Learn More
Random Boolean networks (RBNs) are complex systems composed of many simple components which have been much analysed and shown to have many robust generic properties. Some synchronous versions have been innuential as highly abstract models of speciic biological systems, but for many biological phenomena asynchronous versions are more plausible models. Though(More)
We advance a dominant neural strategy for facilitating conceptual thought. Concepts are groupings of "object" attributes. Once the brain learns such critical groupings, the "object" attributes are inhibited from conscious awareness. We see the whole, not the parts. The details are inhibited when the concept network is activated, ie. the inhibition is(More)
One focus of multi-agent systems research is the notion that complex outcomes or behaviours may be arrived at through the interaction of agents. However, it is still an open question as to how agents in a complex system form coalitions or modules, and how these coalitions self-organize into hierarchies. In this paper, we begin to address this question by(More)
There is growing evidence that for a range of dynamical systems featuring complex interactions between large ensembles of interacting elements, mutual information peaks at order-disorder phase transitions. We conjecture that, by contrast, information flow in such systems will generally peak strictly on the disordered side of a phase transition. This(More)
Dots-and-Boxes is a child's game which remains analytically unsolved. We implement and evolve ar-tiicial neural networks to play this game, evaluating them against simple heuristic players. Our networks do not evaluate or predict the nal outcome of the game, but rather recommend moves at each stage. Superior generalisation of play by co-evolved populations(More)
Many transportation problems, such as the travelling salesman problem, are computationally hard but often soluble quickly, although with less certainty , by heuristic methods. Genetic algorithms fall into this category and generate results with favourable scaling behaviour. We apply a two-level genetic algorithm to an advanced transportation problem, an(More)
The earliest stages in our perception of the world have a subtle but powerful influence on later thought processes; they provide the contextual cues within which our thoughts are framed and they adapt to many different environments throughout our lives. Understanding the changes in these cues is crucial to understanding how our perceptual ability develops,(More)
Both genetic algorithms (GAs) and artificial neural networks (ANNs) (connectionist learning models) are effective generalisations of successful biological techniques to the artificial realm. Both techniques are inherently parallel and seem ideal for implementation on the current generation of parallel supercomputers. We consider how the two techniques(More)