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Guiding Inference Through Relational Reinforcement Learning

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Inductive Logic Programming (ILP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3625))

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Abstract

Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research.

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References

  1. Doyle, J.: A truth maintenance system. Artificial Intelligence 12, 231–272 (1979)

    Article  MathSciNet  Google Scholar 

  2. Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 19, 17–37 (1982)

    Article  Google Scholar 

  3. Zelle, J.M., Mooney, R.J.: Combining FOIL and EBG to speed-up logic programs. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, pp. 1106–1111. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  4. Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proceedings of the ICML 2004 workshop on Relational Reinforcement Learning, Banff, Canada (2004)

    Google Scholar 

  5. Dzeroski, S., Raedt, L.D., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7–52 (2001)

    Article  MATH  Google Scholar 

  6. Choi, D., Kaufman, M., Langley, P., Nejati, N., Shapiro, D.: An architecture for persistent reactive behavior. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 988–995. ACM Press, New York (2004)

    Google Scholar 

  7. Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13, 227–303 (2000)

    MATH  MathSciNet  Google Scholar 

  8. Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1997)

    MATH  Google Scholar 

  9. Genesereth, M.R., Ginsberg, M.L.: Logic programming. Communications of the ACM 28, 933–941 (1985)

    Article  MathSciNet  Google Scholar 

  10. Simon, H.A.: Administrative behavior, 2nd edn. Free Press, New York (1965)

    Google Scholar 

  11. Horvitz, E.: Reasoning about beliefs and actions under computational resource constraints. Journal on Uncertainty in Artificial Intelligence 3, 301–324 (1989)

    Google Scholar 

  12. Russell, S., Wefald, E.: Principles of metareasoning. In: Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  13. Russell, S., Wefald, E.H.: Do the Right Thing: Studies in Limited Rationality. MIT Press, Cambridge (1991)

    Google Scholar 

  14. Minton, S.: Quantitative results concerning the utility of explanation-based learning. In: Proceedings of the Seventh National Conference on Artificial Intelligence, Saint Paul, MN, pp. 564–569. AAAI Press, Menlo Park (1988)

    Google Scholar 

  15. Cohen, W.W., Singer, Y.: A simple, fast, and effective rule learner. In: Proceedings of the Forteenth National Conference on Artificial Intelligence, pp. 335–342 (1999)

    Google Scholar 

  16. Guestrin, C., Koller, D., Gearhart, C., Kanodia, N.: Generalizing plans to new environments in relational MDPs. In: Proceedings of International Joint Conference on Artificial Intelligence, Acapulco, Mexico (2003)

    Google Scholar 

  17. Dietterich, T.G., Flann, N.S.: Explanation-based learning and reinforcement learning: A unified view. Machine Learning 28, 169–210 (1997)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA, 94305, USA

    Nima Asgharbeygi, Negin Nejati, Pat Langley & Sachiyo Arai

Authors
  1. Nima Asgharbeygi
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  2. Negin Nejati
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  3. Pat Langley
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  4. Sachiyo Arai
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Editor information

Editors and Affiliations

  1. Institut für Informatik I12, Technische Universität München, Boltzmannstr. 3, D-85748, Garching b. München, Germany

    Stefan Kramer

  2. Department of Computer Science, University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

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© 2005 Springer-Verlag Berlin Heidelberg

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Asgharbeygi, N., Nejati, N., Langley, P., Arai, S. (2005). Guiding Inference Through Relational Reinforcement Learning. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_2

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  • DOI: https://doi.org/10.1007/11536314_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28177-1

  • Online ISBN: 978-3-540-31851-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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