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Volumn 11, Issue , 2010, Pages 1737-1769

Evolving static representations for task transfer

Author keywords

Evolutionary computation; Indirect encoding; Neuroevolution; Task transfer; Transfer learning

Indexed keywords

BIRD'S EYE VIEW; DIFFERENT DOMAINS; EVOLUTIONARY COMPUTATIONS; HIGH-DIMENSIONAL; KEEPAWAY; LEARNING TO PLAY; MACHINE-LEARNING; NEUROEVOLUTION; ROBOCUP; ROBOCUP SOCCER; STATIC REPRESENTATION; TASK TRANSFER; TRANSFER LEARNING; TWO-DIMENSIONAL MAP;

EID: 77953505969     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (54)

References (67)
  • 1
    • 65549111685 scopus 로고    scopus 로고
    • Measurement of the top quark mass with dilepton events selected using neuroevolution at CDF
    • Timo Aaltonen et al. Measurement of the top quark mass with dilepton events selected using neuroevolution at CDF. Physical Review Letters, 2009.
    • (2009) Physical Review Letters
    • Aaltonen, T.1
  • 6
    • 0031189914 scopus 로고    scopus 로고
    • Multitask learning
    • Rich Caruana. Multitask learning. In Machine Learning, pages 41-75, 1997.
    • (1997) Machine Learning , pp. 41-75
    • Caruana, R.1
  • 10
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • New York, NY, ACM Press
    • Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, New York, NY, 2008. ACM Press.
    • (2008) Proceedings of the 25th International Conference on Machine Learning
    • Collobert, R.1    Weston, J.2
  • 13
    • 56449093331 scopus 로고    scopus 로고
    • An object-oriented representation for efficient reinforcement learning
    • New York, NY, USA, ACM. ISBN 978-1-60558-205-4. doi:http://doi.acm.org/ 10.1145/1390156.1390187
    • Carlos Diuk, Andre Cohen, and Michael L. Littman. An object-oriented representation for efficient reinforcement learning. In ICML '08: Proceedings of the 25th International Conference on Machine learning, pages 240-247, New York, NY, USA, 2008. ACM. ISBN 978-1-60558-205-4. doi:http://doi.acm.org/10.1145/ 1390156.1390187.
    • (2008) ICML '08: Proceedings of the 25th International Conference on Machine Learning , pp. 240-247
    • Diuk, C.1    Cohen, A.2    Littman, M.L.3
  • 14
    • 0035312760 scopus 로고    scopus 로고
    • Relational reinforcement learning
    • April-May
    • Sao Deroski, Luc De Raedt, and Kurt Driessens. Relational reinforcement learning. Machine Learning, 43(1-2):7-52, April-May 2001.
    • (2001) Machine Learning , vol.43 , Issue.1-2 , pp. 7-52
    • Deroski, S.1    De Raedt, L.2    Driessens, K.3
  • 15
    • 34548086552 scopus 로고    scopus 로고
    • Generating large-scale neural networks through discovering geometric regularities
    • New York, NY, GECCO-2007, ACM
    • Jason Gauci and Kenneth O. Stanley. Generating large-scale neural networks through discovering geometric regularities. In Proceedings of the Genetic and Evolutionary Computation Conference, page 8, New York, NY, 2007. GECCO-2007, ACM.
    • (2007) Proceedings of the Genetic and Evolutionary Computation Conference , pp. 8
    • Gauci, J.1    Stanley, K.O.2
  • 17
    • 77955992290 scopus 로고    scopus 로고
    • Stanley. Autonomous evolution of topographic regularities in artificial neural networks
    • Jason Gauci and Kenneth O. Stanley. Autonomous evolution of topographic regularities in artificial neural networks. Neural Computation, page 38, 2010.
    • (2010) Neural Computation , pp. 38
    • Gauci, J.1    Kenneth, O.2
  • 19
    • 0012329219 scopus 로고    scopus 로고
    • A comparison between cellular encoding and direct encoding for genetic neural networks
    • In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors Cambridge, MA, MIT Press
    • Frederic Gruau, Darrell Whitley, and Larry Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 81-89, Cambridge, MA, 1996. MIT Press.
    • (1996) Genetic Programming 1996: Proceedings of the First Annual Conference , pp. 81-89
    • Gruau, F.1    Whitley, D.2    Pyeatt, L.3
  • 20
    • 0011714199 scopus 로고
    • PhD thesis, School of Cognitive and Computing Sciences, University of Sussex, Sussex
    • Inman Harvey. The Artificial Evolution of Adaptive Behavior. PhD thesis, School of Cognitive and Computing Sciences, University of Sussex, Sussex, 1993.
