Jaesuk Ahn

Attitude Driven Team Formation using Multi-Dimensional Trust

When agents form a team to solve a given problem, a critical step in improving performance is selecting beneficial teammates by identifying the helpfulness of other agents. To maximize its performance, an agent must consider the trustworthiness of potential teammates relative to multiple behavioral constraints. This multidimensional trustworthiness assessment is shown to be of significant benefit in solving the team formation problem. This research introduces the concept of attitude to assert how much an agent should trust other agents by identifying the most influential facet among multiple trustworthiness assessments. In this sense, attitudes define how an agent selects beneficial teammates given different situations. In addition, this research shows how those attitudes are learned and aid in teammate selection.

Agent trust evaluation and team formation in heterogeneous organizations

This demonstration highlights different aspects of the bottom-up assembly of multi-agent teams; illustrating trust evaluation of potential partners via experience- and reputation-based trust models, multi-dimensional trust evaluation of potential partners, task selection through personality-based modeling and team selection strategies that maximize a teams ability to function in dynamic environments. The demonstration format will be a software live demo with supporting slide shows.

Teammate Selection Using Multi-dimensional Trust and Attitude Models

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 agents attitudes towards rewards, risks and urgency are used to drive an agents 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.