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Research is also conducted to develop computational intelligence algorithms in the domain of financial analysis, with a specific focus on trading, trend analysis, and forecasting.


List the current members actively doing research in this focus area. [ Show ]


E Papacostantis

M.Sc Started in 2004
Hons-B.Sc Completed in 2003


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 Evangelos Papacostantis

Portrait photo




 Swarm Intelligence
 Evolutionary Computation


 Degree specific information: M.Sc


 Co-Evolutionary Approach to Probabilistic Game Learning using PSO


The domain of complex board games has been under the microscope for many years in the field of AI. These games provide an ideal testing ground to explore a number of AI techniques, concepts and approaches. Games that are more representative of real world problems are probabilistic games or otherwise called non-deterministic games. These games have imperfect information, meaning that a players' actions within the game is determined by random/probabilistic elements. Co-evolution techniques have successfully been applied to these games, allowing competitive and reasonably intelligent agents to immerge. Co-evolution techniques allow unintelligent/random agents to compete against each other and incrementally learn from better performing agents. This research will generally investigate how co-evolution and PSO techniques can be used to find competitive probabilistic game playing agents. Different PSO topologies are going to be examined together with different co-evolution schemes. Backgammon and Poker are the two probabilistic games which are going to be used for evaluation purposes.

 Supervisor / Co-Supervisor:

 AP Engelbrecht
 N Franken


 Not available for download yet.


 Degree specific information: Hons-B.Sc


 Development of advanced data analysis/data mining tool


The cerebral cortex is arguably the most fascinating structure in all of human physiology. The ability to associate items according to their similarities/differences was first pointed out by Aristotle. This project includes the creation of an advanced data analysis/data mining tool, by attempting to simulate the cerebral cortex. It will employ self-organizing feature maps, that compress high dimensional data into two dimensional maps, accomplished by different unsupervised training algorithms. It will allow features such as association, classification, path finding and forecasting. A number of visual techniques will be implemented on the two dimensional maps to reveal all these features. The tool will be further more improved by adding knowledge exploration features with the use of decision trees, rule inductions and genetic algorithms.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.

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