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Supporting reasoning with different types of evidence in intelligence analysis

Toniolo, Alice, Norman, Timothy J., Etuk, Anthony, Cerutti, Federico, Ouyang, Robin Wentao, Srivastava, Mani, Oren, Nir, Dropps, Timothy, Allen, John A. and Sullivan, Paul 2015. Supporting reasoning with different types of evidence in intelligence analysis. Presented at: 4th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, Turkey, 4-8 May 2015. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), Bordini, Elkind, Weiss, Yolum (eds.), May 4{8, 2015, Istanbul, Turkey.

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Abstract

The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sensemaking agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information.

Item Type: Conference or Workshop Item (Speech)
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Last Modified: 04 Jun 2017 09:01
URI: http://orca.cf.ac.uk/id/eprint/89577

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