Learning and Reasoning in Complex Coalition Information Environments: a Critical Analysis

Abstract In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight—i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight—i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.
  • Federico Cerutti (Cardiff)
  • Moustafa Alzantot (UCLA)
  • Tianwei Xing (UCLA)
  • Dan Harborne (Cardiff)
  • Jon Bakdash (ARL)
  • Dave Braines (IBM UK)
  • Supriyo Chakraborty (IBM US)
  • Lance Kaplan (ARL)
  • Angelika Kimmig (Cardiff)
  • Alun Preece (Cardiff)
  • Ramya Raghavendra (IBM US)
  • Mani Srivastava (UCLA)
Date Sep-2018
Venue 2nd Annual Fall Meeting of the DAIS ITA, 2018