| 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. |
| Authors |
- 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)
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| Date |
Sep-2018 |
| Venue |
2nd Annual Fall Meeting of the DAIS ITA, 2018 |
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| Variants |
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