Download Artificial Intelligence and Soft Computing Behavioral and Cognitive Modeling of the Human Brain By Amit Konar

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Introduction

The book covers 24 chapters altogether. It starts with the behavioral perspective of the ‘human cognition’ and covers in detail the tools and techniques required for its intelligent realization on machines. The classical chapters on search, symbolic logic, planning and machine learning have been covered in sufficient details, including the latest research in the subject. The modern aspects of soft computing have been introduced from the first principles and discussed in a semi-informal manner, so that a beginner of the subject is able to grasp it with minimal effort. Besides soft computing, the other leading aspects of current AI research covered in the book include nonmonotonic and spatio-temporal reasoning, knowledge acquisition, verification, validation and maintenance issues, realization of cognition on machines and the architecture of AI machines. The book ends with two case studies: one on ‘criminal investigation’ and the other on ‘navigational planning of robots,’ where the main emphasis is given on the realization of intelligent systems using the methodologies covered in the book.

The book is unique for its diversity in contents, clarity and precision of presentation and the overall completeness of its chapters. It requires no mathematical prerequisites beyond the high school algebra and elementary differential calculus; however, a mathematical maturity is required to follow the logical concepts presented therein. An elementary background of data structure and a high level programming language like Pascal or C is helpful to understand the book. The book, thus, though meant for two semester courses of computer science, will be equally useful to readers of other engineering disciplines and psychology as well as for its diverse contents, clear presentation and minimum prerequisite requirements.

Table of Contents

Chapter 1: Introduction to Artificial Intelligence and Soft Computing
Chapter 2: The Psychological Perspective of Cognition
Chapter 3: Production Systems
Chapter 4: Problem Solving by Intelligent Search
Chapter 5: The Logic of Propositions and Predicates
Chapter 6: Principles in Logic Programming
Chapter 7: Default and Non-Monotonic Reasoning
Chapter 8: Structured Approach to Knowledge Representation
Chapter 9: Dealing with Imprecision and Uncertainty
Chapter 10: Structured Approach to Fuzzy Reasoning
Chapter 11: Reasoning with Space and Time
Chapter 12: Intelligent Planning
Chapter 13: Machine Learning Techniques
Chapter 14: Machine Learning Using Neural Nets
Chapter 15: Genetic Algorithms
Chapter 16: Realizing Cognition Using Fuzzy Neural Nets
Chapter 17: Visual Perception
Chapter 18: Linguistic Perception
Chapter 19: Problem Solving by Constraint Satisfaction
Chapter 20: Acquisition of Knowledge
Chapter 21: Validation, Verification and Maintenance Issues
Chapter 22: Parallel and Distributed Architecture for Intelligent Systems
Chapter 23: Case Study I: Building a System for Criminal Investigation
Chapter 24: Case Study II: Realization of Cognition for Mobile Robots