Learning and Multiagent Reasoning for Autonomous Agents (2007)
One goal of Artificial Intelligence is to enable the creation of robust, fully autonomous agents that can coexist with us in the real world. Such agents will need to be able to learn, both in order to correct and circumvent their inevitable imperfections, and to keep up with a dynamically changing world. They will also need to be able to interact with one another, whether they share common goals, they pursue independent goals, or their goals are in direct conflict. This paper presents current research directions in machine learning, multiagent reasoning, and robotics, and advocates their unification within concrete application domains. Ideally, new theoretical results in each separate area will inform practical implementations while innovations from concrete multiagent applications will drive new theoretical pursuits, and together these synergistic research approaches will lead us towards the goal of fully autonomous agents.
In The 20th International Joint Conference on Artificial Intelligence, pp. 13-30, January 2007.

Peter Stone Faculty pstone [at] cs utexas edu