Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2010

Abstract

As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these problems, the agents and humans optimize their constraints in a distributed manner. This paper makes two key contributions: (a) Proves theoretical properties of the algorithm used (named DSA) for solving distributed constraint optimization problems, which ensures robustness against human biases; and (b) Empirically illustrates that the effect of human biases on team performancefor different problem settings and for varying team sizes is not significant. Both our theoretical and empirical studies support the fact that the solutions provided by DSA for mid to large sized teams are very robust to the common types of human biases.

Keywords

human-agent team, distributed constraint optimization problem, human biases effect, resource allocation problems

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology: Toronto, Canada, August 31 - September 3

First Page

327

Last Page

334

ISBN

9780769541914

Identifier

10.1109/WI-IAT.2010.104

Publisher

IEEE

City or Country

Pistacaway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/WI-IAT.2010.104

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