With the advent of digital printing presses and the continued development of associated technologies, variable data printing (VDP) is becoming more and more common. VDP allows for a series of data instances to be bound to a single template document in order to produce a set of result document instances, each customized depending upon the data provided. As it gradually enters the mainstream of digital publishing there is a need for appropriate and powerful editing tools suitable for use by creative professionals. This thesis investigates the problem of representing variable data documents in an editable visual form, and focuses on the technical issues involved with supporting such an editing model. Using a document processing model where the document is produced from a data set and an appropriate programmatic transform, this thesis considers an interactive editor developed to allow visual manipulation of the result documents. It shows how the speed of the reprocessing necessary in such an interactive editing scenario can be increased by selectively re-evaluating only the required parts of the transformation, including how these pieces of the transformation can be identified and subsequently re-executed. The techniques described are demonstrated using a simplified document processing model that closely resembles variable data document frameworks. A workable editor is also presented that builds on this processing model and illustrates its advantages. Finally, an analysis of the performance of the proposed framework is undertaken including a comparison to a standard processing pipeline.

The field of physical modelling and simulation plays a vital role in advancing numerous scientific and engineering disciplines. To cope with the increasing size and complexity of physical models, a number of modelling and simulation languages have been developed. These languages can be divided into two broad categories: causal and noncausal. Causal languages express a system model in terms of directed equations. In contrast, a noncausal model is formulated in terms of undirected equations. The fact that the causality can be left implicit makes noncausal languages more declarative and noncausal models more reusable. These are considered to be crucial advantages in many physical domains. Current, mainstream noncausal languages do not treat equational models as first-class values; that is, a model cannot be parametrised on other models or generated at simulation runtime. This results in very limited higher-order and structurally dynamic modelling capabilities, and limits the expressiveness and applicability of noncausal languages. This thesis is about a novel approach to the design and implementation of noncausal languages with first-class models supporting higher-order and structurally dynamic modelling. In particular, the thesis presents a language that enables: (1) higher-order modelling capabilities by embedding noncausal models as first-class entities into a functional programming language and (2) efficient simulation of noncausal models that are generated at simulation runtime by runtime symbolic processing and just-in-time compilation. These language design and implementation approaches can be applied to other noncausal languages. This thesis provides a self-contained reference for such an undertaking by defining the language semantics formally and providing an in-depth description of the implementation. The language provides noncausal modelling and simulation capabilities that go beyond the state of the art, as backed up by a range of examples presented in the thesis, and represents a significant progress in the field of physical modelling and simulation.

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature.

Self-assembly is a ubiquitous process in nature in which a disordered set of components autonomously assemble into a complex and more ordered structure. Components interact with each other without the presence of central control or external intervention. Self-assembly is a rapidly growing research topic and has been studied in various domains including nano-science and technology, robotics, micro-electro-mechanical systems, etc. Software self-assembly, on the other hand, has been lacking in research efforts. In this research, I introduced Automated Self-Assembly Programming Paradigm (ASAP²), a software self-assembly system whereby a set of human made components are collected in a software repository and later integrated through self-assembly into a specific software architecture. The goal of this research is to push the understanding of software self-assembly and investigate if it can complement current automatic programming approaches such as Genetic Programming. The research begins by studying the behaviour of unguided software self-assembly, a process loosely inspired by ideal gases. The effect of the externally defined environmental parameters are then examined against the diversity of the assembled programs and the time needed for the system to reach its equilibrium. These analysis on software self-assembly then leads to a further investigation by using a particle swarm optimization based embodiment for ASAP². In addition, a family of network structures is studied to examine how various network properties affect the course and result of software self-assembly. The thesis ends by examining software self-assembly far from equilibrium, embedded in assorted network structures. The main contributions of this thesis are: (1) a literature review on various approaches to the design of self-assembly systems, as well as some popular automatic programming approaches such as Genetic Programming; (2) a software self-assembly model in which software components move and interact with each other and eventually autonomously assemble into programs. This self-assembly process is an entirely new approach to automatic programming; (3) a detailed investigation on how the process and results of software self-assembly can be affected. This is tackled by deploying a variety of embodiments as well as a range of externally defined environmental variables. To the best of my knowledge, this is the first study on software self-assembly.