    • (1993) The Artificial Evolution of Adaptive Behavior
    • Harvey, I.1
  • 21
    • 0036985854 scopus 로고    scopus 로고
    • Pollack. Creating high-level components with a generative representation for body-brain evolution
    • Gregory S. Hornby and Jordan B. Pollack. Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3), 2002.
    • (2002) Artificial Life , vol.8 , pp. 3
    • Hornby, G.S.1    Jordan, B.2
  • 22
    • 38149038551 scopus 로고    scopus 로고
    • RoboCup 2006: Robot soccer world cup X
    • chapter Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study. Springer Berlin / Heidelberg
    • Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone. RoboCup 2006: Robot Soccer World Cup X, volume 4434 of Lecture Notes in Computer Science, chapter Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study. Springer Berlin / Heidelberg, 2007.
    • (2007) Lecture Notes in Computer Science , vol.4434
    • Kalyanakrishnan, S.1    Liu, Y.2    Stone, P.3
  • 24
    • 12244304892 scopus 로고    scopus 로고
    • Non-communicative multi-robot coordination in dynamic environments
    • February
    • Jelle R. Kok, Matthijs T. J. Spaan, and Nikos Vlassis. Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50(2-3):99-114, February 2005.
    • (2005) Robotics and Autonomous Systems , vol.50 , Issue.2-3 , pp. 99-114
    • Kok, J.R.1    Spaan, M.T.J.2    Vlassis, N.3
  • 25
    • 0033714616 scopus 로고    scopus 로고
    • The spatial semantic heirarchy
    • Benjamin Kuipers. The spatial semantic heirarchy. Artifical Intelligence, 119:191-233, 2000.
    • (2000) Artifical Intelligence , vol.119 , pp. 191-233
    • Kuipers, B.1
  • 27
    • 0014265483 scopus 로고
    • Mathematical models for cellular interaction in development parts I and II
    • and 300-315
    • Aristid Lindenmayer. Mathematical models for cellular interaction in development parts I and II. Journal of Theoretical Biology, 18:280-299 and 300-315, 1968.
    • (1968) Journal of Theoretical Biology , vol.18 , pp. 280-299
    • Lindenmayer, A.1
  • 28
    • 64749093402 scopus 로고    scopus 로고
    • Agents, bodies, constraints, dynamics, and evolution
    • Spring
    • Alan Mackworth. Agents, bodies, constraints, dynamics, and evolution. AI Magazine, 30(1):7-28, Spring 2009.
    • (2009) AI Magazine , vol.30 , Issue.1 , pp. 7-28
    • Mackworth, A.1
  • 29
    • 0032885792 scopus 로고    scopus 로고
    • Increasing genomic complexity by gene duplication and the origin of vertebrates
    • Andrew P. Martin. Increasing genomic complexity by gene duplication and the origin of vertebrates. The American Naturalist, 154(2): 111-128, 1999.
    • (1999) The American Naturalist , vol.154 , Issue.2 , pp. 111-128
    • Martin, A.P.1
  • 30
    • 47349130092 scopus 로고    scopus 로고
    • Performance evaluation of EANT in the robocup keepaway benchmark
    • Washington, DC, USA, IEEE Computer Society. ISBN 0-7695-3069-9. doi: http://dx.doi.org/10.1109/ICMLA.2007.80. URL
    • Jan FH. Metzen, Mark Edgington, Yohannes Kassahun, and Frank Kirchner. Performance evaluation of EANT in the robocup keepaway benchmark. In ICMLA '07: Proceedings of the Sixth International Conference on Machine Learning and Applications, pages 342-347, Washington, DC, USA, 2007. IEEE Computer Society. ISBN 0-7695-3069-9. doi: http://dx.doi.org/10.1109/ICMLA.2007.80. URL http://dx.doi.org/10.1109/ICMLA.2007.80.
    • (2007) ICMLA '07: Proceedings of the Sixth International Conference on Machine Learning and Applications , pp. 342-347
    • Metzen, J.F.H.1    Edgington, M.2    Kassahun, Y.3    Kirchner, F.4
  • 32
    • 70350633261 scopus 로고    scopus 로고
    • Technical Report HKUST-CS08-08 Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, November
    • Sinno Pan and Qiang Yang. A survey on transfer learning. Technical Report HKUST-CS08-08, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, November 2008.
    • (2008) A Survey on Transfer Learning
    • Pan, S.1    Yang, Q.2
  • 34
    • 38049100972 scopus 로고    scopus 로고
    • Transfer learning in reinforcement learning problems through partial policy recycling
