Keywords: belief networks, uncertainty in reasoning, decision support, probabilistic modelling, simulation modelling, performance improvement
Belief networks for engineering applications
A relatively new form of artificial intelligence, namely belief networks, provides flexible modelling structures for capturing and evaluating uncertainty. The belief network consists of nodes to model the variables of the domain, and arcs to represent conditional dependence between variables. The development of a belief network requires four major steps: variable definition, identification of conditional relationships, definition of the states of the variables, and determination of the probabilistic values of the conditional relationships. The evaluation of a singly connected belief network is provided. Two applications for belief networks are discussed. One application involves the integration of a belief network with computer simulation resulting in an automated system for performance improvement. The second application is focused on assessing productivity of construction operations.