Joshua Hallam

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There is a spectrum of methods for learning robot control. At one end there are model-free methods (eg. Q-learning, AHC, bucket brigade), and at the other there are model-based methods, (eg. dynamic programming by value or policy iteration). The advantage of one technique is the weakness of the other. Model-based methods use experience eeectively, but are(More)
a r t i c l e i n f o a b s t r a c t We introduce a new method for showing that the roots of the characteristic polynomial of certain finite lattices are all nonnegative integers. This method is based on the notion of a quotient of a poset which will be developed to explain this factorization. Our main theorem will give two simple conditions under which(More)
We introduce a new method for showing that the roots of the characteristic polynomial of a finite lattice are all nonnegative integers. Our method gives two simple conditions under which the characteristic polynomial factors. We will see that Stanley's Supersolvability Theorem is a corollary of this result. We can also use this method to demonstrate a new(More)
We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall(More)
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