UTCS AI Colloquia - Mohan Sridharan, Assistant Professor, Texas Tech University, "Integrating Answer Set Programming and Probabilistic Planning on Robots," ACE 2.402

Contact Name: 
Karl Pichotta
Location: 
ACE 2.402
Date: 
Feb 1, 2013 3:00pm - 4:00pm

Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties

Host: Vladimir Lifschitz and Peter Stone

Talk Abstract: To collaborate with humans, robots need the ability to represent, reason with and revise domain knowledge adapt sensing and processing to the task at hand and learn from human feedback. In this talk, I describe the integration of non-monotonic logic programming and probabilistic decision making to address these challenges. Specifically, Answer Set Programming (ASP) is used to represent, reason with and revise domain knowledge obtained from sensor inputs and human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to adapt visual sensing and information processing to the task at hand. All algorithms are evaluated in simulation and on wheeled robots localizing target objects in indoor domains.

Speaker Bio: Mohan Sridharan is an Assistant Professor of Computer Science at Texas Tech University. Prior to his current appointment, he was a Research Fellow in the School of Computer Science at University of Birmingham (UK), working on the EU Cognitive Systems (CoSy) project between August 2007 and October 2008. He received his Ph.D. (Aug 2007) in Electrical and Computer Engineering at The University of Texas at Austin. Dr.Sridharan's research interests include machine learning, planning, computer vision and cognitive science, as applied to autonomous mobile robots. Furthermore, he is interested in designing learning and inference algorithms for big data domains characterized by a significant amount of uncertainty.

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