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How do you make decisions when there are way more possibilities than you can analyze? How do you decide under such information constraints?
Planning and decisionmaking with information constraints is at the heart of adaptive control, reinforcement learning, robotic path planning, experimental design, active learning, computational neuroscience and games. In most realworld problems, perfect planning is either impossible (computational intractability, lack of information, diminished control) or sometimes even undesirable (distrust, risk sensitivity, level of cooperation of the others). Recent developments have shown that a single method, based on the free energy functional borrowed from thermodynamics, provides a principled way of designing systems with information constraints that parallels Bayesian inference. This single method known in the literature under various labels such as KLcontrol, path integral control, linearlysolvable stochastic control, informationtheoretic bounded rationality is proving itself very general and powerful as a foundation for a novel class of probabilistic planning problems.
The goal of this workshop is twofold:
1) Give a comprehensive introduction to planning with information constraints targeted to a wide audience with machine learning background. Invited speakers will give an overview of the theoretical results and talk about their experience in applications to control, reinforcement learning, computational neuroscience and robotics.
2) Bring together the leading researchers in the field to discuss, compare and unify their approaches, while interacting with the audience. Recent advances will be presented in a poster session based on contributed material. Furthermore, ample space will be given to state open questions and to sketch future directions.
Author Information
Hilbert J Kappen (Radboud University)
Naftali Tishby (The Hebrew University Jerusalem)
Naftali Tishby, is a professor of computer science and the director of the Interdisciplinary Center for Neural Computation (ICNC) at the Hebrew university of Jerusalem. He received his Ph.D. in theoretical physics from the Hebrew University and was a research staff member at MIT and Bell Labs from 1985 to 1991. He was also a visiting professor at Princeton NECI, the University of Pennsylvania and the University of California at Santa Barbara. Dr. Tishby is a leader of machine learning research and computational neuroscience. He was among the first to introduce methods from statistical physics into learning theory, and dynamical systems techniques in speech processing. His current research is at the interface between computer science, statistical physics and computational neuroscience and concerns the foundations of biological information processing and the connections between dynamics and information.
Jan Peters (TU Darmstadt & MPI Intelligent Systems)
Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the MaxPlanck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis RunnerUp Award, the Robotics: Science & Systems  Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society‘s Early Career Award as well as numerous best paper awards. In 2015, he was awarded an ERC Starting Grant. Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master‘s degrees in these disciplines as well as a Computer Science PhD from USC.
Evangelos Theodorou (Georgia Tech)
David H Wolpert (Santa Fe Institute)
Pedro Ortega (DeepMind)
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