Active Sensing as Bayes-Optimal Sequential Decision Making
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 & Movellan, 2010] or one-step look-ahead accuracy [Najemnik & Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and effort. 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 C-DAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate the more complex problem involving peripheral vision, and we notice that the difference between C-DAC and statistical policies becomes even more evident in this scenario.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSensitivitySequential Decision MakingSimilar Papers 제목 키워드 기반
Active Sensing as Bayes-Optimal Sequential Decision Making
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.…
Decision MakingSensitivitySequential Decision MakingGaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian opti…
Active LearningBayesian OptimizationAnomaly Detection via Learning-Based Sequential Controlled Sensing
In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the p…
Anomaly DetectionDecision MakingDeep Reinforcement LearningQ-LearningTargeted Active Learning for Bayesian Decision-Making
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy is not the end goal when the result…
Active LearningDecision MakingNear Optimal Bayesian Active Learning for Decision Making
How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Inst…
Active LearningDecision MakingExperimental Design