Book description
The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intellige
Table of contents
- Preface
- The Author
-
Chapter 1: Introduction
- 1.1 Intelligent Systems
- 1.2 A Spectrum of Intelligent Behavior
- 1.3 Knowledge-Based Systems
- 1.4 The Knowledge Base
- 1.5 Deduction, Abduction, and Induction
- 1.6 The Inference Engine
- 1.7 Declarative and Procedural Programming
- 1.8 Expert Systems
- 1.9 Knowledge Acquisition
- 1.10 Search
- 1.11 Computational Intelligence
- 1.12 Integration with Other Software
- Further Reading
-
Chapter 2: Rule-Based Systems
- 2.1 Rules and Facts
- 2.2 A Rule-Based System for Boiler Control
- 2.3 Rule Examination and Rule Firing
- 2.4 Maintaining Consistency
- 2.5 The Closed-World Assumption
- 2.6 Use of Local Variables within Rules
- 2.7 Forward Chaining (a Data-Driven Strategy)
- 2.8 Conflict Resolution
- 2.9 Backward Chaining (a Goal-Driven Strategy)
- 2.10 A Hybrid Strategy
- 2.11 Explanation Facilities
- 2.12 Summary
- Further Reading
-
Chapter 3: Handling Uncertainty: Probability and Fuzzy Logic
- 3.1 Sources of Uncertainty
-
3.2 Bayesian Updating
- 3.2.1 Representing Uncertainty by Probability
- 3.2.2 Direct Application of Bayes’ Theorem
- 3.2.3 Likelihood Ratios
- 3.2.4 Using the Likelihood Ratios
- 3.2.5 Dealing with Uncertain Evidence
- 3.2.6 Combining Evidence
- 3.2.7 Combining Bayesian Rules with Production Rules
- 3.2.8 A Worked Example of Bayesian Updating
- 3.2.9 Discussion of the Worked Example
- 3.2.10 Advantages and Disadvantages of Bayesian Updating
- 3.3 Certainty Theory
- 3.4 Fuzzy Logic: Type-1
- 3.5 Fuzzy Control Systems
- 3.6 Fuzzy Logic: Type-2
- 3.7 Other Techniques
- 3.8 Summary
- Further Reading
-
Chapter 4: Agents, Objects, and Frames
- 4.1 Birds of a Feather: Agents, Objects, and Frames
- 4.2 Intelligent Agents
- 4.3 Agent Architectures
- 4.4 Multiagent Systems
- 4.5 Swarm Intelligence
-
4.6 Object-Oriented Systems
- 4.6.1 Introducing OOP
- 4.6.2 An Illustrative Example
- 4.6.3 Data Abstraction
- 4.6.4 Inheritance
- 4.6.5 Encapsulation
- 4.6.6 Unified Modeling Language (UML)
- 4.6.7 Dynamic (or Late) Binding
- 4.6.8 Message Passing and Function Calls
- 4.6.9 Metaclasses
- 4.6.10 Type Checking
- 4.6.11 Persistence
- 4.6.12 Concurrency
- 4.6.13 Active Values and Daemons
- 4.6.14 OOP Summary
- 4.7 Objects and Agents
- 4.8 Frame-Based Systems
- 4.9 Summary: Agents, Objects, and Frames
- Further Reading
- Chapter 5: Symbolic Learning
- Chapter 6: Single-Candidate Optimization Algorithms
-
Chapter 7: Genetic Algorithms for Optimization
- 7.1 Introduction
- 7.2 The Basic GA
- 7.3 Selection
- 7.4 Elitism
- 7.5 Multiobjective Optimization
- 7.6 Gray Code
- 7.7 Variable Length Chromosomes
- 7.8 Building Block Hypothesis
- 7.9 Selecting GA Parameters
- 7.10 Monitoring Evolution
- 7.11 Finding Multiple Optima
- 7.12 Genetic Programming
- 7.13 Other Forms of Population-Based Optimization
- 7.14 Summary
- Further Reading
- Chapter 8: Neural Networks
- Chapter 9: Hybrid Systems
- Chapter 10: Artificial Intelligence Programming Languages
-
Chapter 11: Systems for Interpretation and Diagnosis
- 11.1 Introduction
- 11.2 Deduction and Abduction for Diagnosis
- 11.3 Depth of Knowledge
- 11.4 Model-Based Reasoning
- 11.5 Case Study: A Blackboard System for Interpreting Ultrasonic Images
- 11.6 Summary
- Further Reading
-
Chapter 12: Systems for Design and Selection
- 12.1 The Design Process
- 12.2 Design as a Search Problem
- 12.3 Computer-Aided Design
- 12.4 The Product Design Specification (PDS): A Telecommunications Case Study
- 12.5 Conceptual Design
- 12.6 Constraint Propagation and Truth Maintenance
- 12.7 Case Study: Design of a Lightweight Beam
- 12.8 Design as a Selection Exercise
- 12.9 Failure Mode and Effects Analysis (FMEA)
- 12.10 Summary
- Further Reading
-
Chapter 13: Systems for Planning
- 13.1 Introduction
- 13.2 Classical Planning Systems
- 13.3 STRIPS
- 13.4 Considering the Side Effects of Actions
- 13.5 Hierarchical Planning
- 13.6 Postponement of Commitment
- 13.7 Job-Shop Scheduling
-
13.8 Constraint-Based Analysis
- 13.8.1 Constraints and Preferences
- 13.8.2 Formalizing the Constraints
- 13.8.3 Identifying the Critical Sets of Operations
- 13.8.4 Sequencing in Disjunctive Case
- 13.8.5 Sequencing in Nondisjunctive Case
- 13.8.6 Updating Earliest Start Times and Latest Finish Times
- 13.8.7 Applying Preferences
- 13.8.8 Using Constraints and Preferences
- 13.9 Replanning and Reactive Planning
- 13.10 Summary
- Further Reading
-
Chapter 14: Systems for Control
- 14.1 Introduction
- 14.2 Low-Level Control
- 14.3 Requirements of High-Level (Supervisory) Control
- 14.4 Blackboard Maintenance
- 14.5 Time-Constrained Reasoning
- 14.6 Fuzzy Control
- 14.7 The BOXES Controller
- 14.8 Neural Network Controllers
- 14.9 Statistical Process Control (SPC)
- 14.10 Summary
- Further Reading
- Chapter 15: The Future of Intelligent Systems
- References
Product information
- Title: Intelligent Systems for Engineers and Scientists, 3rd Edition
- Author(s):
- Release date: April 2016
- Publisher(s): CRC Press
- ISBN: 9781498783798
You might also like
book
Commonsense Reasoning, 2nd Edition
To endow computers with common sense is one of the major long-term goals of artificial intelligence …
video
Artificial and human intelligence in healthcare
With the fundamental breakthroughs in artificial intelligence and the significant increase of digital healthcare data, there’s …
book
Adaptive Learning Methods for Nonlinear System Modeling
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms …
book
Computer and Machine Vision, 4th Edition
Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the …