This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic pro...
Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for...
Due to computational intractability, large scale coordination algorithms are necessarily heuristic and hence require tuning for particular environments. In domains where character...
We show how Recursive Markov Chains (RMCs) and their restrictions can define probabilistic distributions over XML documents, and study tractability of querying over such models. ...
Michael Benedikt, Evgeny Kharlamov, Dan Olteanu, P...
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...