Skip to main content
SpringerLink
Log in

A Learning-Based Algorithm Selection Meta-Reasoner for the Real-Time MPE Problem

  • Conference paper
AI 2004: Advances in Artificial Intelligence (AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3339))

Included in the following conference series:

Abstract

The algorithm selection problem aims to select the best algorithm for an input problem instance according to some characteristics of the instance. This paper presents a learning-based inductive approach to build a predictive algorithm selection system from empirical algorithm performance data of the Most Probable Explanation(MPE) problem. The learned model can serve as an algorithm selection meta-reasoner for the real-time MPE problem. Experimental results show that the learned algorithm selection models can help integrate multiple MPE algorithms to gain a better overall performance of reasoning.

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 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Abdelbar, A.M., Hedetniemi, S.M.: Approximating MAPs for belief networks in NP-hard and other theorems. Artificial Intelligence 102, 21–38 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  2. Breese, J.S., Horvitz, E.: Ideal reformulation of belief networks. In: UAI 1990, pp. 129–144 (1990)

    Google Scholar 

  3. Fung, R., Chang, K.C.: Weighting and integrating evidence for stochastic simulation in Bayesian networks. In: UAI 1989, pp. 209–219 (1989)

    Google Scholar 

  4. Glover, F., Laguna, M.: Tabu search. Kluwer Academic Publishers, Boston (1997)

    MATH  Google Scholar 

  5. Gomes, C.P., Selman, B.: Algorithm portfolio design: theory vs. practice. In: UAI 1997, pp. 190–197 (1997)

    Google Scholar 

  6. Guo, H.: Algorithm selection for sorting and probabilistic inference: a machine learning-based approach. PhD thesis, Kansas State University (2003)

    Google Scholar 

  7. Horvitz, E.: Computation and action under bounded resources. PhD thesis, Stanford University (1990)

    Google Scholar 

  8. Horvitz, E., Ruan, Y., Kautz, H., Selman, B., Chickering, D.M.: A Bayesian approach to tackling hard computational problems. In: UAI 2001, pp. 235–244 (2001)

    Google Scholar 

  9. Ide, J.S., Cozman, F.G.: Random generation of Bayesian networks. In: Brazlian Symposium on Artificial Intelligence, Pernambuco Brazil (2002)

    Google Scholar 

  10. Jitnah, N., Nicholson, A.E.: Belief network algorithms: A study of performance based on domain characterization. In: Antoniou, G., Truszczyński, M. (eds.) PRICAI-WS 1996. LNCS, vol. 1359, pp. 169–188. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  11. Johnson, D.: A theoretician’s guide to the experimental analysis of algorithms. In: Goldwasser, M.H., Johnson, D.S., McGeoch, C.C. (eds.) Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, pp. 215–250 (2002)

    Google Scholar 

  12. Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems (with discussion). J. Royal Statist. Soc. Series B 50, 157–224 (1988)

    MATH  MathSciNet  Google Scholar 

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

    Google Scholar 

  14. Rice, J.R.: The algorithm selection problem. In: Zelkowitz, M.V. (ed.) Advances in computers, vol. 15, pp. 65–118 (1976)

    Google Scholar 

  15. Shimony, S.E.: Finding MAPs for belief networks is NP-hard. Artificial Intelligence 68, 399–410 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  16. Shimony, S.E., Domshlak, C.: Complexity of probabilistic reasoning in directed-path singly connected Bayes networks. Artificial Intelligence 151, 213–225 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  17. Santos, E., Shimony, S.E., Williams, E.: On a distributed anytime architecture for probabilistic reasoning. Technique Report AFIT/EN/TR94-06. Department of Electrical and Computer Engineering, Air Force Institute of Technology (1995)

    Google Scholar 

  18. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  19. Zilberstein, S.: Operational rationality through compilation of anytime algorithms. PhD Thesis. University of California at Berkeley (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Hong Kong University of Science and Technology,  

    Haipeng Guo

  2. Department of Computing and Information Sciences, Kansas State University,  

    William H. Hsu

Authors
  1. Haipeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William H. Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Monash University, VIC 3800, Australia

    Geoffrey I. Webb

  2. Science, Engineering and Technology Portfolio, Royal Melbourne Institute of Technology, VIC 3001, Melbourne, Australia

    Xinghuo Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guo, H., Hsu, W.H. (2004). A Learning-Based Algorithm Selection Meta-Reasoner for the Real-Time MPE Problem. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30549-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24059-4

  • Online ISBN: 978-3-540-30549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation