Herself’s Artificial Intelligence

Reasoning using graphical models is rapidly gaining popularity. Not just used in reasoning they are also becoming useful in computer vision where they are used to classify objects.

Graphical models include: constraint networks, Markov random fields, belief networks, Bayes networks and influence diagrams.

Traditionally algorithms for these networks have been either inference-based or search based. While inference algorithms are not practical time wise for most problems outside the classroom some of the search algorithms perform quite well. Combinations of these algorithms are being used to solve knowledge representation problems not otherwise practical with just inference based algorithms. Often algorithms are tailored for each specific problem.

The advantages of this method is that all possible outcomes are known and declared in the graphical model. Also the models are easily understood by humans, and the techniques used are well understood.

Nodes on the graph represent facts or states and may be known or unknown, hidden or visible.

Connections between nodes may be hidden or not, they may be probabilities or equations and represent connections between the data.

The graphical representation allows us to decompose the big problem into smaller simpler to solve problems.

More information:
Graphical Model Algorithms at UC Irvine has a large list of software and example problems using graphical models in knowledge reasoning.
There are some Power Point and PDF tutorials you can download from Microsoft
Video lecture on Graphical models
A list of links to several software tools for doing graphical models
Graphical knowledge representation for human detection ( pdf )
Mean Field Theory for Graphical Models
Graphical Models for Discovering Knowledge (excellent beginner’s paper )

See also:
Bayesian logic
Knowledge representation and predicate calculus
Hidden Markov models