Information graphs and their use for Bayesian network graph construction

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