Joel D. Young and Eugene Santos Jr.
Department of Electrical and Computer Engineering
Air Force Institute of Technology
Wright-Patterson AFB, OH 45433-7765
jdyoung@afit.af.mil, esantos@afit.af.mil
A Bayesian network is a directed acyclic graph in which nodes are random variables and the edges indicate that the source exerts direct causal influence on the destination. A problem with the Bayesian network is that there is no natural mechanism for representing temporal relations between and within the random variables. This paper introduces a new technique for representing when a random variable holds a particular state, e.g. when an event happens, as well as techniques for applying temporal constraints (precedes, during, etc.) to the edges. Allen's interval structure is used to provide the formal basis. A restricted model is presented along with a corresponding inferencing algorithm based on a linear constraint formulation.