Abstract
The evolutionary nature of humans requires agent systems to be continuously
replaced due to their inability to meet or adapt to our changing needs. Therefore,
to eliminate the need for a human to continuously adapt an agent, evolutionary
agents are required [Chu04, Ore99, Rak02, Syc96]. This dissertation develops a
feasible option to ensuring that agents continuously develop desirable behaviour.
The solution is a specialized architecture that embeds self-evolvement into a
target agent. The specialized architecture ensures that desirable behaviour
emerges from any agent, as it is embedded between the target agent and the
target agent’s environment and therefore is able to obtain domain- and hardwarespecific
information from the target agent. The specialized architecture is a
comprehensive methodology that incorporates all agents with the ability to
embed the required self-evolvement enhancements as domain- and hardwarespecific
information is obtained from the target agent. The specialized
architecture responsible for embedding self-evolvement into an agent is the
generic self-evolvement effecting evolutionary agent (GSEEA). The GSEEA is
developed with a single goal, which is to ensure that the target agent meets the
requirements of a changing environment. Changing environmental conditions can
include different network conditions and different platforms. The GSEEA’s goal is
accomplished by embedding the required self-evolvement enhancements into the
target agent to produce a self-evolvement enhanced agent.
In this dissertation the GSEEA is implemented to demonstrate its feasibility and
problem-solving accuracy. In the GSEEA implementation the target agent is a
puzzle-solving agent and the self-evolvement enhanced agent is the selfevolvement
enhanced puzzle-solving agent. The GSEEA’s deliberative
component consists of two algorithms, namely a genetic algorithm and a learning
algorithm. The GSEEA’s genetic algorithm develops knowledge base rules (selfContents
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evolvement enhancements) that modify actuator information. The GSEEA’s
learning algorithm updates developed knowledge base rules by modifying sensor
information. The GSEEA tests the developed self-evolvement enhancements by
embedding them into the target agent through the target agent’s knowledge base
manager, evaluating the developed self-evolvement enhancements and deleting
those which do not enhance the target agent. The target agent achieves selfevolvement
as additional enhancements required by the self-evolvement
enhanced agent can be achieved by applying the same process followed to
enhance the target agent which was discussed previously.
The evaluation of the GSEEA implementation demonstrated that the GSEEA was
implemented successfully based on feasibility and problem-solving accuracy as
the self-evolvement enhanced puzzle-solver agent outperformed the puzzlesolver
agent.
Prof. E.M. Ehlers