Modeling of Cognitive Agents

Dariusz Plewczynski

Abstract

Agent-based Modeling (ABM), a novel computational modeling paradigm, is the modeling of phenomena as dynamical systems of interacting agents. Here, we apply this methodology for designing cognitive agents that are allowed to perform categorization process of input training items. The internal agent structure, as in presented previously brainstorming algorithm, and it is equipped with the set of basic machine learning, or clustering algorithms, which allow it for constructing prototypes of categories. Agent links prototypical categories with the subsets of training objects (so called prototypes for a category) during the simulation time. The equilibration process is described here using the mean-field theory, and fully connected cellular automata network of different categories. The individual outcomes of clustering, or machine learning algorithms are combined in order to determine the most effective partitioning of a given training data into the set of distinct categories. The dynamics of cellular automata network simulates the higher level of information integration acquired from repetitive learning trials. The final categorization of training objects is therefore consistent with equilibrium state of the complex system of linked and interacting machine learning methods, each representing different category. The proposed cognitive agent is the first autonomous cognitive system that is able to build the classification system for given perceptual information using ensemble of machine learning algorithms.

References

  1. Searle, L., Prirce, Charles Sander, in The John Hopkins Guide to Literature Theory and Criticism, M. Groden and M. Kreiswirth, Editors. 1994, John Hopkins Iniversity Press: Baltimore and London. p. 560.
  2. Wolfram, S., Cellular Automata and Complexity. 1994, New York, NY: Addison Wesley.
  3. Ajelli, M., et al., Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 10: p. 190.
  4. An, G., Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models. Methods Mol Biol, 2009. 500: p. 445- 68.
  5. Zhang, L., et al., Multiscale agent-based cancer modeling. J Math Biol, 2009. 58(4-5): p. 545-59.
  6. Guo, Z., P. M. Sloot, and J. C. Tay, A hybrid agent-based approach for modeling microbiological systems. J Theor Biol, 2008. 255(2): p. 163-75.
  7. Thorne, B. C., et al., Agent-based modeling of multicell morphogenic processes during development. Birth Defects Res C Embryo Today, 2007. 81(4): p. 344-53.
  8. Walker, D., et al., Modeling the effect of exogenous calcium on keratinocyte and HaCat cell proliferation and differentiation using an agent-based computational paradigm. Tissue Eng, 2006. 12(8): p. 2301-9.
  9. An, G., Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. J Crit Care, 2006. 21(1): p. 105-10; discussion 110- 1.
  10. Pertoldi, C. and C. Topping, Impact assessment predicted by means of genetic agent-based modeling. Crit Rev Toxicol, 2004. 34(6): p. 487-98.
  11. Walker, D. C., et al., Agent-based computational modeling of wounded epithelial cell monolayers. IEEE Trans Nanobioscience, 2004. 3(3): p. 153-63.
  12. Henrickson, L. and B. McKelvey, Foundations of "new" social science: institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling. Proc Natl Acad Sci U S A, 2002. 99 Suppl 3: p. 7288-95.
  13. Lewenstein, M., A. Nowak, and B. Latane, Statistical mechanics of social impact. Phys Rev A, 1992. 45(2): p. 763-776.
  14. Latane, B., Am. Psychol., 1981(36): p. 343.
  15. Nowak, A., J. Szamrej, and B. Latane, Psychol. Rev., 1990(97): p. 362.
  16. Kohring, G. A., Ising models of social impact: The role of cumulative advantage. Journal De Physique I, 1996. 6(2): p. 301-308.
  17. Kohring, G. A., J. Phys. I France, 1996(6): p. 301-308.
  18. Plewczynski, D., Landau theory of social clustering. Physica A, 1998. 261(3-4): p. 608- 617.
  19. Plewczynski, D., Mean-field theory of meta-learning. Journal of Statistical MechanicsTheory and Experiment, 2009: p. -.
  20. Holyst, J. A., K. Kacperski, and F. Schweitzer, Phase transitions in social impact models of opinion formation. Physica A, 2000. 285(1-2): p. 199-210.
  21. Kacperski, K. and J. A. Holyst, Phase transitions and hysteresis in a cellular automatabased model of opinion formation. Journal of Statistical Physics, 1996. 84(1-2): p. 169-189.
  22. Kacperski, K. and J. A. Holyst, Opinion formation model with strong leader and external impact: a mean field approach. Physica A, 1999. 269(2-4): p. 511-526.
  23. Kacperski, K. and J. A. Holyst, Phase transitions as a persistent feature of groups with leaders in models of opinion formation. Physica A, 2000. 287(3-4): p. 631-643.
  24. Conte, R., Agent-based modeling for understanding social intelligence. Proc Natl Acad Sci U S A, 2002. 99 Suppl 3: p. 7189-90.
  25. Liu, J., W. Zhong, and L. Jiao, A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans Syst Man Cybern B Cybern, 2006. 36(1): p. 54-73.
  26. Pedrycz, W. and P. Rai, A multifaceted perspective at data analysis: a study in collaborative intelligent agents. IEEE Trans Syst Man Cybern B Cybern, 2008. 38(4): p. 1062-72.
  27. Pedrycz, W. and P. Rai, A multifaceted perspective at data analysis: a study in collaborative intelligent agents. IEEE Trans Syst Man Cybern B Cybern, 2009. 39(4): p. 834-44.
  28. Rocha, A. F., The brain as a symbol-processing machine. Prog Neurobiol, 1997. 53(2): p. 121-98.
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Paper Citation


in Harvard Style

Plewczynski D. (2011). Modeling of Cognitive Agents . In Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - Volume 1: BILC, (ICAART 2011) ISBN 978-989-8425-42-3, pages 28-36. DOI: 10.5220/0003307200280036


in Bibtex Style

@conference{bilc11,
author={Dariusz Plewczynski},
title={Modeling of Cognitive Agents},
booktitle={Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - Volume 1: BILC, (ICAART 2011)},
year={2011},
pages={28-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003307200280036},
isbn={978-989-8425-42-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - Volume 1: BILC, (ICAART 2011)
TI - Modeling of Cognitive Agents
SN - 978-989-8425-42-3
AU - Plewczynski D.
PY - 2011
SP - 28
EP - 36
DO - 10.5220/0003307200280036