Chris Scogings

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We discuss algorithms and methods for classifying the clusters of model animals that emerge from simulations of collective behaviour in artificial life models. We show how important statistical properties for understanding scaling and universal growth can be measured from complex and chaotic model systems. We describe animal clustering algorithms and the(More)
Animat agents are usually formulated as spatially located agents that interact according to some microscopic behavioural rules. We use our predator-prey animat model to explore spatial segregation and other self-organising effects. We compare the emergent macroscopic behaviour with that of non-intelligence models such as those governed solely by microscopic(More)
There has been some ambiguity about the growth of attractors in Kauffman networks with network size. Some recent work has linked this to the role and growth of circuits or loops of boolean variables. Using numerical methods we have investigated the growth of structural circuits in Kauffman networks and suggest that the exponential growth in the number of(More)
The growth in number and nature of dynamical at-tractors in Kauffman NK network models are still not well understood properties of these important random boolean networks. Structural circuits in the underpinning graph give insights into the number and length distribution of attractors in the NK model. We use a fast direct circuit enumeration algorithm to(More)
Computer simulations of complex systems such as physical aggregation processes or swarming and collective behaviour of life-forms, often require order N-squared computational complexity for N microscopic components. This is a significant handicap to simulating systems large enough to compare with real-world experimental data. We discuss space partitioning(More)
Planning and steering numerical experiments that involve many simulations are difficult tasks to automate. We describe how a simulation scheduling tool can help experimenters submit and revoke simulation jobs on the basis of the most up to date partial results and resource estimates. We show how ideas such as pre- and post-conditions; interrupt handling;(More)
2007 The Kau↵man N-K, or random boolean network, model is an important tool for exploring the properties of large scale complex systems. There are computational challenges in simulating large networks with high connectivities. We describe some high-performance data structures and algorithms for implementing large-scale simulations of the random boolean(More)