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COMPLEX CONCEPT LATTICES FOR SIMULATING HUMAN PREDICTION IN SPORT

ARANDA-CORRAL Gonzalo A , BORREGO-D´IAZ Joaqu´?n ,GAL´AN-P´AEZ Juan   

  1. 1.Universidad de Huelva. Department of Information Technology; Crta. Palos de La Frontera s/n. 21819 Palos de La Frontera. Spain; 2. Universidad de Sevilla. Department of Computer Science and Artificial Intelligence; Avda. Reina Mercedes s/n. 41012 Sevilla. Spain.
  • Received:2012-06-18 Online:2013-02-25 Published:2013-07-02
  • Supported by:

    This work is supported by the TIN2009-09492 project of the Spanish Ministry of Science and Innovation, and Excellence project TIC-6064 of Junta de Andaluc´?a co-financed by FEDER funds.

ARANDA-CORRAL Gonzalo A , BORREGO-D′IAZ Joaqu′?n ,GAL′AN-P′AEZ Juan. COMPLEX CONCEPT LATTICES FOR SIMULATING HUMAN PREDICTION IN SPORT[J]. Journal of Systems Science and Complexity, 2013, 26(1): 117-136.

In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In  articular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis (FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized in concept lattices). In complex environments, such as sport competitions, the large amount of information currently available turns concept lattices into complex networks. The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure- is analyzed. It is applied to building a knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.
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