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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; Hackensack, NJ :
World Scientific,
©2011.
|
| Colección: | Series on intelligence science ;
v. 1. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.