Keynote Speakers - Benjamin Kuipers
How Can a Robot Learn the Foundations of Knowledge?
An embodied agent experiences the physical world through low-level sensory and motor interfaces (the "pixel level"). However, in order to function intelligently, it must be able to describe its world in terms of higher-level concepts such as places, paths, objects, actions, goals, plans, and so on (the "object level"). How can higher-level concepts such as these, that make up the foundation of commonsense knowledge, be learned from unguided experience at the pixel level? I will describe progress on providing a positive answer to this question.
This question is important in practical terms: As robots are developed with increasingly complex sensory and motor systems, and are expected to function over extended periods of time, it becomes impractical for human engineers to implement their high-level concepts and define how those concepts are grounded in sensorimotor interaction. The same question is also important in theory: Must the knowledge of an AI system necessarily be programmed in by a human being, or can the concepts at the foundation of commonsense knowledge be learned from unguided experience?
Benjamin Kuipers joined the University of Michigan in January 2009 as Professor of Computer Science and Engineering. Prior to that, he held an endowed Professorship in Computer Sciences at the University of Texas at Austin. He received his B.A. from Swarthmore College, and his Ph.D. from MIT.
He investigates the representation of commonsense and expert knowledge, with particular emphasis on the effective use of incomplete knowledge. His research accomplishments include developing the TOUR model of spatial knowledge in the cognitive map, the QSIM algorithm for qualitative simulation, the Algernon system for knowledge representation, and the Spatial Semantic Hierarchy models of knowledge for robot exploration and mapping. He has served as Department Chair at UT Austin, and is a Fellow of AAAI and IEEE.