68 research outputs found
Abstraction in decision-makers with limited information processing capabilities
A distinctive property of human and animal intelligence is the ability to
form abstractions by neglecting irrelevant information which allows to separate
structure from noise. From an information theoretic point of view abstractions
are desirable because they allow for very efficient information processing. In
artificial systems abstractions are often implemented through computationally
costly formations of groups or clusters. In this work we establish the relation
between the free-energy framework for decision making and rate-distortion
theory and demonstrate how the application of rate-distortion for
decision-making leads to the emergence of abstractions. We argue that
abstractions are induced due to a limit in information processing capacity.Comment: Presented at the NIPS 2013 Workshop on Planning with Information
Constraint
An information-theoretic on-line update principle for perception-action coupling
Inspired by findings of sensorimotor coupling in humans and animals, there
has recently been a growing interest in the interaction between action and
perception in robotic systems [Bogh et al., 2016]. Here we consider perception
and action as two serial information channels with limited
information-processing capacity. We follow [Genewein et al., 2015] and
formulate a constrained optimization problem that maximizes utility under
limited information-processing capacity in the two channels. As a solution we
obtain an optimal perceptual channel and an optimal action channel that are
coupled such that perceptual information is optimized with respect to
downstream processing in the action module. The main novelty of this study is
that we propose an online optimization procedure to find bounded-optimal
perception and action channels in parameterized serial perception-action
systems. In particular, we implement the perceptual channel as a multi-layer
neural network and the action channel as a multinomial distribution. We
illustrate our method in a NAO robot simulator with a simplified cup lifting
task.Comment: 8 pages, 2017 IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization
problems that involve finding extrema of noisy, nonlinear functions. Previous
work has focused on representing possible functions explicitly, which leads to
a two-step procedure of first, doing inference over the function space and
second, finding the extrema of these functions. Here we skip the representation
step and directly model the distribution over extrema. To this end, we devise a
non-parametric conjugate prior based on a kernel regressor. The resulting
posterior distribution directly captures the uncertainty over the maximum of
the unknown function. We illustrate the effectiveness of our model by
optimizing a noisy, high-dimensional, non-convex objective function.Comment: 9 pages, 5 figure
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building
sample-efficient strategies that learn from past experience to adapt to any
task within a target class. Our goal is to equip the reader with the conceptual
foundations of this tool for building new, scalable agents that operate on
broad domains. To do so, we present basic algorithmic templates for building
near-optimal predictors and reinforcement learners which behave as if they had
a probabilistic model that allowed them to efficiently exploit task structure.
Furthermore, we recast memory-based meta-learning within a Bayesian framework,
showing that the meta-learned strategies are near-optimal because they amortize
Bayes-filtered data, where the adaptation is implemented in the memory dynamics
as a state-machine of sufficient statistics. Essentially, memory-based
meta-learning translates the hard problem of probabilistic sequential inference
into a regression problem.Comment: DeepMind Technical Report (15 pages, 6 figures
Data from: Occam's Razor in sensorimotor learning
A large number of recent studies suggest that the sensorimotor system employs probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor - an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task, where participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models - a simple model with a large length scale, leading to smooth curves and a complex model with a short length scale, leading to more wiggly curves. In training trials participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fit equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants' choice behavior was quantitatively consistent with Bayesian Occam's Razor. We could also show that participants' drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioral principle already present during sensorimotor processing
Occam's Razor in sensorimotor learning - human trajectory data
The data-file contains recordings of human motion data that was acquired in a virtual reality setup. Participants of the experiment were controlling a virtual cursor using a 3D manipulandum in order to draw regression trajectories through clouds of noisy observations that were presented to them through a head mounted display. See corresponding publication and included Readme-file for details
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