Human Behavior Learning and Transfer

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$119.95
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ISBN 9780849377839
Cat# 7783
 

Features

  • Provides a coherent framework for learning, validating, evaluating, optimizing, and transferring discrete-time models of human control strategy
  • Delineates a new nonparametric method for fitting trajectories to phase space data
  • Compares previously existing methods that may be applied to dimension reduction for action learning
  • Summary

    Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explore processes for evaluating, optimizing, and transferring human skills. They also cover modeling continuous and discontinuous human control strategy and discuss simulation studies and practical real-life situations.

    The book examines how to model two main aspects of human behavior: reaction skills and action skills. It begins with a discussion of the various topics involved in human reaction skills modeling. The authors apply machine learning techniques and statistical analysis to abstracting models of human reaction control strategy. They contend that such models can be learned sufficiently to emulate complex human control behaviors in the feedback loop.

    The second half of the book explores issues related to human action skills modeling. The methods presented are based on techniques for reducing the dimensionality of data sets, while preserving as much useful information as possible. The modeling approaches developed are applied in real-life applications including navigation of smart wheel chairs and intelligent surveillance.

    Written in a consistent, easily approachable style, the book includes in-depth discussions of a broad range of topics. It provides the tools required to formalize human behaviors into algorithmic, machine-coded strategies.

    Table of Contents

    INTRODUCTION
    Motivation
    Overview

    INTRODUCTION TO HUMAN REACTION SKILL MODELING
    Motivation
    Related Work

    LEANING OF HUMAN CONTROL STRATEGY: CONTINUOUS AND DISCONTINUOUS
    Experimental Design
    Cascade Neural Networks with Kalman Filtering
    HCS Models: Continuous Control
    HCS Models: Discontinuous Control

    VALIDATION OF HUMAN CONTROL STRATEGY MODELS
    Need for Model Validation
    Stochastic Similarity Measure
    Human-to-Model Comparisons

    EVALUATION OF HUMAN CONTROL STRATEGY
    Introduction
    Obstacle Avoidance
    Tight Turning
    Transient Response
    Time Delay
    Passenger Comfort
    Driving Smoothness
    Summary

    PERFORMANCE OPTIMIZATION OF HUMAN CONTROL STRATEGY
    Introduction
    Simultaneously Perturbed Stochastic Approximation
    Iterative Optimization Algorithm
    Model Optimization and Performance Analysis
    Summary

    TRANSFER OF HUMAN CONTROL STRATEGY
    Introduction
    Model Transfer Based on Similarity Measure
    Model Compensation
    Summary

    TRANSFERRING HUMAN NAVIGATIONAL SKILLS TO SMART WHEELCHAIR
    Introduction
    Methodology
    Experimental Study
    Analysis
    Conclusion

    INTRODUCTION TO HUMAN ACTION SKILL MODELING
    Learning Action Models
    Dimension Reduction Formulation
    Related Research

    GLOBAL PARAMETRIC METHODS FOR DIMENSION REDUCTION
    Introduction
    Parametric Methods for Global Modeling
    An Experimental Data Set
    PCA for Modeling Performance Data
    NLPCA
    SNLPCA
    Comparison
    Characterizing NLPCA Mappings

    LOCAL METHODS FOR DIMENSION REDUCTION
    Introduction
    Non-parametric Methods for Trajectory Fitting
    Scatter Plot Smoothing
    Action Recognition Using Smoothing Splines
    An Experiment Using Spline Smoothing
    Principal Curves
    Expanding the One-Dimensional Representation
    Branching
    Over-Fitting

    A SPLINE SMOOTHER IN PHASE SPACE FOR TRAJECTORY FITTING
    Smoothing with Velocity Information
    Problem Formulation
    Solution
    Notes on Computation and Complexity
    Similar Parameterizations
    Multi-Dimensional Smoothing
    Estimation of Variances
    Windowing Variance Estimates
    The Effect of Velocity Information
    Cross-Validation

    ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE
    Introduction
    System Overview
    Background Subtraction
    Global Trajectory Similarity Estimation
    Trajectory Normality Classifier
    Experiment 1: Trajectory Normality Classifier
    Further Analysis on Global Trajectory Similarity Based on LCSS
    Methodology Used in Boundary Modeling
    LCSS Boundary Limit Establishment
    Experiment 2: Boundary Modeling
    Discussion
    Conclusions

    MODELING OF FACIAL AND FULL-BODY ACTIONS
    Facial Expression Intensity Modeling
    Full-Body Action Modeling

    CONCLUSIONS

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