Generative Policies for Coalition Systems – A Symbolic Learning Framework

Abstract Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.
  • Elisa Bertino (Purdue)
  • Alessandra Russo (Imperial)
  • Mark Law (Imperial)
  • Seraphin Calo (IBM US)
  • Irene Manatos (IBM US)
  • Dinesh Verma (IBM US)
  • Amani Abu Jabal (Purdue)
  • Daniel Cunnington (IBM UK)
  • Geeth de Mel (IBM UK)
  • Graham White (IBM UK)
  • Jorge Lobo
  • Greg Cirincione (ARL)
Date Jul-2019
Venue 2019 39th IEEE International Conference on Distributed Computing Systems