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Modeling Multi-Dimensional Trust

Abstract: 

In a multi-agent system, a goal-driven agent is motivated to trust others when its resources are too limited to permit goal achievement in isolation. These goals may have multiple requirements (quality, completion timeliness, costs) influencing the reward received from goal achievement. To maximize the reward it receives from achieving a goal, an agent must consider the trustworthiness of potential partners relative to multiple dimensions accounting for multiple goal requirements (which determine rewards) and multiple partner constraints (which estimate partner behavior). This research endows agents with the ability to assert how much it should trust multiple facets of a potential partner’s behavior – the availability of the partner to deliver quality and on-time solutions within cost – in the context of multiple goal requirements. The partner selection algorithm
allows an agent to use multiple dimensions (goal requirements and estimated partner behavior) to estimate how much a potential partner can be trusted to help it achieve its goals. Experiments demonstrate that multi-dimensional trust modeling achieves higher rewards, as opposed to modeling partner trustworthiness with respect to a single requirement, in cases when goal requirements are multi-dimensional.
 

N. Gujral, D. DeAngelis, K. K. Fullam and K. S. Barber. Modeling Multi-Dimensional Trust. In the
Proceedings of The Workshop on Trust in Agent Societies at The Fifth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2006); Hakodate, Japan; May 8-12, 2006; pp. 35-41.

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Dave DeAngelis,
Jun 4, 2009, 1:09 PM
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