Abstract
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.