Advertisement

The cumulative consensus of cognitive agents in scenarios: A framework for evolutionary processes in semantic memory

  • Donald W. Dearholt
Special Invited Lecture
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1447)

Abstract

The Pathfinder paradigm utilizes pairwise estimates or measures of proximity to form a family of networks intended to model aspects of human semantic memory. This model supports clustering of similar concepts, higher levels of abstraction, and minimum-cost paths, thus providing a reasonably well-defined structure to the concepts within a domain. Recently, a method of modeling a dynamic phenomenon by incrementally constructing a Pathfinder network based upon counting co-occurring concepts at each sampling time has been developed, utilizing a canonical scenario. This procedure can be viewed as computing the cumulative consensus over a set of adaptive agents, in which each agent has certain responsibilities for the storage of memories. An extension of this procedure allows the adaptive agents to participate in evolutionary processes in the representation and exchange of their memories of present and past events, by the concurrent application of operations to the agents at each time step. This generates a population of agents within each scenario which differ from those in the canonical scenario. These scenarios yield cumulative consensus and Pathfinder networks which are different from those of the canonical network. Candidate operators and measures of fitness are proposed, and potential advantages and difficulties are discussed.

Keywords

co-occurrence clustering dynamic systems adaptive systems discrete models Pathfinder networks consensus agents evolutionary processes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barthelemy, J. P., and M. F. Janowitz, A formal theory of consensus. SIAM J. of Discrete Mathematics, Vol. 4, pp. 305–322, 1991.Google Scholar
  2. Barthelemy, J. P., and F. R. McMorris, The median procedure for n-trees. J. Classification, Vol. 3, pp. 329–334. 1986.Google Scholar
  3. Day, William H. E., and F. R. McMorris, Critical comparison of consensus methods for molecular sequences. Nucleic Acids Research, Vol. 20, No. 5, 1992.Google Scholar
  4. Dearholt, D., Modeling Dynamic Systems Using Co-occurrence and Associative Networks, Technical Report Number MSU-980313. Department of Computer Science, Mississippi State University, Mississippi, March, 1998.Google Scholar
  5. Dearholt, D., Modeling Dynamic Systems Using Co-occurrence and Associative Networks, Proceedings of the Tenth International Conference on Mathematical and Computer Modelling and Scientific Computing, Boston, Massachusetts, July 5, 1995.Google Scholar
  6. Dearholt, D., and R. Schvaneveldt, Properties of Pathfinder Networks. In Pathfinder Associative Networks: Studies in Knowledge Organization, Ed. R. Schvaneveldt. Ablex, 1990.Google Scholar
  7. Goldsmith, T. E., and Davenport, D. M., Assessing Structural Similarity of Graphs. In Pathfinder Associative Networks: Studies in Knowledge Organization, Ed. R. Schvaneveldt. Ablex, 1990.Google Scholar
  8. Johnson, S. C., Hierarchical clustering schemes. Psychometrika, Vol. 32, No. 3, September 1967.Google Scholar
  9. Leclerc, B., Medians and majorities in semimodular lattices. SIAM J. Discrete Mathematics, Vol. 3, pp. 266–276, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Donald W. Dearholt
    • 1
  1. 1.Mississippi State University

Personalised recommendations