Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Therefore, serious flaws may go undetected and manifest themselves only after deployment. This problem may be tackled by applying formal methods to reason about business process models. In this paper we outline the main contributions in this application domain of (Armando et al. 2012), that uses the action-based planning language C and the Causal Calculator tool CCalc. C is used to specify a business process from the banking domain that is representative of an important class of business processes of practical relevance, and proved to be a rich and natural formal specification language in this domain. CCalc is then used to automatically solve three reasoning tasks that arise in this context. We also compare C with the SMV specification language used in model-checking: the comparison highlights some key advantages of C in the business process domain.

The fourth Modeling and Reasoning in Context (MRC) workshop was held on August 20–21, 2007, in conjunction with the Sixth International and Interdisciplinary Conference on Modeling and Using Context, at Roskilde University, Denmark. This year’s workshop included a special track on the role of contextualization in human tasks (CHUT). The overall goal of the workshop was to further the understanding, development, and application of AI methods for context-sensitive information technology.