Amalgam-Based Reuse for Multiagent Case-Based Reasoning

  • Sergio Manzano
  • Santiago Ontañón
  • Enric Plaza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)


Different agents in a multiagent system might have different solution quality or preference criteria. Therefore, when solving problems collaboratively using CBR, case reuse must take this into account. In this paper we propose ABARC, a model for multiagent case reuse, which divides case reuse in two stages: individual reuse, where agents generate full solutions internally, and multiagent reuse, where agents engage in a deliberation process in order to reach an agreement on a final solution. Specifically, ABARC is based on the idea of amalgam, which is a way to generate solutions by combining multiple solutions into one. We illustrate ABARC in the domain of interior room design.


Utility Function Multiagent System Inductive Logic Programming Deliberation Process Subsumption Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Carpenter, B.: Typed feature structures: an extension of first-order terms. In: Saraswat, V., Ueda, K. (eds.) Proceedings of the International Symposium on Logic Programming, San Diego, pp. 187–201 (1991)Google Scholar
  2. 2.
    Cojan, J., Lieber, J.: Belief merging-based case combination. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS(LNAI), vol. 5650, pp. 105–119. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Falkenhainer, B., Forbus, K.D., Gentner, D.: The structure-mapping engine: Algorithm and examples. Artificial Intelligence 41, 1–63 (1989)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hanks, S., Weld, D.S.: A domain-independent algorithm for plan adaptation. J. Artificial Intelligence Research 2(1), 319–360 (1994)Google Scholar
  5. 5.
    Konieczny, S., Lang, J., Marquis, P.: Da2 merging operators. Artificial Intelligence 157(1-2), 49–79 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    van der Laag, P.R.J., Nienhuys-Cheng, S.H.: Completeness and properness of refinement operators in inductive logic programming. Journal of Logic Programming 34(3), 201–225 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lavrač, N., Džeroski, S.: Inductive Logic Programming. Techniques and Applications. Ellis Horwood, England (1994)zbMATHGoogle Scholar
  8. 8.
    Leake, D.B., Sooriamurthi, R.: When two case bases are better than one: Exploiting multiple case bases. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 321–335. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    McGinty, L., Smyth, B.: Collaborative case-based reasoning: Applications in personalized route planning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 362–376. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Ontañón, S., Plaza, E.: Amalgams: A formal approach for combining multiple case solutions. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 257–271. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Plaza, E., Arcos, J.L., Martín, F.: Cooperative case-based reasoning. In: Weiss, G. (ed.) ECAI 1996 Workshops. LNCS (LNAI), vol. 1221, pp. 180–201. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  12. 12.
    Plaza, E., Ontañón, S.: Ensemble case-based reasoning: Collaboration policies for multiagent cooperative cbr. In: Watson, I., Yang, Q. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 437–451. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Prassad, M.V.N., Lesser, V.R., Lander, S.: Retrieval and reasoning in distributed case bases. Tech. rep., UMass Computer Science Department (1995)Google Scholar
  14. 14.
    Ram, A., Francis, A.: Multi-plan retrieval and adaptation in an experience-based agent. In: Leake, D.B. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press, Menlo Park (1996)Google Scholar
  15. 15.
    Wilke, W., Smyth, B., Cunningham, P.: Using configuration techniques for adaptation. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400, pp. 139–168. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sergio Manzano
    • 1
    • 2
  • Santiago Ontañón
    • 1
  • Enric Plaza
    • 1
  1. 1.IIIA-CSIC, Artificial Intelligence Research Institute (Spanish Scientific Research Council)BellaterraSpain
  2. 2.Universitat Autonòma BarcelonaBellaterraSpain

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