Cooperative memory structures and commonsense knowledge for planning

  • P. V. S. R. Bhanu Prasad
  • Deepak Khemani
Posters (Extended Abstracts)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1323)

Abstract

Here we present a memory-based planning system in which the memory is divided into three components namely the memory of skeletons, the memory of properties, and the memory of secondary objects. The planning and domain knowledge is distributed over these components which interact with each other in producing a plan. A skeleton is organized using a packaging hierarchy. An abstraction hierarchy is used in organizing the other two memories. A plan is generated in a hierarchical fashion by unfolding a suitable skeleton with the aid of the other two memories. The culinary domain has been taken up for system's implementation. The system utilizes some commonsense knowledge of the domain to adapt known plans to user requirements. This knowledge is represented in the form of rules.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Bareiter, C.; Scardamalia, M. 1993. Surpassing Ourselves: An Inquiry into the Nature and Implications of Expertise. Open Court, Chicago.Google Scholar
  2. [2]
    Bhanu Prasad, P.V.S.R. 1997. Planning With Cooperative Memory Structures. Ph.D. Thesis, Department of Computer Science and Engineering, Indian Institute of Technology, Madras.Google Scholar
  3. [3]
    Davis, E. 1990. Representations of Commonsense Knowledge. Morgan Kaufman Publishers, San Mateo, CA.Google Scholar
  4. [4]
    Ericsson, L.; Smith, J. (editors). 1991. Prospects and Limits of the Empirical Study of Expertise: An Introduction. In Toward a General Theory of Expertise, Cambridge, NY.Google Scholar
  5. [5]
    Golshani, F. 1990. Rule-Based Expert Systems, Chap. 2 of Knowledge Engineering: Fundamentals. H. Adeli (editor), McGraw-Hill, NY.Google Scholar
  6. [6]
    Hammond, K.J. 1989. Case-Based Planning: Viewing planning as a memory task. Academic Press, Inc, NY.Google Scholar
  7. [7]
    Hinrichs, T.R.; Kolodner, J.L. 1991. The Roles of Adaptation on Case-Based Design. In Proceedings of AAAI.Google Scholar
  8. [8]
    Khemani, D.; Prasad, P.V.S.R.B. 1995a. A Memory-Based Hierarchical Planner, Case-Based Reasoning Research and Development, M. Veloso and A. Aamodt (editors.), Lecture notes in Artificial Intelligence, Springer-Verlag, 1010, Berlin.Google Scholar
  9. [9]
    Khemani, D.; Bhanu Prasad, P.V.S.R. 1995b. A Hierarchical Memory-Based Planner, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Canada.Google Scholar
  10. [10]
    Kolodner, J. 1993. Case-Based Reasoning. Morgan Kaufmann, San Mateo, CA.Google Scholar
  11. [11]
    Leake, D.B. (editor). 1996. Case-Based Reasoning: Experiences, Lessons, and Future Directions. The MIT Press, MA.Google Scholar
  12. [12]
    Lesgold, A. et al. 1988. Expertise in a Complex Skill. In The Nature of Expertise, M. Chi et al. (editors), Lawrence Erlbaum, NJ.Google Scholar
  13. [13]
    McCarthy, J. 1968. Programs With Commonsense. In Semantic Information Processing, M. Minsky (editor), MIT Press, Cambridge, MA.Google Scholar
  14. [14]
    Riesbeck, C.K.; and Schank, R. C. 1989. Inside Case-Based Reasoning. Lawrence Erlbaum Associates, NJ.Google Scholar
  15. [15]
    Schank, R.C. 1982. Dynamic Memory: A Theory of Learning in Computers and People. Cambridge University Press.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • P. V. S. R. Bhanu Prasad
    • 1
  • Deepak Khemani
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology, MadrasMadrasIndia

Personalised recommendations