Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning

Authors:

Max Henrion
Department of Engineering and Public Policy
Carnegie Mellon University and
Rockwell International Science Center
Palo Alto Laboratory
email: henrion@camis.stanford.edu
(currently with:
Lumina Decision Systems)

Marek J. Druzdzel
Department of Engineering and Public Policy
Carnegie Mellon University
(currently with:
University of Pittsburgh
Department of Information Sciences and Telecommunications
and Intelligent Systems Program
e-mail: marek@sis.pitt.edu)

Abstract:

Comprehensible explanations of probabilistic reasoning are a prerequisite for wider acceptance of Bayesian methods in expert systems and decision support systems. A study of human reasoning under uncertainty suggests two different strategies for explaining probabilistic reasoning specially attuned to human thinking: The first, qualitative belief propagation, traces the qualitative effect of evidence through a belief network from one variable to the next. This propagation algorithm is an alternative to the graph reduction algorithms of Wellman (1988) for inference in qualitative probabilistic networks. It is based on a qualitative analysis of intercausal reasoning, which is a generalization of Pearl's "explaining away", and an alternative to Wellman's definition of qualitative synergy. The other, Scenario-based reasoning, involves the generation of alternative causal "stories" accounting for the evidence. Comparing a few of the most probable scenarios provides an approximate way to explain the results of probabilistic reasoning. Both schemes employ causal as well as probabilistic knowledge. Probabilities may be presented as phrases and/or numbers. Users can control the style, abstraction and completeness of explanations.