We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Dominance testing, the problem of determining whether an outcome is preferred over another, is of fundamental importance in many applications. Hence, there is a need for algorithm...
The concept of Player Adaptive Entertainment Computing (PAEC) is introduced to provide personalized experiences when interacting with the entertainment media. Two of the important ...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
If we are to be successful in the development of the next generation of agent oriented systems we must deal with the critical issue of requirements traceability. Failure to do so w...