The process of protein structure prediction is a crucial part of understanding the function of the building blocks of life. It is based on the approximation of a protein free energy that is used to guide the search through the space of protein structures towards the thermodynamic equilibrium of the native state. A function that gives a good approximation of the protein free energy should be able to estimate the structural distance of the evaluated candidate structure to the protein native state. This correlation between the energy and the similarity to the native is the key to high quality predictions. State-of-the-art protein structure prediction methods use very simple techniques to design such energy functions. The individual components of the energy functions are created by human experts with the use of statistical analysis of common structural patterns that occurs in the known native structures. The energy function itself is then defined as a simple weighted sum of these components. Exact values of the weights are set in the process of maximisation of the correlation between the energy and the similarity to the native measured by a root mean square deviation between coordinates of the protein backbone. In this dissertation I argue that this process is oversimplified and could be improved on at least two levels. Firstly, a more complex functional combination of the energy components might be able to reflect the similarity more accurately and thus improve the prediction quality. Secondly, a more robust similarity measure that combines different notions of the protein structural similarity might provide a much more realistic baseline for the energy function optimisation. To test these two hypotheses I have proposed a novel approach to the design of energy functions for protein structure prediction using a genetic programming algorithm to evolve the energy functions and a structural similarity consensus to provide a reference similarity measure. The best evolved energy functions were found to reflect the similarity to the native better than the optimised weighted sum of terms, and therefore opening a new interesting area of research for the machine learning techniques.

In this thesis, the design and development of a decision support system for the selection of a variance reduction technique for discrete event simulation studies is presented. In addition, the performance of variance reduction techniques as stand alone and combined application has been investigated. The aim of this research is to mimic the process of human decision making through an expert system and also handle the ambiguity associated with representing human expert knowledge through fuzzy logic. The result is a fuzzy expert system which was subjected to three different validation tests, the main objective being to establish the reasonableness of the systems output. Although these validation tests are among the most widely accepted tests for fuzzy expert systems, the overall results were not in agreement with expectations. In addition, results from the stand alone and combined application of variance reduction techniques, demonstrated that more instances of stand alone applications performed better at reducing variance than the combined application. The design and development of a fuzzy expert system as an advisory tool to aid simulation users, constitutes a significant contribution to the selection of variance reduction technique(s), for discrete event simulation studies. This is a novelty because it demonstrates the practicalities involved in the design and development process, which can be used on similar decision making problems by discrete event simulation researchers and practitioners using their own knowledge and experience. In addition, the application of a fuzzy expert system to this particular discrete event simulation problem, demonstrates the flexibility and usability of an alternative to the existing algorithmic approach. Under current experimental conditions, a new specific class of systems, in particular the Crossdocking Distribution System has been identified, for which the application of variance reduction techniques, i.e. Antithetic Variates and Control Variates are beneficial for variance reduction.

Evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number of key issues in evolutionary multiobjective optimisation. Also, a new evolutionary multiobjective (EMO) algorithm is proposed. Firstly, this new EMO algorithm is applied to solve the multiple 0/1 knapsack problem (a wellknown benchmark multiobjective combinatorial optimisation problem) producing competitive results when compared to other state-of-the-art MOEAs. Secondly, this thesis also investigates the application of general EMO algorithms to solve real-world nurse scheduling problems. One of the challenges in solving real-world nurse scheduling problems is that these problems are highly constrained and specific-problem heuristics are normally required to handle these constraints. These heuristics have considerable influence on the search which could override the effect that general EMO algorithms could have in the solution process when applied to this type of problems. This thesis outlines a proposal for a general approach to model the nurse scheduling problems without the requirement of problem-specific heuristics so that general EMO algorithms could be applied. This would also help to assess the problems and the performance of general EMO algorithms more fairly.

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”.

This thesis asserts that modern developments in technology have not been used as extensively as they could to aid blind people in their learning objectives. The same could also be said of many aspects of other areas of their lives. In particular in many countries blind students are discouraged from learning mathematics because of the intrinsically visual nature of many of the topics and particularly geometry. For many young people mathematics is also not a subject that is easily or willingly tackled. The research presented here has thus sort to answer whether a playful haptic environment could be developed which would be attractive to blind users to learn and interact with geometric concepts. In the study a software tool using a haptic interface was developed with certain playful characteristics. The environment developed sought to give the blind users practice in interacting with three dimensional geometric shapes and the investigation of the size of these shapes and their cross-section. The playful elements were enhanced by adding elements of competition such as scores and time limits which promote competition between the users. The tests have shown that blind users can easily use the system to learn about three dimensional shapes and that practice increases their confidence in recognising shape and size of these objects.

A key advantage of BDI-based agent programming is that agents can deliberate about which course of action to adopt to achieve a goal or respond to an event. However while state-of-the-art BDI-based agent programming languages provide flexible support for expressing plans, they are typically limited to a single, hard-coded, deliberation strategy(perhaps with some parameterisation) for all task environments. In this thesis, we describe a novel agent programming language, meta-APL, that allows both agent programs and the agent’s deliberation strategy to be encoded in the same programming language. Key steps in the execution cycle of meta-APL are reflected in the state of the agent and can be queried and updated by meta-APL rules, allowing a wide range of BDI deliberation strategies to be programmed. We give the syntax and the operational semantics of meta-APL, focussing on the connections between the agent’s state and its implementation. Finally, to illustrate the flexibility of meta-APL, we show how Jason and 3APL programs and deliberation strategy can be translated into meta-APL to give equivalent behaviour under weak bisimulation equivalence.