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Learning and reasoning in complex coalition information environments: a critical analysis

Cerutti, Federico, Alzantot, Mustafa, Xing, Tianwei, Harborne, Daniel, Bakdash, Jonathan, Braines, Dave, Chakraborty, Supriyo, Kaplan, Lance, Kimmig, Angelika, Preece, Alun David, Raghavendra, Ramya, Sensoy, Murat and Srivastava, Mani 2018. Learning and reasoning in complex coalition information environments: a critical analysis. Presented at: Fusion 2018: 21st International Conference on Information Fusion, Cambridge, UK, 10-13 July 2018.

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Abstract

In this paper we provide a critical analysis with met- rics 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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:
Last Modified: 09 May 2018 20:08
URI: http://orca.cf.ac.uk/id/eprint/111242

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