AI Seminar Series 2022: Elham Parhizkar

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On April 22, Elham Parhizkar, a postdoctoral fellow at the University of Alberta, presented "Efficient and Robust Methods for Computing Trust in Multi-Agent Systems" at the AI Seminar.

Trust and reputation systems constitute an active branch of research in multi-agent systems. In various application domains, agents interact with one another to collect information, goods, or services that help complete a set task. For such interactions to be largely successful, agents try to estimate how trustworthy other individual agents are. To evaluate the trustworthiness of an agent in a multi-agent system, one often combines two types of trust information: direct trust information derived from one's interactions with that agent, and indirect trust information based on advice from other agents.

In this presentation, Parhizkar proposes a new and easy-to-implement method for computing indirect trust, based on a simple prediction from an expert advice strategy often used in online learning. This method either competes with or outperforms all tested systems in most of the simulated settings while scaling substantially better. The research team also provides the first systematic study on when it is beneficial to combine the two types of trust as opposed to relying on only one of them. Finally, she proposes a method based on the Page-Hinkley statistics to handle the dynamic behaviour of agents in a multi-agent system.

Watch the full presentation below:

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