Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
This paper addresses the challenge of recognizing behavior of groups of individuals in unconstraint surveillance environments. As opposed to approaches that rely on agglomerative ...
Conventional methods for state space exploration are limited to the analysis of small systems because they suffer from excessive memory and computational requirements. We have dev...
William J. Knottenbelt, Peter G. Harrison, Mark Me...
We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free gr...
This paper presents three new algorithms for the automatic construction of models from Object Oriented Probabilistic RelationalModels. The first two algorithms are based on the kn...