Professor and Associate Head of
Research
School of Electrical Engineering and Computer
Science
Oregon State University
Office Location: 2071 Kelley
Engineering Center
(541) 737-9202 (office) (I never check phone messages, send an email
instead)
(541) 737-1300 (fax)
E-mail: alan.fern@oregonstate.edu
Postal Address: Kelley Engineering Center, Corvallis, OR 97330-5501, U.S.A.
Quick Links: Teaching Publications
(Google
Scholar) Students
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B.S. Electrical Engineering, University of Maine, 1997 |
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M.S. Computer Engineering, Purdue University, 2000 |
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Ph.D. Computer Engineering, Purdue University, 2004 (advised by Robert Givan) |
o
Lab
Exercises: Distributed AI using Ray
My primary research interests are
in the field of artificial intelligence, where I focus on the sub-areas of
machine learning and automated planning. I am particularly interested in the
intersection of these areas. Some example projects include:
Explainable Artificial Intelligence: It is becoming increasingly common for autonomous and semi-autonomous systems, such as UAVs, robots, and virtual agents, to be develop via a combination of traditional programming and machine learning. Currently, acceptance testing of these systems is problematic due to the black box nature of machine-learned components, which does not allow testers to understand the rationale behind the learned decisions. Our research will develop the new paradigm of explanation-informed acceptance testing (xACT), which will allow testers to not only observe and evaluate the behavior of machine-learned systems, but to also evaluate explanations of the decisions leading to that behavior. As a result, the xACT paradigm allows testers to determine whether machine-learned systems are making decisions "for the right reasons", which provides stronger justification for trusting the system in (semi-)autonomous operation. The public will benefit from this technology via the availability of more understandable and, in turn, trustworthy (semi-)autonomous systems for complex applications in defense, industry, and everyday life.
Machine Common Sense: This DARPA sponsored OSU-led
project is in collaboration with Behavior Psychologist, Karen Adolph at NYU, and roboticist
Tucker Hermans at the
University of Utah. We are studying and developing learning and reasoning
techniques to enable AI systems to exhibit common sense reasoning and planning
capabilities on par with those of an 18-month infant. A key aspect of our
approach is to study how to effectively combine the representation-learning
capabilities of deep neural network with the powerful reasoning capabilities of
state-of-the-art AI planning and reasoning engines.
Learning and Planning for Bipedal Robot Locomotion: In collaboration with Jonathan Hurst and his Dynamic Robot Lab (DRL) we are studying techniques for training the bipedal robot Cassie to exhibit agile biped locomotion. We study learning and planning for both low-level behavior and high-level planned behaviors.
Anomaly Detection and Explanation: We study how to best detect and explain anomalies with a particular focus on security applications and interaction with end-user analysts.
Learning for Sequential Decision Making: We study various aspects of learning in the context of sequential decision making. This includes reinforcement learning and learning for the purpose of speeding up planners (speedup learning) based on prior planning experience.
Former
Students