Knowledge representation standards and interchange formats for causal graphs

Abstract

In many domains, automated reasoning tools must represent graphs of causally linked events. These include fault-tree analysis, probabilistic risk assessment (PRA), planning, and procedures, medical reasoning about disease progression, and functional architectures. Each field has its own requirements for the representation of causation, events, actors and conditions. In no domain has a generally accepted interchange format emerged. This paper makes progress towards interoperability across the wide range of causal analysis methodologies. We survey existing practice and emerging interchange formats across these fields. Setting forth a set of terms and concepts that are broadly shared across the domains, we examine the several ways in which current practice represents them. Some phenomena are difficult to represent or to analyze in several domains. These include mode transitions, reachability analysis, positive and negative feedback loops, conditions correlated but not causally linked and bimodal probability distributions. We work through examples and contrast the differing methods for addressing them. We detail recent work in knowledge interchange formats for causal trees in aerospace analysis applications in early design, safety and reliability. Several examples are discussed, with a particular focus on reachability analysis and mode transitions. We generalize the aerospace analysis work across the several other domains. We also recommend features and capabilities for the next generation of causal knowledge representation standards