We investigate the discrete (finite) case of the Popper-Renyi theory of conditional probability, introducing discrete conditional probabilistic models for (multi-agent) knowledge...
This work addresses the problem of processing continuous nearest neighbor (NN ) queries for moving objects trajectories when the exact position of a given object at a particular t...
We present a new domain for unsupervised learning: automatically customizing the computer to a specific melodic performer by merely listening to them improvise. We also describe B...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
This research describes a probabilistic approach for developing predictive models of how students learn problem-solving skills in general qualitative chemistry. The goal is to use ...
Ron Stevens, Amy Soller, Melanie Cooper, Marcia Sp...