Incremental Tradeoff Resolution in Qualitative Probabilistic Networks

TL;DR

This paper introduces two incremental methods that combine qualitative and numeric probabilistic reasoning to resolve tradeoffs in Bayesian networks, improving interpretability and potentially reducing computational costs.

Contribution

It presents novel incremental techniques for resolving tradeoffs in qualitative probabilistic reasoning by combining marginalization and bound evaluation methods.

Findings

Both methods effectively resolve ambiguous qualitative relationships.

The approaches can be more computationally efficient than purely numeric methods.

Incremental refinement improves the accuracy of qualitative inferences.

Abstract

Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods.