Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Adaptive behaviour through morphological plasticity in natural and artificial systems.

Our concept of intelligence is changing. Embodiment has led to the rise of morphologies in Artificial Intelligence (AI) research. This thesis focuses on two research questions: 1) How can system morphologies, well-adapted to changing environments, be designed? 2) How can adaptive behaviour be generated through morphology? It is the fundamental argument of this thesis that morphological plasticity (MP), the environmentally induced variation in growth or development, can provide a solution to both questions. Specifically, this thesis is based around a detailed study of diatom valve morphogenesis. Diatoms, a unicellular organism, construct intricate siliceous structures (valves) around themselves which exhibit high plasticity to the environment. Diatom valve morphogenesis is a good example of how morphologies can be well-adapted to changing environments, an open problem in AI, and how adaptive behaviour can be generated through morphological processes alone. Through a constructivist approach this thesis contributes to both understanding of MP in natural systems and the design of MP algorithms for artificial adaptive systems. Several original models and frameworks are defined within this thesis: the Nature's Batik Model of basic diatom valve morphogenesis the Cellanimat, a 'Dynamic Morphology' based on the unicell, capable of MP driven adaptive behaviour through its unique 'Artificial Cytoskeleton' model of cytoskeletal dynamics the Environment-Phenotype Map framework and the Cellanimat Colony Model, which combines all previous models for the investigation of MP mechanisms during diatom colony formation. Cellanimat dynamics and optimization are thoroughly investigated and the model is shown to be multi functional, evolvable, scalable and reasonably robust