Traditional interactive evolutionary design systems combine auser based fitness function with an evolutionary search process. Effective integration of machine based tools, human designers and real world design processes, requires a higher level of information exchange between the user and the design system. This dual requirement of increasing the connectivity between the machine and the user as well as incorporating human preferences with machine based fitness evaluations is the main focus of this research. There are two problems in implementing the above, namely the problem of representation as well as user fatigue resulting from design evaluations. The initial work involved an integration of component-based representation, software agents and machine learning with an evolutionary programming algorithm for a relatively simple bridge design problem (the Bridge Design System) with both human and machine based evaluation. The main research contribution of the Bridge Design System was the integration of componentbased representation and the machine learning sub-system. The component-based representation addresses the problem of representation. The machine learning sub-system provides a possible solution to the user fatigue problem. The Bridge Design System was extended to tackle a more complex 3-D design problem related to Urban-Furniture design. To enhance the interactivity and usability of the system, population clustering based on solution similarity was introduced within the urban-furniture design system. The user fatigue issue was addressed further through population clustering which allowed users to work with larger population sizes than usual. Clustering also allowed the identification of features present in high performance as well as user preferred solutions.

This thesis studies deduction systems for modal logics and the relation between them. Natural deduction systems give proofs that are close to human reasoning but are not well suited to automation while refutation systems are well suited to automation but inference steps are not close to human informal reasoning. This thesis will introduce a natural deduction calculus with a resolution rule that gives a good framework for simulating different calculi and studying their properties. We show that this calculus is able to directly simulate a tableau calculus for modal logic using two different search strategies. We then introduce an ordered hyperresolution calculus for modal logic K using a structural transformation to preserve structure of input formulae. We show that there is a mapping from derivations in the ordered hyperresolution calculus to derivations in the natural deduction calculus and a further mapping in the other direction. The hyperresolution calculus is a standard calculus and we show that it is therefore possible to automatically generate proofs dose to human reasoning using already existing, fast theorem provers. We give extensions of the structural transformation to a number of extensions of K and show that the mappings in both directions still hold. Since we have two simulations in a common framework, the relation between the tableau and resolution simulation are considered.

In this thesis I present a framework for intelligent software agents to manage risk in electronic marketplaces using Option Derivatives. To compare the perfonnance of agents that trade Option Derivatives with agents not using them, I create a simulation of a financial marketplace in which software agents are vested with decision rules for buying and selling assets and Options. The motivation of my work is the need of risk management mechanisms for those Multi-Agent Systems where resources are allocated according to a market mechanism. Autonomous agents participating in such markets need to consider the risks to which they are exposed when trading in them, and to take actions to manage those risks. This thesis considers the hypothesis that software agents can benefit from trading Option Derivatives, using them as a tool to manage their exposure to uncertainty in the market. The main contributions of this thesis are: First, an abstract framework of an Option trading market is developed. This framework serves as a foundation for the implementation of computational Option trading mechanisms in systems using Market-Based resource allocation. The framework can be incorporated into existing Market-Based systems using the traded resources as the underlying assets for the Option market. Within the framework, four basic Option trading strategies are introduced, some of which reason about the risks exposed by their actions. These strategies are provided as a foundation for the development of more complex strategies that maximise the utility of the trading agents by the use of Options. The second contribution of this thesis is the analysis of the results from simulation experiments perfonned with the implementation of a software Multi-Agent System based on the developed Option trading framework. The system was developed in Java using the Repast simulation platfonn. The experiments were used to test the perfonnance of the developed trading strategies. This research shows that agents which traded Options by choosing actions aiming to minimize their risk perfonned significantly better than agents using other trading strategies, in the majority of the experiments. Agents using this risk-minimizing strategy also observed a lower correlation between the asset price and their returns, for the majority of the experimented scenarios. Agents which traded Options aiming to maximize their returns perfonned better than their peers in the scenarios where the asset price volatility was high. Finally, it was also observed that the perfonnance differential of the strategies increased as the uncertainty about the future price of the asset was increased.

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The human brain is an extremely powerful pattern recogniser, as well as being capable of displaying amazing feats of memory. It is clear that human memory is associative; we recall information by associating items together so that one may be used to recall another. This model of memory, where items are associated as pairs rather than stored at a particular location, can be used to implement computer memories which display powerful properties such as robustness to noise, a high storage capacity and the ability to generalise. One example of such a memory is the Binary Correlation Matrix Memory (CMM), which in addition to the previously listed properties is capable of operating extremely quickly in both learning and recall, as well as being well suited for hardware implementation. These memories have been used as elements of larger pattern recognition architectures, solving problems such as object recognition, text recognition and rule chaining, with the memories being used to store rules. Clearly, the performance of the memories is a large factor in the performance of such architectures. This thesis presents a discussion of the issues involved with optimising the performance of CMMs in the context of larger architectures. Two architectures are examined in some detail, which motivates a desire to improve the storage capacity and generalisation capability of the memories. The issues surrounding the optimisation of storage capacity of CMMs are discussed, and a method for improving the capacity is presented. Additionally, while CMMs are able to generalise, this capability is often ignored. A method for producing codes suitable for storage in a CMM is presented, which provides the ability to react to previously unseen inputs. This potentially adds a powerful new capability to existing architectures.