Flexible integration of multiple learning methods into a problem solving architecture

  • Enric Plaza
  • Josep Lluís Arcos
Extended Abstracts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 784)


One of the key issues in so-called multi-strategy learning systems is the degree of freedom and flexibility with which different learning and inference components can be combined. Most of multi-strategy systems only support fixed, tailored integration of the different modules for a specific domain of problems. We will report here our current research on the Massive Memory Architecture (MMA), an attempt to provide a uniform representation framework for inference and learning components supporting flexible, multiple combination of these components. Rather than a specific combination of learning methods, we are interested in an architecture adaptable to different domains where multiple learning strategies (combinations of learning methods) can be programmed or even learned.


  1. [1]
    Carbonell, J. G., Knoblock, C. A., Minton, S. (1991), Prodigy: An integrated architecture for planning and learning. In K. van Lehn (Ed.), Architectures for Intelligence. Lawrence Erlbaum Ass.: Hillsdale, NJ.Google Scholar
  2. [2]
    Michalski, R., (1993), Inferencetial theory of learning as a conceptual basis for multistrategy learning, Machine Learning, 11(2–3), 111–152.Google Scholar
  3. [3]
    Mitchell, T.M., Allen, J., Chalasani, P., Cheng, J., Etzioni, O., Ringuette, M., Schlimmer, J. C. Theo: a framework for self-improving systems. In K Van Lenhn (Ed.) Architectures for Intelligence. Laurence Erlbaum, 1991.Google Scholar
  4. [4]
    A. Newell (1990), Unified Theories of Cognition. Cambridge MA: Harvard University Press.Google Scholar
  5. [5]
    Plaza, E (1992), Reflection for analogy: Inference-level reflection in an architecture for analogical reasoning. Proc. IMSA'92 Workshop on Reflection and Metalevel Architectures, Tokyo, November 1992, p. 166–171.Google Scholar
  6. [6]
    Plaza, E. Arcos J. L., Reflection and Analogy in Memory-based Learning, Proc. Multistrategy Learning Workshop., 1993. p. 42–49.Google Scholar
  7. [7]
    Plaza, E., Aamodt, A., Ram, A., van de Velde, W., van Someren, M. (1993), Integrated learning architectures. In P.V. Brazdil (Ed.) Machine Learning: ECML-93., pp. 429–441. Lecture Notes in Artificial Intelligence 667, Springer-Verlag.Google Scholar
  8. [8]
    Ram, A, Cox, M T, Narayanan, S. (1992), An architecture for integrated introspective learning. Proc. ML'92 Workshop on Computational Architectures for Machine Learning and Knowledge Acquisition.Google Scholar
  9. [9]
    Russell S (1990), The Use of Knowledge in Analogy and Induction. Morgan Kaufmann.Google Scholar
  10. [10]
    Steels, L. The Components of Expertise, AI Magazine, Summer 1991.Google Scholar
  11. [10]
    Tecuci, G. (1993), Plausible justification trees: A framework for deep and dynamic integration of learning strategies. Machine Learning, 11(2–3), 237–261.Google Scholar
  12. [11]
    Wielinga, B, Schreiber, A, Breuker, J (1992), KADS: A modelling approach to knowledge engineering. Knowledge Acquisition 4(1).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Enric Plaza
    • 1
  • Josep Lluís Arcos
    • 1
  1. 1.Institut d'Investigació en Intelligència ArtificialIIIA-C.S.I.C.BlanesSpain

Personalised recommendations