Intelligent Autonomous Systems: Foundations and Applications (Studies in Computational Intelligence, 275)

✍ Scribed by Dilip Kumar Pratihar (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 269
• Category: Library

✦ Synopsis

Intelligent Autonomous Systems (IAS) are the physical embodiment of machine intelligence providing a core concept for integrating various advanced techno- gies with pattern recognition and learning. The basic philosophy of IAS research is to explore and understand the nature of intelligence in problems of perception, reasoning, learning and control in order to develop and implement the theory to engineered realization. In other words, the objective is to formulate various me- odologies for the development of robots which can operate autonomously and exhibit intelligent behavior by making appropriate decisions to perform the right task at the right time. Since IAS basically deals with the integration of machines, computing, sensing, and software to create intelligent systems capable of intera- ing with the complexities of the real world, advanced topics like soft computing, artificial life, evolutionary biology, and cognitive psychology have great promise in improving its intelligence and performance. Because of the inter-disciplinary character, the subject has several challenging issues for research, design and development covering a number of disciplines. These issues are further concerned with the development of both technology and methodology apart from various operations. The present research monograph titled “Intelligent Autonomous Systems: Foundations and Applications", edited by two renowned researchers, Professor Dilip K. Pratihar of IIT, Kharagpur, India and Professor Lakhmi C. Jain, Univ- sity of South Australia, Australia, provides a fairly representative cross-section of the activities that is going on all over the world in this area.

✦ Table of Contents

Title Page
Foreword
Preface
Contents
Towards Intelligent Autonomous Systems
Introduction
Chapters Included in the Book
Conclusion
References
General Aspects of Intelligent Autonomous Systems
Introduction
Preliminaries and Motivations
Agents
Autonomy and Intelligence
Motivation for Intelligent Autonomous Agents
Representative Examples of the State of the Art
Simple Reflex Agents
Model-Based Reflex Agents
IAA Technology in Progress
AI Methods in Store for IAAs
Longer-Term Perspectives
Five Challenges
The Logical Approach to IAAs
Considerations for the Development of an IAA
Conclusions
References
Design and Development of Intelligent Autonomous Robots
Introduction
Autonomous Mobile Robots
Literature Review
Robot Motion Planning Approaches
Environment Modeling
Scope of the Chapter
Statement of the Problem
Proposed Motion Planning Scheme and Mathematical Formulation of the Problem
Developed Motion Planning Approaches
Results and Discussion
Performance Testing through Computer Simulations
Camera Calibration and Image Processing
Performance Testing through Real Experiments
Concluding Remarks and Scope for Future Work
Scope for Future Work
References
Gait Planning of Biped Robots Using Soft Computing: An Attempt to Incorporate Intelligence
Introduction
Literature Review
Research Issues
A Case Study
Analytical Approach
Soft Computing-Based Approaches
Results and Discussion
Concluding Remarks
Scope for Future Study
Appendix A: Bounadary Conditions for Swing Foot Trajectory
Appendix B: Determination of Joint Torques of the Biped Robot
References
On Design and Development of an Intelligent Mobile Robotic Vehicle for Stair-Case Navigation
Introduction
Literature Survey
Genesis
Kinematics, Dynamics and Control
Dynamic Model for Stair Climbing
Modeling of the Payload Platform Orientation Mechanism
Fuzzy Logic Controller
Vision System
Results and Discussion
Motion Simulation and Experimentation on Stair
Motion Simulation and Experimentation on Stair
Stability Margin
Simulation Results of Fuzzy Logic Controller
Results for Staircase Detection Using Vision Sensor
Conclusion
References
Ensemble Learning for Multi-source Information Fusion
Introduction
Multiple Source Fusion
Definition
Classification of Information Fusion
Ensemble Models
Stacked Generalization
Boosting
Mixture-of-Experts
Piecewise Linear Regression Models
Fusion of Locally Valid Heterogeneous Models
Validity Function
Heterogeneous Mixture-of-Experts
Applications of Information Fusion
Combinations of Analytical and Data-Driven Models
Modeling of Energy Flow in a Hybrid Electric Vehicle
Conclusions
References
Towards Developing Intelligent Autonomous Systems in Psychiatry: Its Present State and Future Possibilities
Introduction
Knowledge Engineering (KE) and Intelligent Decision Support Systems: The Parent and the Child
Knowledge Engineering and IDSS in Psychiatry: Predicted Advantages
Knowledge Engineering in Psychiatry: Current State of Art
Knowledge Engineering at the Genetic and Molecular Levels
Knowledge Engineering on Clinical Psychiatry Data
Towards Developing Autonomous Intelligent Decision Support Systems in Psychiatry: Current State of Art
Hard Computing Techniques and Autonomous Systems in Psychiatry: A Review
Soft Computing Techniques and Intelligent Autonomous Systems in Psychiatry: A Critical Review
Issues Behind Automating Psychiatric Decision-Making
Lack of Multidisciplinary Approach
Issues Related to Adoption of IDSS
Concluding Remarks and Future Work
References
Condition Monitoring of Internal Combustion Engine Using EMD and HMM
Introduction
Working Principle of IC Engine and Noise Sources
Empirical Mode Decomposition
Basic Fundamentals of HMM
Proposed Method
IC Engine Fault Diagnosis Using Proposed Method
Experimental Setup
Feature Extraction
Experimental Result and Classification
Conclusions
References
An Intelligent Approach for Security Management of an Enterprise Network Using Planner
Introduction
Planner
Introduction
Definitions and Notations
Description of the Algorithm
Additional Features
Related Works
Generation of Minimal Attack Graph Using Planner
Case Study
Identification of Attack Path Using GraphPlan
Attack Path Enumeration Algorithm
Attack Graph Building Algorithm
Analysis of the Proposed Approach
Conclusions
References
High Dimensional Neural Networks and Applications
Features of Artificial Neuron
Learning and Acquisition of Knowledge
PCA/ICA
Real Domain Neural Network
Complex Domain Neural Network
Complex Activation Function
Learning in Complex Domain
Complex Domain Neural Network-Based Intelligent Systems
Conformal Mapping
Communication Channel Equalization
Time-Series Prediction Problems
Radar and Sonar Signal Classification
2D Face Recognition for Biometric Applications
3D Vector Valued Neural Network
Generalized Mapping in 3D
3-D Face Recognition for Biometric Applications
Conclusions
References
Author Index
Appendix