ABSTRACT
One of the key issues in designing appropriate and effective learning environments is understanding how learners advance and what factors contribute to their progress. This holds true for both human and machine learning environments. In Artificial Intelligence, there is a long tradition of studying human skill acquisition in order to design intelligent agents that learn. Using insight gained from analyzing co-evolutionary machine learners, we have been experimenting with human learning environments by simulating the interactions in a classroom. Here we detail our classroom model, formulated as an electronic institution. We describe the types of interactions that can occur between agents bearing one of two roles -- TEACHER or STUDENT - and define a dialogic framework and a performative structure for these agents. We share the results of simulation experiments, demonstrating how particular sets of interaction rules can correspond to certain styles of human teaching and learning.
- P. J. Angeline and J. B. Pollack. Competitive environments evolve better solutions for complex tasks. In S. Forrest, editor, Genetic Algorithms: Proceedings of the Fifth International Conference (GA93), 1993.]] Google ScholarDigital Library
- J. L. Arcos, M. Esteva, P. Noriega, J. A. Rodriguez-Aguilar, and C. Sierra. Environment engineering for multiagent systems. Preprint submitted to Elsevier Science, 2004.]]Google Scholar
- R. Axelrod. The Evolution of Cooperation. Basic Books, 1984.]]Google Scholar
- K. Binmore. Fun and games: A text on game theory. D. C. Heath and Company, Lexington, MA, USA, 1992.]]Google Scholar
- A. Cypher. Eager: Programming repetitive tasks by example. In Proceedings of CHI'91, 1991.]] Google ScholarDigital Library
- C. R. Dyer. http://www.cs.wisc.edu/~dyer/cs540/notes/learning.html, 2003.]]Google Scholar
- M. Esteva, D. de la Cruz, and C. Sierra. ISLANDER: an electronic institutions editor. In First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2002), pages 1045--1052, Bologna, Italy, 2002.]] Google ScholarDigital Library
- M. Esteva, J. A. Rodriguez, C. Sierra, P. Garcia, and J. L. Arcos. On the formal specifications of electronic institutions. Agent-mediated Electronic commerce (The European AgentLink Perspective), LNAI 1991, pages 126--147, 2001.]] Google ScholarDigital Library
- C. Fellbaum, editor. WordNet: An Electronic Lexical Database. Bradford Books, MIT Press, 1998.]]Google Scholar
- P. Funes, E. Sklar, H. Juillé, and J. B. Pollack. Animal-animat coevolution: Using the animal population as fitness function. In From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, 1998.]] Google ScholarDigital Library
- H. Gardner. Frames of Mind: The Theory of Multiple Intelligences. BasicBooks, 1983.]]Google Scholar
- W. D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In L. et al., editor, Proceedings of ALIFE-2, pages 313--324. Addison Wesley, 1992.]] Google ScholarDigital Library
- J. H. Holland. Adaption in Natural and Artificial Systems. University of Michigan Press, 1975.]] Google ScholarDigital Library
- M. A. Mark and J. E. Greer. Evaluation methodologies for intelligent tutoring systems. Journal of Artificial Intelligence and Education, 4:129--153, 1993.]]Google Scholar
- http://ccl.northwestern.edu/netlogo/.]]Google Scholar
- S. Papert. Situating constructionism. Constructionism, 1991.]]Google Scholar
- J. B. Pollack and A. D. Blair. Co-evolution in the successful learning of backgammon strategy. Machine Learning, 32:225--240, 1998.]] Google ScholarDigital Library
- http://repast.sourceforge.net.]]Google Scholar
- E. Sklar. CEL: A Framework for Enabling an Internet Learning Community. PhD thesis, Department of Computer Science, Brandeis University, 2000.]] Google ScholarDigital Library
- E. Sklar, A. D. Blair, and J. B. Pollack. Co-evolutionary learning: Machines and humans schooling together. In Workshop on Current Trends and Applications of Artificial Intelligence in Education: 4th World Congress on Expert Systems, 1998.]]Google Scholar
- E. Sklar, M. Davies, and M. S. T. Co. SimEd: Simulating Education as a MultiAgent System. In Proceedings of the Third International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-2004), pages 998--1005, 2004.]] Google ScholarDigital Library
- E. Sklar and S. Parsons. Towards the Application of Argumentation-based Dialogues for Education. In Proceedings of the Third International Conference of Autonomous Agents and Multi Agent Systems (AAMAS), pages 1420--1421, 2004.]] Google ScholarDigital Library
- V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.]] Google ScholarDigital Library
- L. R. Weaver and J. A. Carroll. Merrill education's link to foundations website. http://cwx.prenhall.com/bookbind/pubbooks/foundations-cluster/, 2000--2001.]]Google Scholar
Index Terms
- Multiagent simulation of learning environments
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