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Original Articles

PROGRAMMING AGENT BEHAVIOR BY LEARNING IN SIMULATION MODELS

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Pages 349-375 | Published online: 08 May 2012

REFERENCES

  • Adami , C. 1998 . Introduction to artificial life . New York , NY , USA : Springer-Verlag, Inc .
  • Adele , L. P. , L. D. Pyeatt , A. E. Howe , and A. E. Howe . 1998 . Decision tree function approximation in reinforcement learning. Technical report, In Proceedings of the third international symposium on adaptive systems: evolutionary computation and probabilistic graphical models .
  • Bernon , C. , M.-P. Gleizes , S. Peyruqueou , and G. Picard . 2003 . Adelfe: A methodology for adaptive multi-agent systems engineering . In Engineering societies in the agents world III Volume 2577 of Lecture Notes in Computer Science , eds. P. Petta , R. Tolksdorf , and F. Zambonelli , 70 – 81 . Berlin/Heidelberg : Springer .
  • Cobo , L. C. , P. Zang , C. L. Isbell , and A. Thomaz . 2011 . Automatic state abstraction from demonstration. In Proceedings of the twenty-second international joint conference on artificial intelligence (IJCAI) .
  • Collins , R. J. , and D. R. Jefferson . 1991 . Antfarm: Towards simulated evolution . In Artificial life II , 579 – 601 . Santa Fe , NM : Addison-Wesley .
  • Denzinger , J. , and M. Kordt . 2000 . Evolutionary online learning of cooperative behavior with situation-action pairs. In Proceedings of the fourth international conference on multiagent systems, 2000. 103–110 .
  • Denzinger , J. , and A. Schur . 2004 . On customizing evolutionary learning of agent behavior . In Advances in artificial intelligence, Vol. 3060 of Lecture notes in computer science , eds. A. Tawfik and S. Goodwin , 146 – 160 . Berlin/Heidelberg , Springer .
  • Grefenstette , J. 1987 . The evolution of strategies for multi-agent environments . Adaptive Behavior 1 : 65 – 90 .
  • Huysmans , J. , B. Baesens , and J. Vanthienen . 2006 . Using rule extraction to improve the comprehensibility of predictive models. Open access publications from katholieke universiteit leuven, Katholieke Universiteit Leuven .
  • Junges , R. , and F. Klügl . 2010a . Evaluation of techniques for a learning-driven modeling methodology in multiagent simulation . In Multiagent system technologies, Vol. 6251 of Lecture notes in computer science , eds. J. Dix and C. Witteveen , 185 – 196 . Berlin/Heidelberg : Springer .
  • Junges , R. , and F. Klügl . 2010b . Generating inspiration for multiagent simulation design by q-learning. In MAS&S Workshop at MALLOW 2010, Vol. 627 of CEUR Workshop Proceedings. Lyon, France: CEUR-WS.org .
  • Junges , R. , and F. Klügl . 2011a . Evolution for modeling–A genetic programming framework for sesam. In Proceedings of ECoMASS@GECCO 2011. Evolutionary computation and multi-agent systems and simulation (ECoMASS) . New York , NY : ACM .
  • Junges , R. , and F. Klügl . 2011b . Modeling agent behavior through online evolutionary and reinforcement learning. In MAS&S workshop at FedCSIS 2011, Szczecin, Poland .
  • Klügl , F. 2009 . Agent-based simulation engineering. Habilitation Thesis, University of Würzburg .
  • Lee , M. R. , and E.-K. Kang . 2006 . Learning enabled cooperative agent behavior in an evolutionary and competitive environment . Neural Computing & Applications 15 : 124 – 135 .
  • Mahadevan , S. , and J. Connell . 1992 . Automatic programming of behavior-based robots using reinforcement learning . Artificial Intelligence 55 ( 2–3 ): 311 – 365 .
  • Neruda , R. , S. Slusny , and P. Vidnerova . 2008 . Performance comparison of relational reinforcement learning and rbf neural networks for small mobile robots. In FGCNS '08: Proceedings of the 2008 second international conference on future generation communication and networking symposia, 29–32. Washington, DC, USA: IEEE Computer Society .
  • Nowe , A. , K. Verbeeck , and M. Peeters . 2006 . Learning automata as a basis for multi agent reinforcement learning . In Learning and adaption in multi-agent systems, Vol. 3898 of lecture notes in computer science , eds. K. Tuyls , P. Hoen , K. Verbeeck , and S. Sen , 71 – 85 . Berlin : Springer .
  • Quinlan , J. R. 1993 . C4.5: Programs for machine learning (morgan kaufmann series in machine learning) () , 1st ed. . San Francisco , CA : Morgan Kaufmann .
  • Sutton , R. S. , and A. G. Barto . 1998. Reinforcement learning: An introduction . Cambridge , MA : MIT Press.
  • Watkins , C. J. C. H. , and P. Dayan . 1992 . Q-learning . Machine Learning 8 ( 3 ): 279 – 292 .
  • Weiß , G. 1996 . Adaptation and learning in multi-agent systems: Some remarks and a bibliography. In IJCAI ‘95: Proceedings of the workshop on adaption and learning in multi-agent systems, 1–21. London, UK: Springer-Verlag .
  • Whiteson , S. , and P. Stone . 2006 . Sample-efficient evolutionary function approximation for reinforcement learning. In Proceedings of the twenty-first national conference on artificial intelligence. 518–23 . Boston , MA : AAAI Press .

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