    • Berlin, Germany, Springer-Verlag
    • Jan Ramon, Kurt Driessens, and Tom Croonenborghs. Transfer learning in reinforcement learning problems through partial policy recycling. In Proceedings of the 18th European Conference on Machine Learning, pages 699-707, Berlin, Germany, 2007. Springer-Verlag.
    • (2007) Proceedings of the 18th European Conference on Machine Learning , pp. 699-707
    • Ramon, J.1    Driessens, K.2    Croonenborghs, T.3
  • 35
    • 0003636089 scopus 로고
    • On-line Q-learning using connectionist systems
    • Cambridge University Engineering Department
    • Gavin A. Rummery and Mahesan Niranjan. On-line Q-learning using connectionist systems. CUED/F-INFENG/TR 166, Cambridge University Engineering Department, 1994.
    • (1994) CUED/F-INFENG/TR , vol.166
    • Rummery, G.A.1    Niranjan, M.2
  • 36
    • 0029326731 scopus 로고
    • Evolving neural control systems
    • June
    • Natarajan Saravanan and David B. Fogel. Evolving neural control systems. IEEE Expert, pages 23-27, June 1995.
    • (1995) IEEE Expert , pp. 23-27
    • Saravanan, N.1    Fogel, D.B.2
  • 39
    • 0036594106 scopus 로고    scopus 로고
    • Evolving neural networks through augmenting topologies
    • Kenneth O. Stanley and Risto Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10:99-127, 2002.
    • (2002) Evolutionary Computation , vol.10 , pp. 99-127
    • Stanley, K.O.1    Miikkulainen, R.2
  • 42
    • 67650188046 scopus 로고    scopus 로고
    • A hypercube-based indirect encoding for evolving large-scale neural networks
    • Kenneth O. Stanley, David B. D'Ambrosio, and Jason Gauci. A hypercube-based indirect encoding for evolving large-scale neural networks. Artificial Life, 15(2), 2009.
    • (2009) Artificial Life , vol.15 , pp. 2
    • Stanley, K.O.1    D'Ambrosio, D.B.2    Gauci, J.3
  • 43
    • 77953510013 scopus 로고    scopus 로고
    • Multiagent matching algorithms with and without coach
    • Special issue on Decision Support Systems. Guest editors: Fatima C. C Dargam and Pascale Zarate
    • Frieder Stolzenburg, Jan Murray, and Karsten Sturm. Multiagent matching algorithms with and without coach. Journal of Decision Systems, 15(2-3):215-240, 2006. Special issue on Decision Support Systems. Guest editors: Fatima C. C. Dargam and Pascale Zarate.
    • (2006) Journal of Decision Systems , vol.15 , Issue.2-3 , pp. 215-240
    • Stolzenburg, F.1    Murray, J.2    Sturm, K.3
  • 44
    • 0013528313 scopus 로고    scopus 로고
    • Sutton. Scaling reinforcement learning to robocup soccer
    • New York, NY, June ICML 2001, ACM
    • Peter Stone and Richard S. Sutton. Scaling reinforcement learning to robocup soccer. In The Eighteenth International Conference on Machine Learning, pages 537-544, New York, NY, June 2001. ICML 2001, ACM.
    • (2001) The Eighteenth International Conference on Machine Learning , pp. 537-544
    • Stone, P.1    Richard, S.2
  • 45
    • 84867452958 scopus 로고    scopus 로고
    • Reinforcement learning in 3 vs. 2 keepaway
    • In Peter Stone, T. Balch, and G. Kraetszchmar, editors Springer Verlag, Berlin
    • Peter Stone, Richard S. Sutton, and Satinder Singh. Reinforcement learning in 3 vs. 2 keepaway. In Peter Stone, T. Balch, and G. Kraetszchmar, editors, Robocup-2000: Robot soccer world cup IV, pages 249-258. Springer Verlag, Berlin, 2001.
    • (2001) Robocup-2000: Robot Soccer World Cup IV , pp. 249-258
    • Stone, P.1    Sutton, R.S.2    Singh, S.3
  • 46
    • 27544506565 scopus 로고    scopus 로고
    • Reinforcement learning for RoboCupsoccer keepaway
    • Peter Stone, Richard S. Sutton, and Gregory Kuhlmann. Reinforcement learning for RoboCupsoccer keepaway. Adaptive Behavior, 13(3):165-188, 2005.
    • (2005) Adaptive Behavior , vol.13 , Issue.3 , pp. 165-188
    • Stone, P.1    Sutton, R.S.2    Kuhlmann, G.3
  • 47
    • 37249034293 scopus 로고    scopus 로고
    • Keepaway soccer: From machine learning testbed to benchmark
    • Springer Verlag
    • Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, and Yaxin Liu. Keepaway soccer: From machine learning testbed to benchmark. In RoboCup-2005: Robot Soccer World Cup IX, pages 93-105. Springer Verlag, 2006.
