Skip to main content
SpringerLink
Log in

The Independent Choice Logic and Beyond

  • Chapter
Book cover Probabilistic Inductive Logic Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4911))

Abstract

The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

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

  • Allen, D., Darwiche, A.: New advances in inference by recursive conditioning. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), pp. 2–10. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  • Andre, D., Russell, S.: State abstraction for programmable reinforcement learning agents. In: Proc. AAAI 2002 (2002)

    Google Scholar 

  • Apt, K.R., Bezem, M.: Acyclic programs. New Generation Computing 9(3-4), 335–363 (1991)

    Google Scholar 

  • Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. In: Proceedings of LPNMR7, pp. 21–33 (2004)

    Google Scholar 

  • Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Horvitz, E., Jensen, F. (eds.) UAI 1996, Portland, OR, pp. 115–123 (1996)

    Google Scholar 

  • Brown, J.S., Burton, R.R.: Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science 2, 155–191 (1978)

    Article  Google Scholar 

  • Buntine, W.L.: Operations for learning with graphical models. Journal of Artificial Intelligence Research 2, 159–225 (1994)

    Google Scholar 

  • Chang, C.L., Lee, R.C.T.: Symbolic Logical and Mechanical Theorem Proving. Academic Press, New York (1973)

    Google Scholar 

  • Chavira, M., Darwiche, A., Jaeger, M.: Compiling relational bayesian networks for exact inference. International Journal of Approximate Reasoning (IJAR) 42, 4–20 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  • Chickering, D.M., Heckerman, D., Meek, C.: A Bayesian approach to learning Bayesian networks with local structure. In: UAI 1997, pp. 80–89 (1997)

    Google Scholar 

  • Clark, K.L.: Negation as failure. In: Gallaire, H., Minker, J. (eds.) Logic and Databases, pp. 293–322. Plenum Press, New York (1978)

    Google Scholar 

  • Cobba, B.R., Shenoy, P.P.: Inference in hybrid bayesian networks with mixtures of truncated exponentials. International Journal of Approximate Reasoning 41(3), 257–286 (2006)

    Article  MathSciNet  Google Scholar 

  • Cozman, F., Krotkov, E.: Truncated gaussians as tolerance sets. Technical Report CMU-RI-TR-94-35, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (September 1994)

    Google Scholar 

  • Darwiche, A.: Recursive conditioning. Artificial Intelligence 126(1-2), 5–41 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  • de Salvo Braz, R., Amir, E., Roth, D.: Lifted first-order probabilistic inference. In: IJCAI 2005, Edinburgh (2005), http://www.cs.uiuc.edu/~eyal/papers/fopl-res-ijcai05.pdf

  • Díez, F.J., Galán, S.F.: Efficient computation for the noisy max. International Journal of Intelligent Systems (to appear, 2002)

    Google Scholar 

  • Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: UAI 1996, pp. 252–262 (1996), http://www2.sis.pitt.edu/~dsl/UAI/UAI96/Friedman1.UAI96.html

  • Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Proceedings of the Fifth Logic Programming Symposium, Cambridge, MA, pp. 1070–1080 (1988)

    Google Scholar 

  • Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 307–337. Springer, Heidelberg (2001)

    Google Scholar 

  • Heckerman, D.: A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, March 1995. URL Revised (November 1996), http://www.research.microsoft.com/research/dtg/heckerma/heckerma.html

  • Heckerman, D., Meek, C., Koller, D.: Probabilistic models for relational data. Technical Report MSR-TR-2004-30, Microsoft Research (March 2004)

    Google Scholar 

  • Horsch, M., Poole, D.: A dynamic approach to probabilistic inference using Bayesian networks. In: Proc. Sixth Conference on Uncertainty in AI, Boston, July 1990, pp. 155–161 (1990)

    Google Scholar 

  • Kersting, K., De Raedt, L.: Bayesian logic programming: Theory and tool. In: Getoor, L., Taskar, B. (eds.) An Introduction to Statistical Relational Learning, MIT Press, Cambridge (2007)

    Google Scholar 

  • Laskey, K.B., da Costa, P.G.C.: Of klingons and starships: Bayesian logic for the 23rd century. In: Uncertainty in Artificial Intelligence: Proceedings of the Twenty-First Conference (2005)

    Google Scholar 

  • Lifschitz, V.: Answer set programming and plan generation. Artificial Intelligence 138(1–2), 39–54 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  • Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Symbolic Computation Series. Springer, Berlin (1987)

    MATH  Google Scholar 

  • Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D.L., Kolobov, A.: BLOG: Probabilistic models with unknown objects. In: IJCAI 2005, Edinburgh (2005)

    Google Scholar 

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

    Google Scholar 

  • Muggleton, S.: Inverse entailment and Progol. New Generation Computing 13(3-4), 245–286 (1995)

    Google Scholar 

  • Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19(20), 629–679 (1994)

