There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynam...
Descriptive Sampling (DS), a Monte Carlo sampling technique based on a deterministic selection of the input values and their random permutation, represents a deep conceptual chang...
One method for reducing the time required for plan generation is to learn search control rules from experience. Most of the recent work in learning search control knowledge has co...
We present a fully distributed multi-agent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure ...
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...