We dene the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, ...
We show how to nd a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the rst step in the method of conditioning for inference....
— Ergodic stochastic optimization (ESO) algorithms are proposed to solve resource allocation problems that involve a random state and where optimality criteria are expressed in t...
Abstract— Many techniques for constructing unitary spacetime constellations have been proposed. To minimize bit-error rate (BER) in a wireless communication system, constellation...
The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-...