Identifying beneficial teammates using multi-dimensional trust

Authors:
Jaesuk Ahn;Xin Sui;David DeAngelis;K. Suzanne Barber
Affiliations:
The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX
Venue:
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Year:
2008

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Abstract

Multi-agent teams must be capable of selecting the most beneficial teammates for different situations. Multi-dimensional trustworthiness assessments have been shown significantly beneficial to agents when selecting appropriate teammates to achieve a given goal. Reliability, quality, availability, timeliness and compatibility define the behavioral constraints of the multidimensional trust (MDT) model. Given the MDT model in this research, an agent learns to identify the most beneficial teammates by prioritizing each dimension differently. An agent's attitudes towards rewards, risks and urgency are used to drive an agent's prioritization of dimensions in a MDT model. Each agent is equipped with a Temporal-Difference (TD) learning mechanism with tile coding to identify its optimal set of attitudes and change its attitudes when the environment changes. Experimental results show that changing attitudes to give preferences for respective dimensions in the MDT offers a superior means to finding the best teammates for goal achievement.