New methods to generate hard random problem instances have driven progress on algorithms for deduction and constraint satisfaction. Recently Achlioptas et al. (AAAI 2000) introduc...
Henry A. Kautz, Yongshao Ruan, Dimitris Achlioptas...
The evolution of distributed applications to reflect structural changes or to adapt to specific conditions of the run-time environment is a difficult issue especially if continuou...
Noel De Palma, Sara Bouchenak, Slim Ben Atallah, D...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
We argue that in general, the analysis of lexical cohesion factors in a document can drive a summarizer, as well as enable other content characterization tasks. More narrowly, thi...
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...