This paper discusses recent work in creating a computer program that plays the role of a sensitive musical accompanist. An accompanist must synthesize a number of diierent sources of information including a real-time analysis of the soloist's acoustic signal, an understanding of the timing relationships represented in the musical score, the interpretation of the soloist learned through rehearsa...

The problem of setting production lead times in manufacturing is well known and many firms set the value as an average of past operations, frequently resorting to inventory, overtime and other unplanned activities to match supply with demand. In this paper, we use Bayesian belief networks to model a common production process with probabilistic manufacturing lead time based on more realistic ass...

In the absence of an agreed measure of software quality the density of defects has been a very commonly used surrogate measure. As a result there have been numerous attempts to build models for predicting the number of residual software defects. Typically, the key variables in these models are either size and complexity metrics or measures arising from testing information. There are, however, s...

This paper examines the use of stochastic simulation of Bayesian belief networks as a method for computing the probabilities of values of variables. Specifically, it examines the use of a scheme described by Henrion, called logic sampling, and an extension to that scheme described by Pearl. The scheme devised by Pearl allows us to "clamp" any number of variables to given values and to conduct s...

A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless P=NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks. Stochastic simulation methods, which often improve run times, provide an alternative to exact inference algorith...

Bayesian belief networks or causal probabilistic networks may reach a certain size and complexity where the computations involved in exact probabilistic inference on the network tend to become rather time consuming. Methods for approximating a network by a simpler one allow the computational complexity of probabilistic inference on the network to be reduced at least to some extend. We propose a...

In this paper we present a novel constraint based structural learning algorithm for causal networks. A set of conditional independence and dependence statements (CIDS) is derived from the data which describes the relationships among the variables. Although we implicitly assume that there exists a perfect map for the true, yet unknown, distribution , there does not need to be a perfect map for t...

The food industry is highly competitive, and in order to survive, manufacturers must constantly innovate and match the ever changing tastes of consumers. A recent survey [1] found that 90% of the 13,000 new food products launched each year in the US fail within one year. Food companies are therefore changing the way new products are developed and launched, and this includes the use of intellige...

We present here an approach to battlefield situation assessment based on a level 2 fusion processing of incoming information via probabilistic Bayesian Belief Network technology. A belief network (BN) can be thought of as a graphical program script representing causal relationships among various battlefield concepts represented as nodes to which observed significant events are posted as evidenc...

We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.