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Recurrent neural networks are capable of learning context-free tasks, however learning performance is unsatisfactory. We investigate the eeect of biasing learning towards nding a solution to a context-free prediction task. The rst series of simulations xes various sets of weights of the network to values found in a successful network, limiting the search(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)
– We present a new paradigm extending the Iterated Prisoner's Dilemma to multiple players. Our model is unique in granting players information about past interactions between all pairs of players – allowing for much more sophisticated social behaviour. We provide an overview of preliminary results and discuss the implications in terms of the evolutionary(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)