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This focus area concentrates on approaches to control and manage a collection of embedded agents to perform a collective task. The main focus is on swarm robotics, where a collection of robot agents have to compete or cooperate to successfully achieve a task. This is achieved by developing control and communications mechanisms that have their foundations in the social and economic sciences.


M Mtshali

PhD Started in 2009

R Visagie

M.Sc Started in 2005

H Grobler

M.Sc Completed in 2005
Hons-B.Sc Completed in 2004

A Eyal

M.Sc Started in 2003

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999


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 Daniel Rodic

Portrait photo




 Multi-Agent Systems
Data Mining


 Degree specific information: PhD


 Intelligent Distributed Agent Based Architecture, INDABA


This thesis presents work done on developing a multi-robot system architecture for cooperation. The thesis and the architecture presented herein focuses on two aspects of a multi-robot systems that form INDABA: Hybrid Agent Architecture and Framework for Cooperation. Hybrid Agent Architecture presented here combines the sub-symbolic knowledge representation layered architecture with a symbolic layer that allows for deliberative cooperation and social relationships. In this manner, the best characteristics of both approaches are utilised while their weaknesses are rectified by such a complementary approach. The Framework for Cooperation fully utilises a symbolic portion of the agents participating in the architecture in order to provide a framework for positive interaction - cooperation. The framework caters for heterogeneous agents with not necessarily a common set of beliefs, desires and intentions. From a topological view, the architecture presented in this thesis is a hybrid architecture. There is a central component of the whole system (centralised approach) but it is by no means the controlling component (decentralised approach). The central component of the system has more of a facilitating than controlling role.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.


 Degree specific information: M.Sc


 A Hybrid Heuristic Approach for Rule Extraction


The topic of this thesis is knowledge discovery algorithms. The knowledge discovery process and associated problems are discussed, followed by an overview of three classes of artificial intelligence based knowledge discovery algorithms. Typical representatives of each of these classes are presented and discussed in greater detail. Then a new knowledge discovery algorithm, called Hybrid Classifier System (HCS), is presented. The guiding concept behind the new algorithm was simplicity. The new knowledge discovery algorithm is loosely based on schemata theory. It is evaluated against CN2, C4.5, BRAINNE and BGP. Results are discussed and compared. A comparison was done using a benchmark of classification problems. These results show that the new knowledge discovery algorithm performs satisfactory, yielding accurate, crisp rule sets. Probably the main strength of the HCS algorithm is its simplicity, so it can be the foundation for many possible future extensions.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.

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