Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks

TL;DR

Pulcinella is a versatile tool that propagates uncertainty across various theories like probabilities and belief functions, enabling comparison and customization for different valuation network applications.

Contribution

Introduces Pulcinella, a flexible, general-purpose tool for propagating uncertainty in valuation networks, supporting multiple theories and user-defined specializations.

Findings

Analyzes differences between uncertainty theories.

Assesses the suitability of each theory for specific problems.

Demonstrates Pulcinella's application in example scenarios.

Abstract

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.