Ed.M. Heiss, 617-623, 1998
29. P.A. Castillo, J. González, J.J. Merelo, V. Rivas, G. Romero, A. Prieto. SA-Prop: Optimization of Multilayer Perceptron Parameters using Simulated Annealing. Proc. of IWANN99, 1999
30. T. Hogg, C. P. Williams. Solving the Really Hard problems with Cooperative Search. Proc. of the Eleventh National Conference on Artificial Intelligence (AAAI-93), 231-236, 1993
31. C. Goldman, J. Rosenschein. Mutually supervised learning in multi-agent systems. Proc. of the IJCAI-95 Workshop on Adaptation and Learning in Multi-Agent Systems, Montreal, CA., August 1995
32. T. Thorpe. Vehicle Traffic Light Control Using SARSA. Masters Thesis, Department of Computer Science, Colorado State University, 1997
33. E. Brockfeld, R. Barlovic, A. Schadschneider, M. Schreckenberg. Optimizing Traffic Lights in a Cellular Automaton Model for City Traffic. Physical Review E 64, 2001
34. L. Nunes, E. Oliveira. On Learning By Exchanging advice. Symposium on Adaptive Agents and Multi-Agent Systems (AISB/AAMAS-II), Imperial College, London, April 2002
35. S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi. Optimization by simulated Annealing. Science, Vol. 220: 671-680, May 1983
36. M. Glickman, K. Sycara. Evolution of Goal-Directed Behavior Using Limited Information in a Complex Environment. Proc. of the Genetic and Evolutionary Computation Conference (GECCO-99), July 1999
37. R. S. Sutton. Integrated architectures for learning planning and reacting based on approximating dynamic programming. Proc. of the Seventh International Conference on Machine Learning, 216-224, Morgan-Kaufman.
38. K. Nagel, M Shreckenberg. A Cellular Automaton Model for Freeway Traffic. J. Phisique I, 2(12): 2221-2229, 1992
39. S. Sen, A. Biswas, S. Debnath. Believing others: Pros and Cons. Proc. of the Fourth International Conference on Multiagent Systems, 279-286, 2000