Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks
This tool aids researchers and practitioners in comparing and applying different uncertainty theories, but it is incremental as it builds on existing local computation methods.
The authors introduced Pulcinella, a tool for propagating uncertainty in valuation networks using Shafer and Shenoy's Local Computation technique, which supports probabilities, belief functions, Boolean values, and possibilities, and allows user-defined specializations, with examples analyzed to compare theories and assess their adequacy for specific problems.
We present PULCinella and its use in comparing uncertainty theories. PULCinella is a general tool for Propagating Uncertainty based on the Local Computation technique of Shafer and Shenoy. It may be specialized to different uncertainty theories: at the moment, Pulcinella can propagate probabilities, belief functions, Boolean values, and possibilities. Moreover, Pulcinella allows the user to easily define his own specializations. To illustrate Pulcinella, we analyze two examples by using each of the four theories above. In the first one, we mainly focus on intrinsic differences between theories. In the second one, we take a knowledge engineer viewpoint, and check the adequacy of each theory to a given problem.