Abstract
Multi-dimensional trustworthiness assessments have been shown significantly beneficial to agents when selecting appropriate teammates to achieve a given goal. Reliability, quality, availability, and timeliness define the behavioral constraints of the proposed multi-dimensional trust (MDT) model. Given the multi-dimensional trust model in this research, an agent learns to identify the most beneficial teammates given different situations 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 reinforcement learning mechanism with clustering technique 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, and consequently, teammate selection based on the situation offer a superior means of finding the best teammates for goal achievement.
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Ahn, J., DeAngelis, D., Barber, K.S. (2008). Teammate Selection Using Multi-dimensional Trust and Attitude Models. In: Falcone, R., Barber, S.K., Sabater-Mir, J., Singh, M.P. (eds) Trust in Agent Societies. TRUST 2008. Lecture Notes in Computer Science(), vol 5396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92803-4_1
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DOI: https://doi.org/10.1007/978-3-540-92803-4_1
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