    Article  MathSciNet  Google Scholar 

  • Neumann, J.V., Morgenstern, O.: Theory of Games and Economic Behavior, 3rd edn. Princeton University Press, Princeton (1953)

    MATH  Google Scholar 

  • Nilsson, N.J.: Logic and artificial intelligence. Artificial Intelligence 47, 31–56 (1991)

    Article  MathSciNet  Google Scholar 

  • Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching. In: NIPS, vol. 15 (2003)

    Google Scholar 

  • Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  • Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)

    Google Scholar 

  • Pfeffer, A.: IBAL: A probabilistic rational programming language. In: IJCAI 2001 (2001), http://www.eecs.harvard.edu/~avi/Papers/ibal.ijcai01.ps

  • Poole, D.: Logical generative models for probabilistic reasoning about existence, roles and identity. In: 22nd AAAI Conference on AI (AAAI 2007) (2007)

    Google Scholar 

  • Poole, D.: Learning, Bayesian probability, graphical models, and abduction. In: Flach, P., Kakas, A. (eds.) Abduction and Induction: Essays on their relation and integration, Kluwer, Dordrecht (2000a)

    Google Scholar 

  • Poole, D.: First-order probabilistic inference. In: Proc. Eighteenth International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico, pp. 985–991 (2003)

    Google Scholar 

  • Poole, D.: Explanation and prediction: An architecture for default and abductive reasoning. Computational Intelligence 5(2), 97–110 (1989)

    Article  MathSciNet  Google Scholar 

  • Poole, D.: A methodology for using a default and abductive reasoning system. International Journal of Intelligent Systems 5(5), 521–548 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  • Poole, D.: Representing diagnostic knowledge for probabilistic Horn abduction. In: IJCAI 1991, Sydney, pp. 1129–1135 (August 1991a)

    Google Scholar 

  • Poole, D.: Representing Bayesian networks within probabilistic Horn abduction. In: UAI 1991, Los Angeles, July 1991, pp. 271–278 (1991b)

    Google Scholar 

  • Poole, D.: Logic programming, abduction and probability: A top-down anytime algorithm for computing prior and posterior probabilities. New Generation Computing 11(3–4), 377–400 (1993a)

    MATH  Google Scholar 

  • Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64(1), 81–129 (1993b)

    Article  MATH  Google Scholar 

  • Poole, D.: Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks. Artificial Intelligence 88, 69–100 (1996)

    Article  MATH  Google Scholar 

  • Poole, D.: Probabilistic partial evaluation: Exploiting rule structure in probabilistic inference. In: IJCAI 1997, Nagoya, Japan, pp. 1284–1291 (1997a), http://www.cs.ubc.ca/spider/poole/abstracts/pro-pa.html

  • Poole, D.: The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence 94, 7–56 (special issue on economic principles of multi-agent systems) (1997b), http://www.cs.ubc.ca/spider/poole/abstracts/icl.html

  • Poole, D.: Abducing through negation as failure: stable models in the Independent Choice Logic. Journal of Logic Programming 44(1–3), 5–35 (2000), http://www.cs.ubc.ca/spider/poole/abstracts/abnaf.html

    Article  MATH  MathSciNet  Google Scholar 

  • Poole, D., Zhang, N.L.: Exploiting contextual independence in probabilistic inference. Journal of Artificial Intelligence Research 18, 263–313 (2003)

    MATH  MathSciNet  Google Scholar 

  • Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence: A Logical Approach. Oxford University Press, New York (1998)

    MATH  Google Scholar 

  • Quinlan, J.R., Cameron-Jones, R.M.: Induction of logic programs: FOIL and related systems. New Generation Computing 13(3-4), 287–312 (1995)

    Article  Google Scholar 

  • Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)

    Article  Google Scholar 

  • Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research (JAIR) 15, 391–454 (2001)

    MATH  MathSciNet  Google Scholar 

  • Savage, L.J.: The Foundation of Statistics, 2nd edn. Dover, New York (1972)

    Google Scholar 

  • Shanahan, M.: Prediction is deduction, but explanation is abduction. In: IJCAI-1989, Detroit, MI, pp. 1055–1060 (August 1989)

    Google Scholar 

  • Thrun, S.: Towards programming tools for robots that integrate probabilistic computation and learning. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, IEEE, Los Alamitos (2000)

    Google Scholar 

  • Zhang, N.L., Poole, D.: Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research 5, 301–328 (1996)

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, B.C., Canada, V6T 1Z4

    David Poole

Authors
  1. David Poole
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Luc De Raedt Paolo Frasconi Kristian Kersting Stephen Muggleton

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Poole, D. (2008). The Independent Choice Logic and Beyond. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds) Probabilistic Inductive Logic Programming. Lecture Notes in Computer Science(), vol 4911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78652-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78652-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78651-1

  • Online ISBN: 978-3-540-78652-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation