TY - GEN
T1 - MIP-Nets
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
AU - Amir, Ofra
AU - Grosz, Barbara J.
AU - Gajos, Krzysztof Z.
N1 - Publisher Copyright:
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85007233192&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85007233192
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 4192
EP - 4193
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI press
Y2 - 12 February 2016 through 17 February 2016
ER -