Policy-Based Autonomic Data Governance

Generative policies have been proposed as a mechanism to learn the constraints and preferences of a system—especially complex systems such as the ones found in coalitions—in a given context so that the system can adapt to unexpected changes seamlessly, thus achieving the system goals with minimal human intervention. Generative policies can help a coalition system to be more effective when working in a distributed, continuously transforming environment with a diverse set of members, resources, and tasks. Learning mechanisms based on logic programming, e.g., Inductive Logic Programming (ILP), have several properties that make them suitable and attractive for the creation and adaptation of generative policies, such as the ability to learn a general model from a small number of examples, and being able to incorporate existing background knowledge. ILP has recently been extended with the introduction of systems for Inductive Learning of Answer Set Programs (ILASP) which are capable of supporting automated acquisition of complex knowledge such as constraints, preferences and rule-based models. Motivated by the capabilities of ILASP, we present AGENP, an Answer Set Grammar-based Generative Policy Framework for Autonomous Managed Systems (AMS) that aims to support the creation and evolution of generative policies by leveraging ILASP. We describe the framework components, i.e., inputs, data structures, mechanisms to support the refinement and instantiation of policies, identification of policy violations, monitoring of policies, and policy adaptation according to changes in the AMS and its context. Additionally, we present the main work-flow for the global and local refinement of policies and their adaptation based on Answer Set Programming (ASP) for policy representation and reasoning using ILASP. We then discuss an application of the AGENP framework and present preliminary results. c © Springer Nature Switzerland AG 2019 S. Calo et al. (Eds.): PADG 2018, LNCS 11550, pp. 3–20, 2019. https://doi.org/10.1007/978-3-030-17277-0_1