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IBPRIA
2009
Springer

Inference and Learning for Active Sensing, Experimental Design and Control

9 years 7 months ago
Inference and Learning for Active Sensing, Experimental Design and Control
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Examples include, among others, gaze planning and other active vision problems, active learning, sensor and actuator placement and coordination, intelligent humancomputer interfaces, and optimal control. Following this remark, we present a common inference and learning framework for attacking these problems. We demonstrate this approach on three examples: (i) active sensing with nonlinear, non-Gaussian, continuous models, (ii) optimal experimental design to discriminate among competing scientific models, and (iii) nonlinear optimal control. 1 The Principle of Maximum Expected Utility Broadly speaking, utility reflects the preferences of an agent. That is, if outcome o1 is preferred to o2 (i.e. o1 o2), we say that o1 has higher utility than o2. More formally, let o1 o2 denote weak preference, o1 o2 denote stron...
Hendrik Kück, Matthew Hoffman, Arnaud Doucet,
Added 25 Jul 2010
Updated 25 Jul 2010
Type Conference
Year 2009
Where IBPRIA
Authors Hendrik Kück, Matthew Hoffman, Arnaud Doucet, Nando de Freitas
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