Author:
Dariusz Plewczynski
Affiliation:
University of Warsaw, Poland
Keyword(s):
Mean-Field; Symbol Grounding, Categorization, Clustering, Fuzzy Logic, Artificial Intelligence; Cellular automata; Agent Based Modelling; Computational Intelligence; Semiotic Landscape, Semiotic Triad, Brainstorming, Categories
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 dynami
cs 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.
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