Advanced artificial intelligence /

Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavio...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Shi, Zhongzhi
Format: Electronic eBook
Language:Inglés
Published: Singapore ; Hackensack, NJ : World Scientific, ©2011.
Series:Series on intelligence science ; v. 1.
Subjects:
Artificial intelligence.
Artificial Intelligence
Intelligence artificielle.
artificial intelligence.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Online Access:Texto completo
Table of Contents:
  • Machine generated contents note: ch. 1 Introduction
  • 1.1. Brief History of AI
  • 1.2. Cognitive Issues of AI
  • 1.3. Hierarchical Model of Thought
  • 1.4. Symbolic Intelligence
  • 1.5. Research Approaches of Artificial Intelligence
  • 1.6. Automated Reasoning
  • 1.7. Machine Learning
  • 1.8. Distributed Artificial Intelligence
  • 1.9. Artificial Thought Model
  • 1.10. Knowledge Based Systems
  • Exercises
  • ch. 2 Logic Foundation of Artificial Intelligence
  • 2.1. Introduction
  • 2.2. Logic Programming
  • 2.3. Nonmonotonic Logic
  • 2.4. Closed World Assumption
  • 2.5. Default Logic
  • 2.6. Circumscription Logic
  • 2.7. Nonmonotonic Logic NML
  • 2.8. Autoepistemic Logic
  • 2.9. Truth Maintenance System
  • 2.10. Situation Calculus
  • 2.11. Frame Problem
  • 2.12. Dynamic Description Logic
  • Exercises
  • ch. 3 Constraint Reasoning
  • 3.1. Introduction
  • 3.2. Backtracking
  • 3.3. Constraint Propagation
  • 3.4. Constraint Propagation in Tree Search
  • 3.5. Intelligent Backtracking and Truth Maintenance.
  • 3.6. Variable Instantiation Ordering and Assignment Ordering
  • 3.7. Local Revision Search
  • 3.8. Graph-based Backjumping
  • 3.9. Influence-based Backjumping
  • 3.10. Constraint Relation Processing
  • 3.11. Constraint Reasoning System COPS
  • 3.12. ILOG Solver
  • Exercise
  • ch. 4 Qualitative Reasoning
  • 4.1. Introduction
  • 4.2. Basic approaches in qualitative reasoning
  • 4.3. Qualitative Model
  • 4.4. Qualitative Process
  • 4.5. Qualitative Simulation Reasoning
  • 4.6. Algebra Approach
  • 4.7. Spatial Geometric Qualitative Reasoning
  • Exercises
  • ch. 5 Case-Based Reasoning
  • 5.1. Overview
  • 5.2. Basic Notations
  • 5.3. Process Model
  • 5.4. Case Representation
  • 5.5. Case Indexing
  • 5.6. Case Retrieval
  • 5.7. Similarity Relations in CBR
  • 5.8. Case Reuse
  • 5.9. Case Retainion
  • 5.10. Instance-Based Learning
  • 5.11. Forecast System for Central Fishing Ground
  • Exercises
  • ch. 6 Probabilistic Reasoning
  • 6.1. Introduction
  • 6.2. Foundation of Bayesian Probability
  • 6.3. Bayesian Problem Solving
  • 6.4. Naive Bayesian Learning Model.
  • 6.5. Construction of Bayesian Network
  • 6.6. Bayesian Latent Semantic Model
  • 6.7. Semi-supervised Text Mining Algorithms
  • Exercises
  • ch. 7 Inductive Learning
  • 7.1. Introduction
  • 7.2. Logic Foundation of Inductive Learning
  • 7.3. Inductive Bias
  • 7.4. Version Space
  • 7.5. AQ Algorithm for Inductive Learning
  • 7.6. Constructing Decision Trees
  • 7.7. ID3 Learning Algorithm
  • 7.8. Bias Shift Based Decision Tree Algorithm
  • 7.9. Computational Theories of Inductive Learning
  • Exercises
  • ch. 8 Support Vector Machine
  • 8.1. Statistical Learning Problem
  • 8.2. Consistency of Learning Processes
  • 8.3. Structural Risk Minimization Inductive Principle
  • 8.4. Support Vector Machine
  • 8.5. Kernel Function
  • Exercises
  • ch. 9 Explanation-Based Learning
  • 9.1. Introduction
  • 9.2. Model for EBL
  • 9.3. Explanation-Based Generalization
  • 9.4. Explanation Generalization using Global Substitutions
  • 9.5. Explanation-Based Specialization
  • 9.6. Logic Program of Explanation-Based Generalization
  • 9.7. SOAR Based on Memory Chunks.
  • 9.8. Operationalization
  • 9.9. EBL with imperfect domain theory
  • Exercises
  • ch. 10 Reinforcement Learning
  • 10.1. Introduction
  • 10.2. Reinforcement Learning Model
  • 10.3. Dynamic Programming
  • 10.4. Monte Carlo Methods
  • 10.5. Temporal-Difference Learning
  • 10.6. Q-Learning
  • 10.7. Function Approximation
  • 10.8. Reinforcement Learning Applications
  • Exercises
  • ch. 11 Rough Set
  • 11.1. Introduction
  • 11.2. Reduction of Knowledge
  • 11.3. Decision Logic
  • 11.4. Reduction of Decision Tables
  • 11.5. Extended Model of Rough Sets
  • 11.6. Experimental Systems of Rough Sets
  • 11.7. Granular Computing
  • 11.8. Future Trends of Rough Set Theory
  • Exercises
  • ch. 12 Association Rules
  • 12.1. Introduction
  • 12.2. The Apriori Algorithm
  • 12.3. FP-Growth Algorithm
  • 12.4. CFP-Tree Algorithm
  • 12.5. Mining General Fuzzy Association Rules
  • 12.6. Distributed Mining Algorithm For Association Rules
  • 12.7. Parallel Mining of Association Rules
  • Exercises
  • ch. 13 Evolutionary Computation
  • 13.1. Introduction
  • 13.2. Formal Model of Evolution System Theory.
  • 13.3. Darwin's Evolutionary Algorithm
  • 13.4. Classifier System
  • 13.5. Bucket Brigade Algorithm
  • 13.6. Genetic Algorithm
  • 13.7. Parallel Genetic Algorithm
  • 13.8. Classifier System Boole
  • 13.9. Rule Discovery System
  • 13.10. Evolutionary Strategy
  • 13.11. Evolutionary Programming
  • Exercises
  • ch. 14 Distributed Intelligence
  • 14.1. Introduction
  • 14.2. The Essence of Agent
  • 14.3. Agent Architecture
  • 14.4. Agent Communication Language ACL
  • 14.5. Coordination and Cooperation
  • 14.6. Mobile Agent
  • 14.7. Multi-Agent Environment MAGE
  • 14.8. Agent Grid Intelligence Platform
  • Exercises
  • ch. 15 Artificial Life
  • 15.1. Introduction
  • 15.2. Exploration of Artificial Life
  • 15.3. Artificial Life Model
  • 15.4. Research Approach of Artificial Life
  • 15.5. Cellular Automata
  • 15.6. Morphogenesis Theory
  • 15.7. Chaos Theories
  • 15.8. Experimental Systems of Artificial Life
  • Exercises.

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