Cerutti, F.Alzantot, M.Xing, T.Harborne, D.Bakdash, J. Z.Braines, D.Chakraborty, S.Kaplan, L.Kimmig, A.Preece, A.Raghavendra, R.Şensoy, MuratSrivastava, M.2020-04-202020-04-202018-09-05978-0-9964-5276-2http://hdl.handle.net/10679/6523https://doi.org/10.23919/ICIF.2018.8455458In 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.enginfo:eu-repo/semantics/restrictedAccessConference paper82282900049507190011410.23919/ICIF.2018.8455458Collective situational understandingArtificial intelligence for situational understandingCritical analysis of artificial intelligence techniques2-s2.0-85054089533