Computing Science Seminar. Dr. Meneguzzi on "A Bayesian approach to norm identification"
Abstract: When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that affect it. Existing solutions to this norm identification problem make use of observations of either norm compliant, or norm violating, behaviour. Thus, they assume an extreme situation where norms are typically violated, or complied with. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. We evaluate our approach’s effectiveness empirically and compare its accuracy to existing approaches. By utilising both types of behaviour, we not only overcome a major limitation of such approaches, but also obtain improved performance over the state of the art, allowing norms to be learned with fewer observations.
Bio: Dr. Felipe Meneguzzi is an associate professor/senior lecturer at Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Brazil. Prior to that appointment he was a Project Scientist at the Robotics Institute at Carnegie Mellon University working under Professor Katia Sycara as part of the US/UK funded International Technology Alliance. Felipe obtained his PhD from King's College London in 2009. His current research interests include plan recognition, hybrid planning and norm reasoning, and he has published extensively in venues such as ECAI, IJCAI and AAMAS.
- Speaker
- Dr. Felipe Meneguzzi
- Hosted by
- Dr. Nir Oren
- Venue
-
Meston 311
