1,340 research outputs found
Stochastic models of evidence accumulation in changing environments
Organisms and ecological groups accumulate evidence to make decisions.
Classic experiments and theoretical studies have explored this process when the
correct choice is fixed during each trial. However, we live in a constantly
changing world. What effect does such impermanence have on classical results
about decision making? To address this question we use sequential analysis to
derive a tractable model of evidence accumulation when the correct option
changes in time. Our analysis shows that ideal observers discount prior
evidence at a rate determined by the volatility of the environment, and the
dynamics of evidence accumulation is governed by the information gained over an
average environmental epoch. A plausible neural implementation of an optimal
observer in a changing environment shows that, in contrast to previous models,
neural populations representing alternate choices are coupled through
excitation. Our work builds a bridge between statistical decision making in
volatile environments and stochastic nonlinear dynamics.Comment: 26 pages, 7 figure
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to
adapt to variations of unknown environment contexts, such as changing terrains
in robotic tasks and fluctuated bandwidth in congestion control. Existing works
on adaptation to unknown environment contexts either assume the contexts are
the same for the whole episode or assume the context variables are Markovian.
However, in many real-world applications, the environment context usually stays
stable for a stochastic period and then changes in an abrupt and unpredictable
manner within an episode, resulting in a segment structure, which existing
works fail to address. To leverage the segment structure of piecewise stable
context in real-world applications, in this paper, we propose a
\textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented
\textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief
distribution over latent context with the posterior over segment length and
perform more accurate belief context inference with observed data within the
current context segment. The inferred belief context can be leveraged to
augment the state, leading to a policy that can adapt to abrupt variations in
context. We demonstrate empirically that SeCBAD can infer context segment
length accurately and outperform existing methods on a toy grid world
environment and Mujuco tasks with piecewise-stable context.Comment: NeurIPS 202
Data-driven fault diagnosis of awind farm benchmark model
The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning
We investigate an application in the automatic tuning of computer codes, an
area of research that has come to prominence alongside the recent rise of
distributed scientific processing and heterogeneity in high-performance
computing environments. Here, the response function is nonlinear and noisy and
may not be smooth or stationary. Clearly needed are variable selection,
decomposition of influence, and analysis of main and secondary effects for both
real-valued and binary inputs and outputs. Our contribution is a novel set of
tools for variable selection and sensitivity analysis based on the recently
proposed dynamic tree model. We argue that this approach is uniquely well
suited to the demands of our motivating example. In illustrations on benchmark
data sets, we show that the new techniques are faster and offer richer feature
sets than do similar approaches in the static tree and computer experiment
literature. We apply the methods in code-tuning optimization, examination of a
cold-cache effect, and detection of transformation errors.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS590 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Statistical Computations Underlying the Dynamics of Memory Updating
Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861
Data-driven techniques for the fault diagnosis of a wind turbine benchmark
This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances
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