Bayesian networks are a popular way for representing probabilistic knowledge in artificial intelligence. They have been used for quite some time [37,29], but their use was restricted until the late eighties due to efficiency reasons. Work by Pearl [93] and Lauritzen and Spiegelhalter [71] on efficient localized interpretation methods led to the renewed interest in Bayesian networks.
Pearl [92,93] has done influential work on
probabilistic reasoning for artificial intelligence. An important
contribution is a method for localized computation of Bayesian
networks for the special case of polytrees (singly connected
networks). This is done by breaking down the probabilistic support
for a node into two components. The first component is the causal
support from the node's parents (
), potentially influenced by
other children of the parents. The other component is the influence
from the node's childrens (
). By separating these
components, belief propagation can be done locally without introducing
instabilities (e.g., counting evidence twice).
Lauritzen and Spiegelhalter [71] also have done influential work in improving the efficiency of Bayesian Networks. They describe a method that can be used for any type of Bayesian Networks, not just polytrees as above. The idea is to convert the directed graph into a tree of cliques, where each clique is a maximal set of nodes that directly influence each other. Propagation then proceeds among the cliques in a manner analogous to that in Pearl's method.
Many commercial systems for Bayesian Networks incorporate this algorithm. In particular, this algorithm forms the basis for the belief propagation in Hugin [62] which was developed by researchers from the University of Aalborg who have done earlier work with Lauritzen.
Large Bayesian networks can be intractable to interpret exactly when there are sets of interdependent nodes that collectively define a very large state space. In cases like this approximate techniques can be used to provide a solution. Pearl [93] developed an approach to doing this that uses stochastic simulation to determine the node posterior probabilities. This is a Monte Carlo method in which during each iteration, random values are selected for each node from the distribution of values based on the current parent configuration for the node. This iterative process is similar to the data augmentation used by Bruce and Wiebe [23]. At the end of the simulation, the relative frequency of the times that the node has each value defines it's posterior probability distribution.
Charniak and Goldman [28] describe how Bayesian networks can be applied to plan recognition. They show how this is a more natural approach that previous ones, because of the uncertainty involved in ascribing plans to people. The main contribution of the paper is the application of Bayesian Networks to natural language processing. An additional contribution of the paper is a description of how knowledge encoded in traditional knowledge representation languages can be converted into a Bayesian network. This is illustrated with examples from earlier work in abductive reasoning (e.g., the liquor store robbery scenario). One drawback of the approach is that it relies solely on ``explaining away'' of evidence among competing hypotheses. For instance, there is no mechanism for negative evidence or the influence of higher-level plans (e.g., be a good citizen).