Active sensing as bayes-optimal sequential decision-making
Journal
Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013
Pages
12-21
Date Issued
2013
Author(s)
Ahmad S
Abstract
Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that optimize abstract statistical objectives such as information maximization (Infomax) [Butko and Movellan, 2010] or one-step look-ahead accuracy [Najemnik and Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and sensor repositioning cost. We simulate these algorithms on a simple visual search task to illustrate scenarios in which context-sensitivity is particularly beneficial and optimization with respect to generic statistical objectives particularly inadequate. Motivated by the geometric properties of the CDAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate a more complex search problem involving peripheral vision, and we notice that the performance advantage of C-DAC over generic statistical policies is even more evident in this scenario.
Other Subjects
Active controller; Computational neuroscience; Context dependent; Context sensitivity; Geometric properties; Information maximization; Peripheral vision; Sensory processing; Algorithms; Artificial intelligence; Optimization
Type
conference paper