A generic framework for life simulation and learning multi-agent systems with the ability to solve complex problems in multiple domains

M.Sc. (Computer Science)This research study investigates multi-agent systems (MASs), artificial life concepts and machine learning, amongst other things, in answering the key research question: “How can a generic multi-agent system integrate with machine learning through artificial life principles?” In answering this question, this dissertation illustrates the design and development of a generic multi-agent, life simulation and learning software framework. This framework simplifies and enables the realisation of MASs in solving complex problems in multiple domains. Finally, this research presents a prototype solution as a proof of concept of the framework’s strengths and weaknesses. The research study illustrates the design of MASs utilising sound design principles, patterns and methodologies. Furthermore, this research explores the requirements for creating and integrating MASs with other technologies, as well as the possible pitfalls in creating such large-scale systems. In addressing the necessity of learning, several machine learning techniques are examined and reinforcement learning is identified as an ideal candidate for the proposed framework. In addition, by understanding the overall machine learning process, the proposed framework integrates machine learning as three separate processes: data extraction, learning and inference. Lastly, the literature study focuses on artificial life, specifically its use in MASs, and defines what constitutes an intelligent system. This research depicts artificial life as a plausible natural integrator between MAS and machine learning technologies. The proposed framework presented in this dissertation consists of five core agent modules that can be extended, depending on the problem domain requirements. The framework in itself is self-containing and independent of any concrete implementation. A multi-agent antivirus system is presented as the prototype implementation of the proposed framework. A quantitative and qualitative analysis was conducted, identifying the results of the prototype and generic framework while highlighting strengths and weaknesses. The contribution of this research is found partly in the proposed generic framework as a means of augmenting mechanisms for MAS design and development by means of artificial life and machine learning integration. In a broader context, this research serves as a foundation towards creating advanced MAS frameworks, leading to numerous interesting and influential agent-oriented applications