计算机科学数学硕士-人工智能

计算机科学学院提供的研究生课程可导致数学硕士（MMath）或博士学位。人工智能小组在人工智能的许多领域进行研究。该小组对智能交互模型抱有浓厚的兴趣
The David R. Cheriton School of Computer Science has an international reputation in teaching, academics, research, and employment. We attract exceptional students from all over the world to study and conduct research with our award-winning faculty. You can participate in research projects in a wide variety of topics with our internationally acclaimed researchers. Our research spans the field of computer science, from core work on systems, theory and programming languages to human-computer interaction, DNA and quantum computing to theoretical and applied machine learning, just to name a few. As a graduate student, you will: Access research-intensive lab spaces. Gain the opportunity to publish your work in top conferences and journals. Present at premier conferences in front of peers, industry leaders, researchers, and experts in your field. As a graduate student, you will have the independence to pursue your preferred area of research with a faculty supervisor, or complete eight courses to fulfill your degree requirements through the coursework option<br><br>The Artificial Intelligence Group conducts research in many areas of artificial intelligence. The group conducts research on models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, decision-theoretic planning and learning, and machine learning. Integrating natural language processing models and user models to produce more effective human-computer interaction. This includes designing interfaces that allow for mixed-initiative interaction. Applications include interface agents, electronic commerce and recommender systems. Studying how computational limitations influence strategic behaviour in multi-agent systems, as well as developing approaches to overcome computational issues that arise in practical applications of mechanism design and game theory. Designing systems of collaborative problem-solving agents, with an emphasis on issues of communication and co-ordination for applications of multi-agent systems to the design of effective electronic marketplaces and adjustable autonomy systems. Modelling trust, reputation and incentives in multi-agent systems, including the use of social networks.