MIP-Nets: Enabling information sharing in loosely-coupled teamwork

Ofra Amir, Barbara J. Grosz, Krzysztof Z. Gajos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
Pages4192-4193
Number of pages2
ISBN (Electronic)9781577357605
StatePublished - 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period12/02/1617/02/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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