Tech Reports
Tech Report KMI-06-07 Abstract
Co-OPR: Design and Evaluation of Collaborative Sensemaking and Planning Tools for Personnel Recovery
Techreport ID: KMI-06-07
Date: 2006
Author(s): Austin Tate, Simon Buckingham Shum, Jeff Dalton, Clara Mancini, Albert Selvin
Personnel recovery teams must operate under intense pressure, taking into account not only hard logistics, but 'messy' factors such as the social or political implications of a decision. The Collaborative Operations for Personnel Recovery (Co-OPR) project has developed decision-support for sensemaking in such scenarios, seeking to exploit the complementary strengths of human and machine reasoning. Co-OPR integrates the Compendium sensemaking-support tool for real time information and argument mapping, with the I-X artificial intelligence planning and execution framework to support group activity and collaboration. Both share a common model for dealing with issues, the refinement of options for the activities to be performed, handling constraints and recording other information. The tools span the spectrum from being very flexible with few constraints on terminology and content, to knowledge-based relying on rich domain models and formal conceptual models (ontologies). In a personnel recovery experimental simulation of an UN peacekeeping operation, with roles played by military planning staff, the Co-OPR tools were judged by external evaluators to have been very effective.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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