The Apriori Stochastic Dependency Detection (ASDD) Algorithm for Learning Stochastic Logic Rules

  • Christopher Child
  • Kostas Stathis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3259)


Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases (VLDB), pp. 12–15. Morgan Kaufmann, San Francisco (1994)Google Scholar
  2. 2.
    Boutilier, C., Dean, T., Hanks, S.: Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. Journal of Artificial Intelligence Research 11, 1–94 (1999)MathSciNetMATHGoogle Scholar
  3. 3.
    Cheung, D.W., Han, J., Ng, V., Wong, C.Y.: Maintenance of discovered association rules in large databases: An incremental updating technique. In: Proc. 1996 Int. Conf. Data Engineering, New Orleans, Louisiana, February 1996, pp. 106–114 (1996)Google Scholar
  4. 4.
    Child, C., Stathis, K.: SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report. In: Adaptive Agents and Multi-Agent Systems AAMAS-3. AISB 2003 Convention, Dimitar Kazakov. Aberystwyth, University of Wales (2003), ISBN 1 902956 31 5Google Scholar
  5. 5.
    Drescher, G.L.: Made-Up Minds, A Constructivist Approach to Artificial Intelligence. MIT Press, Cambridge (1991)MATHGoogle Scholar
  6. 6.
    Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem-solving. Artificial Intelligence 2(3-4), 189–208 (1971)CrossRefMATHGoogle Scholar
  7. 7.
    Hidber, C.: Online Association Rule Mining. SIGMOD Conf. (1999),
  8. 8.
    Hipp, J., Gunter, U., Nakhaeizadeh, G.: Algorithms for Association Rule Mining - A General Survey and Comparison. In: SIGKDD Explorations, 2000, July 2000, vol. 2(1), pp. 58–64 (2000)Google Scholar
  9. 9.
    Kaelbling, L.P., Littman, H.L., Moore, A.P.: Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)Google Scholar
  10. 10.
    Kaelbling, L.P., Oates, T., Hernandez, N., Finney, S.: Learning in Worlds with Objects,
  11. 11.
    McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence 4, 463–502 (1969)MATHGoogle Scholar
  12. 12.
    Muggleton, S.H.: Learning Stochastic Logic Programs. In: Getoor, L., Jensen, D. (eds.) Proceedings of the AAAI 2000 Workshop on Learning Statistical Models from Relational Data. AAAI, Menlo Park (2000)Google Scholar
  13. 13.
    Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Thesis, University of California, Berkeley (2002)Google Scholar
  14. 14.
    Oates, T., Schmill, M.D., Gregory, D.E., Cohen, P.R.: Detecting complex dependencies in categorical data. Chap. In: Finding Structure in Data: Artificial Intelligence and Statistics V. Springer, Heidelberg (1995)Google Scholar
  15. 15.
    Oates, T., Cohen, P.R.: Learning Planning Operators with Conditional and Probabilistic Ef-fects. In: AAAI-1996 Spring Symposium on Planning with Incomplete Information for Robot Problems. AAAI, Menlo Park (1996)Google Scholar
  16. 16.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Christopher Child
    • 1
  • Kostas Stathis
    • 1
  1. 1.Department of Computing, School of InformaticsCity UniversityLondonUK

Personalised recommendations