Skip to main content
SpringerLink
Log in

Learning Stochastic Logical Automaton

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4012))

Included in the following conference series:

Abstract

This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The inductive generalisation algorithms consist of three parts. The first part describes the graphical generalisation of state transition models. State transition models are generalised by applying state mergers. The second part involves symbolic generalisation of logic programs which are embedded in each states. Plotkin’s LGG is used for symbolic generalisation of logic programs. The third part covers learning of parameters using statistics derived from the input sequences. The state transitions are unobservable in our settings. The probability distributions over the state transitions and actions are estimated using the EM algorithm. As an application of these algorithms, we learn chemical reaction rules from StochSim, the stochastic software simulator of biochemical reactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. McCarthy, J., Hayes, P.J.: Some Philosophical Problems from the Standpoint of Artificial Intelligence. In: Machine Intelligence, vol. 4, pp. 463–502. Edinburgh University Press (1969)

    Google Scholar 

  2. Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77 (1989)

    Google Scholar 

  3. Watanabe, H., Muggleton, S.: First-Order Stochastic Action Language. Electronic Transactions in Artificial Intelligence 7 (2002), http://www.doc.ic.ac.uk/~hw3/doc/watanabe02FirstSAL.ps

  4. H. Watanabe, S. Muggleton.: Towards Belief Propagation in Shared Logic Program, BN2003, Kyoto (2003), http://www.doc.ic.ac.uk/~hw3/doc/bn2003final2.pdf

  5. Moyle, S., Muggleton, S.H.: Learning programs in the event calculus. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS (LNAI), vol. 1297, pp. 205–212. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  6. Otero, R.: Induction of Stable Models. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 193–205. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  7. Kersting, K., Raiko, T., De Raedt, L.: Logical Hidden Markov Models (Extended Abstract). In: Ga’mez, J.A., Salmero’n, A. (eds.) Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM-02), Cuenca, Spain, November 6-8, 2002, pp. 99–107 (2002)

    Google Scholar 

  8. Kersting, K., De Raedt, L.: Logical markov decision programs and the convergence of logical TD(λ). In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS, vol. 3194, pp. 180–197. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Morton-Firth, C.J.: Stochastic simulation of cell signalling pathways Ph.D. Thesis, University of Cambridge (1998)

    Google Scholar 

  10. Dupont, P., Miclet, L., Vidal, E.: What is the search space of Regular Inference? In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS (LNAI), vol. 862, pp. 25–37. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  11. Coste, F., Fredouille, D.: What is the search space for the inference of non deterministic, unambiguous and deterministic automata? technical report INRIA RR-4907 (2003)

    Google Scholar 

  12. Plotkin, G.: Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, UK (1971)

    Google Scholar 

  13. Gold, E.M.: Complexity of automaton identification from given data. Information and Control 37(3), 302–320 (1978)

    Article  MathSciNet  Google Scholar 

  14. Angluin, D.: Negative Results for Equivalence Queries. Machine Learning 5, 121–150 (1990)

    Google Scholar 

  15. Kearns, M., Valiant, L.G.: Cryptographic limitations on learning boolean formulae and finite automata. In: Proceedings of the 21st Annual ACM Symposium on Theory of Computing, pp. 433–444. ACM Press, New York (1989)

    Google Scholar 

  16. Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75, 87–106 (1987)

    Article  MathSciNet  Google Scholar 

  17. Halpern, J.Y.: An analysis of first-order logics of probability. In: Proceedings of IJCAI-1989, 11th International Joint Conference on Artificial Intelligence, pp. 1375–1381 (1989)

    Google Scholar 

  18. Muggleton, S.H.: Stochastic logic programs. In: de Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 254–264. IOS Press, Amsterdam (1996)

    Google Scholar 

  19. Taisuke Sato.: A statistical learning method for logic programs with distribution semantics. Proc. ICLP 1995, Syounan-village, pages 715–729, 1995.

    Google Scholar 

  20. Kersting, K., Raedt, L.D.: Bayesian Logic Programs. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 138–155. Springer, Heidelberg (2000)

    MATH  Google Scholar 

  21. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 1300–1309. Morgan Kaufmann Publishers, San Francisco (1999)

    Google Scholar 

  22. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2003)

    MATH  Google Scholar 

  23. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)

    MATH  Google Scholar 

  24. Kietz, J.-U., Lübbe, M.: An efficient subsumption algorithm for inductive logic programming. In: Proc. of the 4th International Workshop on Inductive Logic Programming (ILP-1994), pp. 97–105 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK

    Hiroaki Watanabe & Stephen Muggleton

Authors
  1. Hiroaki Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Muggleton
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. ISIR, Osaka University, 8-1, Mihogaoka, 567-0047, Osaka, Ibaraki, Japan

    Takashi Washio

  2. CREST, Japan Science and Technology Agency, Japan

    Akito Sakurai

  3. Dept. of Information Media, Tokyo Denki University, room 1009, 10th floor, building 11, 2-2, Kandanishikicho, Chiyodaku, 101-8457, Tokyo, Japan

    Katsuto Nakajima

  4. National Institute of Informatics, 2-1-2 Chiyoda-ku, 101-8430, Tokyo, Japan

    Hideaki Takeda

  5. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Satoshi Tojo

  6. Department of ISEE, Kyushu University, 819-0395, Fukuoka, Japan

    Makoto Yokoo

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Watanabe, H., Muggleton, S. (2006). Learning Stochastic Logical Automaton. In: Washio, T., Sakurai, A., Nakajima, K., Takeda, H., Tojo, S., Yokoo, M. (eds) New Frontiers in Artificial Intelligence. JSAI 2005. Lecture Notes in Computer Science(), vol 4012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780496_23

Download citation

  • DOI: https://doi.org/10.1007/11780496_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35470-3

  • Online ISBN: 978-3-540-35471-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation