Information graphs and their use for Bayesian network graph construction

Abstract

In this paper, we present the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where ‘deduction’ is used for defeasible ‘forward’ inference. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Our IG-formalism is intended to facilitate the construction of formal representations within AI systems by serving as an intermediary formalism between analyses performed using informal reasoning tools and formalisms that allow for formal evaluation. In this paper, we investigate the use of the IG-formalism as an intermediary formalism in facilitating Bayesian network (BN) graph construction. We propose a structured approach for automatically constructing from an IG a directed BN graph, together with qualitative constraints on the probability distribution represented by the BN. Moreover, we prove a number of formal properties of our approach and identify assumptions under which the construction of an initial BN graph can be fully automated.