SCOPUS 정보 검색 플랫폼

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 116-121

Learning graphical game models

(4) Duong, Quang a Vorobeychik, Yevgeniy b Singh, Satinder a Wellman, Michael P a

a UNIVERSITY OF MICHIGAN (United States)

b UNIVERSITY OF PENNSYLVANIA (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HEURISTIC ALGORITHMS; LEARNING ALGORITHMS; OUTSOURCING; TREES (MATHEMATICS);

BRANCH AND BOUND SEARCH; COMPACT REPRESENTATION; LIMITED OBSERVATIONS; LOCAL NEIGHBORHOODS; LOCAL SEARCH ALGORITHM; MULTI-AGENT INTERACTION; PERFORMANCE METRICS; UPPER AND LOWER BOUNDS;

LEARNING SYSTEMS;

EID: 78751691593 PISSN: 10450823 EISSN: None Source Type: Conference Proceeding
DOI: None Document Type: Conference Paper

Times cited : (20)

References (7)

1
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- Knowledge combination in graphical multiagent models
- Q. Duong, M. P. Wellman, and S. Singh. Knowledge combination in graphical multiagent models. In Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 153-160, 2008.
- (2008) Twenty-Fourth Conference on Uncertainty in Artificial Intelligence , pp. 153-160
- Duong, Q.¹ Wellman, M.P.² Singh, S.³

2
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- Learning and solving many-player games through a cluster-based representation
- S. G. Ficici, D. C. Parkes, and A. Pfeffer. Learning and solving many-player games through a cluster-based representation. In Twenth-Fourth Conference on Uncertainty in Artificial Intelligence, pages 187-195, 2008.
- (2008) Twenth-Fourth Conference on Uncertainty in Artificial Intelligence , pp. 187-195
- Ficici, S.G.¹ Parkes, D.C.² Pfeffer, A.³

3
- 34249761849
- Learning Bayesian networks: The combination of knowledge and statistical data
- D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
- (1995) Machine Learning , vol.20 , pp. 197-243
- Heckerman, D.¹ Geiger, D.² Chickering, D.M.³

4
- 0141591857
- Graphical models for game theory
- M. Kearns, M. Littman, and S. Singh. Graphical models for game theory. In Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 253-260, 2001.
- (2001) Seventeenth Conference on Uncertainty in Artificial Intelligence , pp. 253-260
- Kearns, M.¹ Littman, M.² Singh, S.³

5
- 4544335718
- Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms
- E. Nudelman, J. Wortman, Y. Shoham, and K. Leyton-Brown. Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms. In Third International Joint Conference on Autonomous Agents and Multiagent Systems, pages 880-887, 2004.
- (2004) Third International Joint Conference on Autonomous Agents and Multiagent Systems , pp. 880-887
- Nudelman, E.¹ Wortman, J.² Shoham, Y.³ Leyton-Brown, K.⁴

6
- 36348990694
- Learning graphical model structure using L1-regularization paths
- M. Schmidt, A. Niculescu-Mizil, and K. Murphy. Learning graphical model structure using L1-regularization paths. In Twenty-Second National Conference on Artificial Intelligence, pages 1278-1283, 2007.
- (2007) Twenty-Second National Conference on Artificial Intelligence , pp. 1278-1283
- Schmidt, M.¹ Niculescu-Mizil, A.² Murphy, K.³

7
- 34147115582
- Learning payoff functions in infinite games
- Y. Vorobeychik, M. P.Wellman, and S. Singh. Learning payoff functions in infinite games. Machine Learning, 67(2):145-168, 2007.
- (2007) Machine Learning , vol.67 , Issue.2 , pp. 145-168
- Vorobeychik, Y.¹ Wellman, M.P.² Singh, S.³

* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.