Human Behavior Learning and Transfer
Auteurs : Xu Yangsheng, C. Lee Ka Keung
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.
Date de parution : 09-2005
15.6x23.4 cm
Thèmes de Human Behavior Learning and Transfer :
Mots-clés :
Cascade Neural Network; cascade; HCS; neural; De Hoog; network; Support Vector Regression; control; Nonlinear Principal Component Analysis; strategy; Ck Models; hidden; Support Vector Classification; markov; Independent Component Analysis; models; Hidden Markov Models; training; Robotic Wheelchair; data; Dick’s Model; full-body actions; Trajectory Fitting; walking trajectories; Support Vector Machines; reaction behaviors; Human Performance Data; facial actions; Isometric Feature Mapping; human control strategy; Obstacle Avoidance Maneuver; Principal Curves; RMS Error; Human Action Skill; Hidden Units; Facial Feature Points; Raw Data Space; Iterative Optimization Algorithm; Data Set; HMM