Skip to main content

A Unified Long-Term Memory System⋆

  • Conference paper
  • First Online:
Case-Based Reasoning Research and Development (ICCBR 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1650))

Included in the following conference series:

Abstract

Memory-based reasoning systems are a class of reasoners that derive solutions to new problems based on past experiences. Such reasoners use a long-term memory (LTM) to act as a knowledge base of these past experiences, which may be represented by such things as specific events (i.e. cases), plans, scripts, etc. This paper describes a Unified Long-Term Memory (ULTM) system, which is a dynamic, conceptual memory that was designed to be a general LTM capable of simultaneously supporting multiple intentional reasoning systems. Through a unique mixture of content-independent and domain-specific mechanisms, the ULTM is able to flexibly provide reasoners accurate and timely storage and recall of episodic memory structures. In addition, the ULTM provides support for recognizing opportunities to satisfy suspended goals, allowing reasoning systems to better cope with the unpredictability of dynamic real-world domains by helping them take advantage of unexpected events.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Francis, A.G. Jr. (1997). “Memory-Based Opportunistic Reasoning”, Ph.D. Thesis proposal, Georgia Institute of Technology.

    Google Scholar 

  2. .Hammond, K. (1990). “Case-Based Planning: A Framework for Planning from Experience”, The Journal of Cognitive Science, 14(3).

    Google Scholar 

  3. Hammond, K. (1993). “Opportunistic Memory”, The Journal of Machine Learning, 10(3).

    Google Scholar 

  4. Kellermann, K., Broetzmann, S., Lim, T.-S., and Kitao, K. (1989). “The conversation mop: Scenes in the steam of discourse”, Discourse Processes, 12(1):27–61.

    Article  Google Scholar 

  5. Kolodner, J. (1981). “Organization and Retrieval in a Conceptual Memory for Events”, Proceedings of the Seventh International Joint Conference on Artificial Intelligence.

    Google Scholar 

  6. Kolodner, J. (1989). “Selecting the Best Case for a Case-Based Reasoner”, Proceedings of the Eleventh Conference of the Cognitive Science Society.

    Google Scholar 

  7. Kolodner, J. (1993). Case-Based Reasoning, Morgan Kaufman, San Mateo.

    Google Scholar 

  8. Patalano, A., Seifert, C., and Hammond, K. (1991). “Predictive Encodings: Planning for Opportunities”, Proceedings of the Fifteenth Conference of the Cognitive Science Society.

    Google Scholar 

  9. Schank, R. (1982). Dynamic Memory, Cambridge University Press, New York.

    Google Scholar 

  10. Schank, R. and Osgood, R. (1990). “A content theory of memory indexing”, Northwestern University, Institute for Learning Sciences Technical Report no. 2.

    Google Scholar 

  11. Steele, G. (1990). Common Lisp: The Language (Second Edition), Digital Press, Bedford, MA.

    MATH  Google Scholar 

  12. Sycara, K. and Navinchandra, D. (1991). “Index Transformation and Generation for Case Retrieval”, In Proceedings of the 1991 Case-Based Reasoning Workshop (DARPA), Bareiss, E. (ed.), Morgan Kaufman, San Mateo, CA.

    Google Scholar 

  13. Turner, E. (1990). “Integrating Intention and Convention To Organize Problem Solving Dialogues”, Ph.D. Dissertation, Georgia Institute of Technology technical report GIT-ICS-90/02.

    Google Scholar 

  14. Turner, R. (1987). “Issues in the design of advisory systems: The consumer-advisor system”, in Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, Detroit, MI.

    Google Scholar 

  15. Turner, R. (1994). Adaptive Reasoning for Real-World Problems: A Schema-Based Approach, Lawrence Erlbaum Associates, Hillsdale, NJ.

    Google Scholar 

  16. Turner, R. (1995a). “Context-Sensitive, Adaptive Reasoning for Intelligent AUV Control: Orca Project Update”, In Proceedings of the 9th International Symposium on Unmanned Untethered Submersible Technology (AUV’95), Durham, New Hampshire.

    Google Scholar 

  17. Turner, R. (1995b). “Intelligent Control of Autonomous Underwater Vehicles: The Orca Project”, Roy M. Turner. In Proceedings of the 1995 IEEE Conference on Systems, Man, and Cybernetics, Vancouver, BC, Canada.

    Google Scholar 

  18. Turner, R. (1997). “Orca Documentation (for Version 2.1) ”, CDPS Research Group inhouse report, University of Maine. http://cdps.umcs.maine.edu/Docs/orca-2.0/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lawton, J.H., Turner, R.M., Turner, E.H. (1999). A Unified Long-Term Memory System⋆. In: Althoff, KD., Bergmann, R., Branting, L. (eds) Case-Based Reasoning Research and Development. ICCBR 1999. Lecture Notes in Computer Science, vol 1650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48508-2_14

Download citation

  • DOI: https://doi.org/10.1007/3-540-48508-2_14

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66237-2

  • Online ISBN: 978-3-540-48508-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics