Alan D. Blair

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In recent years it has been shown that first order recurrent neural networks trained by gradient-descent can learn not only regular but also simple context-free and context-sensitive languages. However, the success rate was generally low and severe instability issues were encountered. The present study examines the hypothesis that a combination of(More)
Although TD-Gammon is one of the major successes in machine learning , it has not led to similar impressive breakthroughs in temporal difference learning for other applications or even other games. We were able to replicate some of the success of TD-Gammon, developing a competitive evaluation function on a 4000 parameter feed-forward neu-ral network,(More)
Abstract Algorithms for evolving agents that learn during their lifetime have typically been evaluated on only a handful of environments. Designing such environments is labour intensive, potentially biased, and provides only a small sample size that may prevent accurate general conclusions from being drawn. In this paper we introduce a method for(More)
— We demonstrate applicability of a general class of multivariate probability density functions of the form e −P (x) , where P (x) is an elliptic polynomial, to decentralised data fusion tasks. In particular, we derive an extension to the Covariance Intersect algorithm for this class of distributions and demonstrate the necessary operations – diffusion,(More)
As a test-bed for studying evolutionary and other machine learning techniques, we have developed a simulated hockey game called Shock in which players attempt to shoot a puck into their enemy's goal during a fixed time period. Multiple players may participate – one can be controlled by a human user, while the others are guided by artificial controllers. In(More)