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Following Tesauro's work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of the dice, application of the network to all legal moves, and choosing the move with the highest evaluation. However, no back-propagation , reinforcement or temporal difference… (More)

One of the persistent themes in Artificial Life research is the use of co-evolutionary arms races in the development of specific and complex behaviors. However, other than Sims's work on artificial robots, most of the work has attacked very simple games of prisoners dilemma or predator and prey. Following Tesauro's work on TD-Gammon, we used a 4000… (More)

Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and IFS-like fractal state sets. Follow-on work focused mainly on the extraction and minimization of a nite state automaton (FSA) from… (More)

Recent work by Siegelmann has shown that the computational power of neural networks matches that of Turing Machines. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction… (More)

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)

Robustness has long been recognised as a critical issue for co-evolutionary learning. It has been achieved in a number of cases, though usually in domains which involve some form of non-determinism. We examine a deterministic domain { a pseudo real-time two-player game called Tron { and evolve a neural network player using a simple hill-climbing algorithm.… (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)