We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful...
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned b...
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Pe...
A new class of associations (polynomial itemsets and polynomial association rules) is presented which allows for discovering nonlinear relationships between numeric attributes wit...
This paper investigates the manner in which decisionmaking is influenced by the impressions given by lifelike agents in negotiation situations. These impressions comprise an agent...
This paper describes the ADO.NET Entity Framework, a platform for programming against data that raises the level of ion from the logical (relational) level to the conceptual (enti...