People collaborate in carrying out such complex activities as treating patients, co-Authoring documents and developing software. While technologies such as Dropbox and Github enable groups to work in a distributed manner, coordinating team members' individual activities poses significant challenges. In this paper, we formalize the problem of "information sharing in loosely-coupled extended-duration teamwork". We develop a new representation, Mutual Influence Potential Networks (MIP-Nets), to model collaboration patterns and dependencies among activities, and an algorithm, MIP-DOI, that uses this representation to reason about information sharing.
CITATION STYLE
Amir, O., Grosz, B. J., & Gajos, K. Z. (2016). MIP-Nets: Enabling information sharing in loosely-coupled teamwork. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 4192–4193). AAAI press. https://doi.org/10.1609/aaai.v30i1.9946
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