Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
This paper discusses Simphony as an integrated environment for building special purpose simulation tools for modeling construction systems. Simphony provides various services that...
Predicting the behavior of physical systems is essential to both common sense and engineering tasks. It is made especially challenging by the lack of complete precise knowledge of...
Formalizing the ontological commitment of a logical language means offering a way to specify the intended meaning of its vocabulary by constraining the set of its models, giving e...
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...