Debra K. Zarley, Yen-Teh Hsia, Glenn Shafer
The Dempster-Shafer theory of belief functions [Shafer 1976] is an intuitively appealing formalism for reasoning under uncertainty. Several AI implementations have been undertaken [e.g., Lowrance et al. 1986, Biswas and Anand 1987], but the computational complexity of Dempster’s rule has limited the usefulness of such implementations. With the advent of efficient propagation schemes in Markov trees [Shafer et al. 1987], the time is ripe for more powerful systems. This paper discusses DELIEF (Design of bELIEFs), an interactive system that allows the design of belief function arguments via a simple graphical interface. The user of DELIEF constructs a graph, with nodes representing variables and edges representing relations among variables. This graph serves as a default knowledge schema. The user enters belief functions representing evidence pertinent to the individual variables in a specific situation, and the system combines them to obtain beliefs on all variables. The schema may be revised and re-evaluated until the user is satisfied with the result. The Markov tree used for belief propagation is displayed on demand. The system handles Bayesian causal trees [Pearl 1986] as a special case, and it has a special user interface for this case.