1  5
1. Intelligence Science [2012]
 Shi, Zhongzhi.
 Singapore : World Scientific, 2012.
 Description
 Book — 1 online resource (682 pages)
 Summary

 Introduction
 Foundation of NeuroPhysiology
 Neural Computing
 Mind Model
 Perception
 Visual Information Processing
 Audio Information Processing
 Language
 Learning
 Memory
 Thought
 Development of Intelligence
 Emotion
 Immune System
 Consciousness
 Symbolic Logic
 The Machine Proves
 Perspective.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
2. Advanced artificial intelligence [2020]
 Shi, Zhongzhi, author.
 Second edition.  Singapore ; Hackensack, NJ : World Scientific, [2020]
 Description
 Book — 1 online resource.
 Summary

The joint breakthrough of big data, cloud computing and deep learning has made artificial intelligence (AI) the new focus in the international arena. AI is a branch of computer science, developing intelligent machine with imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behaviour.This comprehensive compendium, consisting of 15 chapters, captures the updated achievements of AI. It is completely revised to reflect the current researches in the field, through numerous techniques and strategies to address the impending challenges facing computer scientists today.The unique volume is useful for senior or graduate students in the information field and related tertiary specialities. It is also a suitable reference text for professionals, researchers, and academics in AI, machine learning, electrical & electronic engineering and biocomputing.
(source: Nielsen Book Data)
 Shi, Zhongzhi.
 Singapore ; Hackensack, N.J. : World Scientific Pub. Co., c2012.
 Description
 Book — xxi, 659 p. : ill. (some col.)
 Summary

 Introduction
 Foundation of NeuroPhysiology
 Neural Computing
 Mind Model
 Perception
 Visual Information Processing
 Audio Information Processing
 Language
 Learning
 Memory
 Thought
 Development of Intelligence
 Emotion
 Immune System
 Consciousness
 Symbolic Logic
 The Machine Proves
 Perspective.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
4. Advanced artificial intelligence [2011]
 Shi, Zhongzhi.
 Singapore ; Hackensack, NJ : World Scientific, ©2011.
 Description
 Book — 1 online resource (xvi, 613 pages) : illustrations
 Summary

 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. Graphbased Backjumping
 3.9. Influencebased 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 CaseBased 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. InstanceBased 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. Semisupervised 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 ExplanationBased Learning
 9.1. Introduction
 9.2. Model for EBL
 9.3. ExplanationBased Generalization
 9.4. Explanation Generalization using Global Substitutions
 9.5. ExplanationBased Specialization
 9.6. Logic Program of ExplanationBased 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. TemporalDifference Learning
 10.6. QLearning
 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. FPGrowth Algorithm
 12.4. CFPTree 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. MultiAgent 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.
(source: Nielsen Book Data)
5. Mind computation [electronic resource]. [2017]
 Shin, Zhongzhi.
 Singapore : World Scientific Publishing Co. Pte Ltd., c2017.
 Description
 Book — 1 online resource (489 p.) : ill. (some col.)
 Summary

"Mind computation is a hot topic of intelligence science. It is explored by computing to explain the theoretical basis of human intelligence. Through longterm research, a mind model CAM (Consciousness and Memory) is proposed, which provides a general framework for brainlike intelligence and brainlike intelligent systems. This novel book centers on mind model CAM, systematically discusses the theoretical basis of mind computation in nine chapters. Because of its advanced progresses on brainlike intelligence, it is useful as a primary reference volume for professionals and graduate students in intelligence science, cognitive science and artificial intelligence."Publisher's website.
Articles+
Journal articles, ebooks, & other eresources
Guides
Course and topicbased guides to collections, tools, and services.