We argue that some of the computational complexity associated with estimation of stochastic attributevalue grammars can be reduced by training upon an informative subset of the fu...
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...
Traditional algorithms for prime implicate generation [Quine, 1952; McCluskey, 1956; Tison, 1967; Kean and Tsiknis, 1990; de Kleer, 1992] require the input formulas to be first tr...
In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We...
We present in this work a wide spectrum of results on analyzing the behavior of parallel heuristics for solving optimization problems. We focus on evolutionary algorithms as well ...