Supporting reasoning with different types of evidence in intelligence analysis
- Submitting institution
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University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 252071858
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (AAMAS'15)
- First page
- 781
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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9
- Research group(s)
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A - Artificial Intelligence
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In intelligence analysis, extracting reliable hypotheses from heterogenous information is a complex and cognitively demanding task and mostly performed manually. Informed by experienced analysts, CISpaces complements human expertise with automated reasoning to produce and evaluate hypotheses. CISpaces demonstrates a novel model of reasoning about typical and diversely sourced intelligence and its provenance. This publication is from the project "Collaborative Intelligence Analysis" led by Norman, a collaboration with UCLA and Honeywell, funded by UK-MoD and US-DoD through the NIS-ITA programme. Research from this project was identified by both US and UK governments as a key highlight from the 10-year programme (http://nis-ita.org/capstone).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
