Rate My Professor Nathan Griffiths

Griffiths specializes in multi-agent systems, trust and reputation mechanisms, social network analysis, and machine learning. His ongoing research explores supporting cooperation via reputation and provenance reporting, machine learning for intelligent vehicle applications such as telemetry and mobility data mining, the emergence of conventions and norms in multi-agent interactions, and influence manipulation within social networks. He has contributed significantly to these areas, with his work cited over 2,698 times according to Google Scholar. Notable publications include 'Emergence of Norms in Interactions with Complex Rewards' (2023, Autonomous Agents and Multi-Agent Systems), 'Convention Emergence with Congested Resources' (2023, SN Computer Science), 'A Reputation-based Framework for Honest Provenance Reporting' (2022, ACM Transactions on Internet Technology), 'Feature Selection for Supervised Learning and Compression' (2022, Applied Artificial Intelligence), 'The BOSS Online Submission and Assessment System' (2005, co-authored with Mike Joy and Russell Boyatt), and 'The Use of Learning Objects and Learning Styles in a Multi-Agent Education System' (2007, co-authored with Shanghua Sun and Mike Joy). Griffiths teaches the CS255 Artificial Intelligence module and has supervised PhD students in related fields.