Human-understandable inference of causal relationships

We present a method for aiding humans in understanding causal relationships between entities in complex systems via a simplified calculus of facts and rules. Facts are human-readable subject-verb-object statements about system entities, interpreted as (entity-relationship-entity) triples. Rules construct new facts via implication and “weak transitive” rules of the form “If X r Y and Y s Z then X t Z”, where X, Y, and Z are entities and r, s, and t are relationships. Constraining facts and rules in this way allows one to treat abductive inference as a graph computation, to quickly answer queries about the most related entities to a chosen one, and to explain any derived fact as a shortest chain of base facts that were used to infer it. The resulting chain is easily understood by a human without the details of how it was inferred. This form of simplified reasoning has many applications in human understanding of knowledge bases, including causality analysis, troubleshooting, and documentation search, and can also be used to verify knowledge bases by examining the consequences of recorded facts.