Case-Based Reasoning
Materials for CS.367 CS.760  & CS.767

(parts adapted with permission from Ashwin Ram's course at Georgia Tech)

Case-based reasoning (CBR) is a family of artificial intelligence techniques, based on human problem solving, in which new problems are solved by recalling and adapting the solutions of similar past problems. CBR is an active area of research and has also been adopted by many companies such as AT&T, British Airways, Cisco, Daimler, Benz, Dell, GE, Intel, Lockheed, Nokia, Siemens, and Visa, to name but a few.

What is all the excitement about? What is CBR, how does it work, how is it applied, what research is ongoing to improve it? We'll discuss a range of topics—case representation, indexing and retrieval, similarity assessment, adaptation, and learning. 

Recommended Texts:

Lecture Notes

All in Acrobat pdf format (note: not all presentations or all slides may have been used during the lectures but they're all interesting).

Background Reading

For CS767 you will be expected to read and present one of the papers below. That paper you will have to know in depth, but you are recommended to read the other papers to both better understand their presentation and to deepen your understanding of CBR.

Read How To Present A Paper for some ideas on how to approach the papers.
(The paper assignments and presentation schedule for 767 2010 are here)

  1. Case-based reasoning: Foundational issues, methodological variations, and system approaches (Agnar Aamodt & Enric Plaza, AI-Communications, 1994)
  2. Aha, D. W. (1998). The Omnipresence of Case-Based Reasoning in Science and Application. Knowledge-Based Systems, 11(5-6), 261-273. (Companion paper for an invited talk at the Seventeenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence) (pdf) student presentation
  3. DW Aha, M Molineaux, & M Ponsen (2005), Learning to win. ( pdf ) student presentation
  4. Aha, D.W. (1991). Case-based learning algorithms. Proceedings of the DARPA Case-Based Reasoning Workshop (pp. 147-158). Washington, D.C.: Morgan Kaufmann.  ( pdf )
  5. Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A review and comparative evaluation of feature weighting methods for lazy learning algorithms. Artificial Intelligence Review, 11, 273-314. Also available as Technical Report AIC-96-006: Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence. ( pdf ) student presentation
  6. Watson, I., (1999). CBR is a methodology not a technology. In, the Knowledge Based Systems Journal, Vol. 12. no.5-6, Oct. 1999, pp.303-8. Elsevier, UK.   (pdf)   student presentation    
  7. Smyth, B. & McKenna, E. (2001). Competence guided incremental footprint-based retrieval. Knowledge-Based Systems
    Volume 14, Issues 3-4, June 2001, Pages 155-161 ( pdf ) student presentation
  8. Categorizing Case-Base Maintenance: Dimensions and Directions  David B. Leake and David C. Wilson. Advances in Case-Based Reasoning: Proceedings of EWCBR-98, Springer-Verlag, Berlin. ( pdf )
  9. DB Leake & DC Wilson. Remembering Why to Remember: Performance-guided case-base maintenance. ( pdf ) student presentation
  10. B Smyth & M Keane (1995). Remembering to Forget: A competence-preserving deletion policy for case-based reasoning. In Proc. 14th Intl. Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, Canada  ( pdf ) student presentation
  11. Kolodner, Janet L &Guzdial, Mark (2000) Theory and practice of case-based learning aids. Theoretical foundations of learning environments. Jonassen, David H. (Ed); Land, Susan M. (Ed). (2000). Theoretical foundations of learning environments. (pp. 215-242). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers ( pdf )  student presentation
  12. H Munoz-Avila & MT Cox (2007). Case-Based Plan Adaptation: An analysis and review.  IEEE Intelligent Systems ( pdf ) student presentation
  13. SL Mansar & F Marir (2003). Case-based reasoning as a technique for knowledge management in business process redesign, Electronic Journal on Knowledge Management, 1(2):113-124 ( pdf )
  14. C Baccigalupo & E Plaza (2007), A case-based song scheduler for group customised radio. In ICCBR-07, Belfast, Northern Ireland ( pdf )
  15. Aha, D.W., Breslow, L.A., & Muñoz-Avila, H. (2001). Conversational case-based reasoning. Applied Intelligence, 14, 9-32.  ( pdf ) student presentation
  16. Barry Smyth , Case-Based Recommendation. P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 342–376, 2007. ( pdf ) student presentation


Library Books

Click on the link below to search the University library for books on case-based reasoning.

Search Library Database

Software Resources

The following software resources are available. Note that WEKA contains a nearest neighbour algorithm that can be used with modification (i.e., retrieved and adapted) to act as the retrieval algorithm for a CBR system.

Other On-line Resources

The following are all excellent websites that contain lots of information about CBR: 

last updated 27/02/2009