The Design of Cognitive Social Simulation Framework using Statistical Methodology in the Domain of Academic Science

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Author(s):
V. Maniraj, R. Sivakumar
Published Date:
May 05, 2013
Issue:
Volume 3, Issue 3
Page(s):
1 - 7
DOI:
10.7815/ijorcs.33.2013.062
Views:
3736
Downloads:
100

Keywords:
cognitive architecture, social simulation, reinforcement learning, function approximation
Citation:
V. Maniraj, R. Sivakumar, "The Design of Cognitive Social Simulation Framework using Statistical Methodology in the Domain of Academic Science". International Journal of Research in Computer Science, 3 (3): pp. 1-7, May 2013. doi:10.7815/ijorcs.33.2013.062 Other Formats

Abstract

Modeling the behavior of the cognitive architecture in the context of social simulation using statistical methodologies is currently a growing research area. Normally, a cognitive architecture for an intelligent agent involves artificial computational process which exemplifies theories of cognition in computer algorithms under the consideration of state space. More specifically, for such cognitive system with large state space the problem like large tables and data sparsity are faced. Hence in this paper, we have proposed a method using a value iterative approach based on Q-learning algorithm, with function approximation technique to handle the cognitive systems with large state space. From the experimental results in the application domain of academic science it has been verified that the proposed approach has better performance compared to its existing approaches.

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