This paper introduces decision-theoretic planning techniques into automatic music generation. Markov decision processes (MDPs) are a mathematical model of planning under uncertain...
This paper is part of a project to match descriptions of real-world instances and probabilistic models, both of which can be described at mulvel of abstraction and detail. We use ...
Inspectable Bayesian student models have been used to support student reflection, knowledge awareness and communication among teacher, students and parents. This paper presents a...
The traditional approach to building Bayesian networks is to build the graphical structure using a graphical editor and then add probabilities using a separate spreadsheet for eac...
A research organization responds to a variety of customer requests. Each high level request is broken down into a set of low level requests. For each low level request, the resear...
Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain e...
Jonathan D. Pfautz, Zach Cox, Geoffrey Catto, Davi...
The next development in building Bayesian networks will most likely entail constructing multipurpose models that can be employed for varying tasks and by different types of user. ...
Hermina J. M. Tabachneck-Schijf, Linda C. van der ...
Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At any time, a human reasoner in a large domain ma...
In this paper, a unified framework for representing uncertain information based on the notion of an interval structure is proposed. It is shown that the lower and upper approximat...