    • (2006) RoboCup-2005: Robot Soccer World Cup IX , pp. 93-105
    • Stone, P.1    Kuhlmann, G.2    Taylor, M.E.3    Liu, Y.4
  • 49
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • Richard S. Sutton. Learning to predict by the methods of temporal differences. In Machine Learning, pages 9-44, 1988.
    • (1988) Machine Learning , pp. 9-44
    • Sutton, R.S.1
  • 50
    • 85156221438 scopus 로고    scopus 로고
    • Generalization in reinforcement learning: Successful examples using sparse coarse coding
    • MIT Press
    • Richard S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, pages 1038-1044. MIT Press, 1996.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 1038-1044
    • Sutton, R.S.1
  • 51
    • 55149107173 scopus 로고    scopus 로고
    • Learning to solve problems from exercises
    • Prasad Tadepalli. Learning to solve problems from exercises. Computational Intelligence, 4(24): 257-291, 2008.
    • (2008) Computational Intelligence , vol.4 , Issue.24 , pp. 257-291
    • Tadepalli, P.1
  • 56
    • 34848816477 scopus 로고    scopus 로고
    • Transfer learning vis inter-task mappings for temporal difference learning
    • September
    • Matthew E. Taylor, Peter Stone, and Yaxin Liu. Transfer learning vis inter-task mappings for temporal difference learning. Journal of Machine Learning Research, 1(8):2125-2167, September 2007a.
    • (2007) Journal of Machine Learning Research , vol.1 , Issue.8 , pp. 2125-2167
    • Taylor, M.E.1    Stone, P.2    Liu, Y.3
  • 57
    • 34848896604 scopus 로고    scopus 로고
    • Transfer via intertask mappings in policy search reinforcement learningn
    • New York, NY, May AAMAS-2007, ACM Press
    • Matthew E. Taylor, Shimone Whiteson, and Peter Stone. Transfer via intertask mappings in policy search reinforcement learningn. In The Autonomous Agents and Multi-Agent Systems Conference, New York, NY, May 2007b. AAMAS-2007, ACM Press.
    • (2007) The Autonomous Agents and Multi-Agent Systems Conference
    • Taylor, M.E.1    Whiteson, S.2    Stone, P.3
  • 58
    • 0001046225 scopus 로고
    • Practical issues in temproal difference learning
    • May
    • Gerald Tesauro. Practical issues in temproal difference learning. Machine Learning, 8(3-4):257-277, May 1992.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 257-277
    • Tesauro, G.1
  • 61
    • 77952176586 scopus 로고    scopus 로고
    • Advice taking and transfer learning: Naturally inspired extensions to reinforcement learning
    • Washington, DC, AAAI Press
    • Lisa Torrey, TrevorWalker, Richard Maclin, and Jude Shavlik. Advice taking and transfer learning: Naturally inspired extensions to reinforcement learning. In AAAI Fall Symposium on Naturally Inspired AI, Washington, DC, 2008b. AAAI Press.
    • (2008) AAAI Fall Symposium on Naturally Inspired AI
    • Torrey, L.1    Walker, T.2    Maclin, R.3    Shavlik, J.4
  • 64
    • 77953531123 scopus 로고    scopus 로고
    • Improving reinforcement learning function approximators via neuroevolution
    • New York, NY, USA, ACM. ISBN 1-59593-093-0. doi: http://doi.acm.org/10. 1145/1082473.1082794
    • Shimon Whiteson. Improving reinforcement learning function approximators via neuroevolution. In AAMAS '05: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1386-1386, New York, NY, USA, 2005. ACM. ISBN 1-59593-093-0. doi: http://doi.acm.org/10.1145/ 1082473.1082794.
    • (2005) AAMAS '05: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems , pp. 1386-1386
    • Whiteson, S.1
  • 66
    • 21244469857 scopus 로고    scopus 로고
    • Evolving soccer keepaway players through task decomposition
    • Shimon Whiteson, Nate Kohl, Risto Miikkulainen, and Peter Stone. Evolving soccer keepaway players through task decomposition. Mach. Learn., 59(1-2):5-30, 2005.
    • (2005) Mach. Learn. , vol.59 , Issue.1-2 , pp. 5-30
    • Whiteson, S.1    Kohl, N.2    Miikkulainen, R.3    Stone, P.4
  • 67
    • 0033362601 scopus 로고    scopus 로고
    • Evolving artificial neural networks
    • Xin Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, 1999.
    • (1999) Proceedings of the IEEE , vol.87 , Issue.9 , pp. 1423-1447
    • Yao, X.1


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