This paper summarizes a series of recent research results made at Edinburgh University based on projects that apply domain knowledge of standard shapes and relationships to solve ...
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Qualitativeprobabilistic reasoningin a Bayesiannetworkoften reveals tradeoffs: relationships that are ambiguousdue to competingqualitative influences. Wepresent twotechniquesthat ...
In the standard model of inductive inference, a learner gets as input the graph of a function, and has to discover (in the limit) a program for the function. In this paper, we cons...
In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of regionbased and contour-based segmentation ...