Modeling human ad hoc coordination
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krafft-et-al-2016-modeling-human-ad-hoc-coordination.pdf
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Author(s)
Krafft, Peter
Baker, Christopher Lawrence
Pentland, Alex Paul
Tenenbaum, Joshua B
Date Issued
February 2016
Journal
Thirtieth AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Krafft, Peter M. et al."Modeling human ad hoc coordination." Thirtieth AAAI Conference on Artificial Intelligence, February 12-17 2016, Phoenix, Arizona, USA, Association for the Advancement of Artificial Intelligence, February 2016 © 2016 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-Agent collaboration.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://dl.acm.org/citation.cfm?id=3016430
