A competitive approach to game learning

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A competitive approach to game learning

Full text

Pdf (1.33 MB)

Source

Annual Workshop on Computational Learning Theory archive
Proceedings of the ninth annual conference on Computational learning theory table of contents

Desenzano del Garda, Italy

Pages: 292 - 302

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Christopher D. Rosin	Cognitive Computer Science Research Group, CSE Department, University of California, San Diego, La Jolla, CA
Richard K. Belew	Cognitive Computer Science Research Group, CSE Department, University of California, San Diego, La Jolla, CA

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 40, Citation Count: 2

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/238061.238153 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Dana Angluin, Negative Results for Equivalence Queries, Machine Learning, v.5 n.2, p.121-150, Jun. 1990 [doi>10.1023/A:1022692615781]
	2	Martin Anthony , Graham Brightwell , Dave Cohen , John Shawe-Taylor, On exact specification by examples, Proceedings of the fifth annual workshop on Computational learning theory, p.311-318, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130420]
	3	Nader H. Bshouty , Richard Cleve , Sampath Kannan , Christino Tamon, Oracles and queries that are sufficient for exact learning (extended abstract), Proceedings of the seventh annual conference on Computational learning theory, p.130-139, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181067]
	4	Chvatal, V. (1979) A greedy heuristic for the setcovering problem. Mathematics of Operations Research 4: 233-235.
	5	Dave Cliff , Geoffrey F. Miller, Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations, Proceedings of the Third European Conference on Advances in Artificial Life, p.200-218, June 04-06, 1995
	6	Susan L. Epstein, Toward an Ideal Trainer, Machine Learning, v.15 n.3, p.251-277, June 1994 [doi>10.1023/A:1022637925775]
	7	Claude-Nicolas Fiechter, Efficient reinforcement learning, Proceedings of the seventh annual conference on Computational learning theory, p.88-97, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181019]
	8	Y. Freund , M. Kearns , Y. Mansour , D. Ron , R. Rubinfeld , R. E. Schapire, Efficient algorithms for learning to play repeated games against computationally bounded adversaries, Proceedings of the 36th Annual Symposium on Foundations of Computer Science (FOCS'95), p.332, October 23-25, 1995
	9	Sally A. Goldman , Michael J. Kearns, On the complexity of teaching, Proceedings of the fourth annual workshop on Computational learning theory, p.303-314, August 05-07, 1991, Santa Cruz, California, United States
	10	Gordon, G.J. (1995) Stable Function Approximation in Dynamic Programming. Proceedings of the Twelfth Internatwnal Conference on Machine Learning.
	11	Hillis, W.D. (1991) Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure. Artificzal Life II.
	12	M R Jerrum , L G Valiant , V V Vazirani, Random generation of combinatorial structures from a uniform, Theoretical Computer Science, v.43 n.2-3, p.169-188, July 1986
	13	Joe Kilian , Kevin J. Lang , Barak A. Pearlmutter, Playing the matching-shoulders lob-pass game with logarithmic regret, Proceedings of the seventh annual conference on Computational learning theory, p.159-164, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181094]
	14	Richard J. Lipton , Neal E. Young, Simple strategies for large zero-sum games with applications to complexity theory, Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, p.734-740, May 23-25, 1994, Montreal, Quebec, Canada [doi>10.1145/195058.195447]
	15	Littman, M. (1994) Markov Games as a Framework for Multi-agent Reinforcement Learning. Machzne Learning: Proceedings of the Eleventh International Conference.
	16	Wolfgang Maass, On-line learning with an oblivious environment and the power of randomization, Proceedings of the fourth annual workshop on Computational learning theory, p.167-178, August 05-07, 1991, Santa Cruz, California, United States
	17	Papadimitriou, C. (1994) Computational Complexity.
	18	Christos H. Papadimitriou , Mihalis Yannakakis, On complexity as bounded rationality (extended abstract), Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, p.726-733, May 23-25, 1994, Montreal, Quebec, Canada [doi>10.1145/195058.195445]
	19	Pcll, B. (1993) Strategy Generation and Evaluation for Meta-Game Playing. Ph.D. Thesis, University of Cambridge.
	20	Pollack, J., A. Blair, and M. Land. (1995) Coevolution of a Backgammon Player. Artificial Life V (forthcoming).
	21	Christopher D. Rosin , Richard K. Belew, Methods for Competitive Co-Evolution: Finding Opponents Worth Beating, Proceedings of the 6th International Conference on Genetic Algorithms, p.373-381, July 15-19, 1995
	22	A. L. Samuel, Some studies in machine learning using the game of checkers, Computers & thought, MIT Press, Cambridge, MA, 1995
	23	Sehraudolph, N., P. Dayan, and T.J. Sejnowski. (1994) Temporal Difference Learning of Position Evaluation in the Game of Go. Advances in Neural Information Processing Systems 6.
	24	Sebald, A.V., and J. Schlenzig. (1994) Minimax Design of Neural Net Controllers for Highly Uncertain Plants. IEEE Transactions on Neural Networks Jan. 1994.5: 73-82.
	25	Karl Sims, Evolving 3D morphology and behavior by competition, Artificial Life, v.1 n.4, p.353-372, Summer 1994
	26	Sun, Chuen-Tsai, Ying-Hong Liao, Jing-Yi Lu, and Fu-May Zheng. (1994) Genetic Algorithm Learning in Game Playing with Multiple Coaches. Proceedings of the First IEEE Conference on Evolutionary Computation.
	27	Gerald Tesauro, Temporal difference learning and TD-Gammon, Communications of the ACM, v.38 n.3, p.58-68, March 1995 [doi>10.1145/203330.203343]
	28	Walker, S., R. Lister, and T. Downs. (1994) Temporal Difference, Non-determinism, and Noise: a Case Study on the 'Othello' Board Game. ICANN '94. Proceedings of the International Conference on Artificial Neural Networks.

CITED BY 2

	Sevan G. Ficici, Monotonic solution concepts in coevolution, Proceedings of the 2005 conference on Genetic and evolutionary computation, June 25-29, 2005, Washington DC, USA
	Wee-Chong Oon , Martin Henz, M²ICAL analyses HC-gammon, Proceedings of the 22nd national conference on Artificial intelligence, p.621-626, July 22-26, 2007, Vancouver, British Columbia, Canada