Professor Matthew E. Taylor

Matthew E. Taylor, Ph.D. is Assistant Professor, Allred Distinguished Professorship in Artificial Intelligence, School of Electrical Engineering and Computer Science, Washington State University. He is a member of the IFAAMAS Board of directors.
 
Matt graduated magna cum laude with a double major in computer science and physics from Amherst College in 2001. After working for two years as a software developer, he began his Ph.D. work at the University of Texas at Austin with an MCD fellowship from the College of Natural Sciences. He earned his doctorate from the Department of Computer Sciences in the summer of 2008, supervised by Peter Stone.
 
Matt then completed a two year postdoctoral research position at the University of Southern California with Milind Tambe and spent 2.5 years as an assistant professor at Lafayette College in the computer science department. He is currently an assistant professor at Washington State University in the School of Electrical Engineering and Computer Science and is a recipient of the National Science Foundation CAREER award. Current research interests include intelligent agents, multi-agent systems, reinforcement learning, transfer learning, and robotics.
 
His research focuses on agents, physical or virtual entities that interact with their environments. His main goals are to enable individual agents, and teams of agents, to

1. learn tasks in real world environments that are not fully known when the agents are designed;
2. perform multiple tasks, rather than just a single task; and
3. allow agents to robustly coordinate with, and reason about, other agents.

Additionally, Matt is interested in exploring how agents can learn from humans, whether the human is explicitly teaching the agent, the agent is passively observing the human, or the agent is actively cooperating with the human on a task.
 
He coauthored Transfer Learning via Inter-Task Mappings for Temporal Difference Learning, Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning, DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks, Mitigating Multi-Path Fading in a Mobile Mesh Network, and Feature Selection and Policy Optimization for Distributed Instruction Placement Using Reinforcement Learning, Read the full list of his publications!
 
Watch Towards knowledge transfer between robots: Computers teach each other Pac-Man. Read Takes one to teach one: Computers teach each other how to play video games and Robot school starts at Pac-Man, ends with world domination. Read his LinkedIn profile.