A Risk Reduction Framework for Dynamic Workflows

Workflows tend to fail in real-world scenarios due to the uncertain/unreliable sensory information which sometimes needs to be updated during the execution of workflows. In a logic based framework, these dynamic predicates that can be updated are called non-monotonic predicates (NMPs). In this paper, we focus on reducing the risk of a given workflow due to the NMPs in that workflow. The main idea is to synthesize a backup workflow by augmenting the main workflow without introducing new NMPs. The backup workflow is generated by using expected values of NMPs if necessary instead of given values. The expected values are calculated from the execution history or provided by a domain expert. It is argued that total risk reduces to the square root of the main workflow itself.