Causality-Based Reasoning for Cognitive Factories

We propose the use of causality-based formal representation and automated reasoning methods to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as, optimal planning and diagnostic reasoning. We introduce algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We discuss how these algorithms can be embedded in an execution and monitoring framework, and show their applicability on an intelligent painting factory scenario.