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• 1. Introduction
• 2. Foundation of neurophysiology
• 3. Neural Computing
• 4. Mind model CAM
• 5. Perceptual Intelligence
• 6. Language cognition
• 7. Learning
• 8. Memory
• 9. Thought
• 10. Intellectual development and cognitive structure
• 11. Emotion and affect
• 12. Consciousness
• 13. Brain machine integration
• 14. Brain-like intelligence.
• (source: Nielsen Book Data)

Intelligence Science: Leading the Age of Intelligence covers the emerging scientific research on the theory and technology of intelligence, bringing together disciplines such as neuroscience, cognitive science, and artificial intelligence to study the nature of intelligence, the functional simulation of intelligent behavior, and the development of new intelligent technologies. The book presents this complex, interdisciplinary area of study in an accessible volume, introducing foundational concepts and methods, and presenting the latest trends and developments. Chapters cover the Foundations of neurophysiology, Neural computing, Mind models, Perceptual intelligence, Language cognition, Learning, Memory, Thought, Intellectual development and cognitive structure, Emotion and affect, and more. This volume synthesizes a very rich and complex area of research, with an aim of stimulating new lines of enquiry.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Foundation of neurophysiology
• 3. Neural Computing
• 4. Mind model CAM
• 5. Perceptual Intelligence
• 6. Language cognition
• 7. Learning
• 8. Memory
• 9. Thought
• 10. Intellectual development and cognitive structure
• 11. Emotion and affect
• 12. Consciousness
• 13. Brain machine integration
• 14. Brain-like intelligence.
• (source: Nielsen Book Data)

Intelligence Science: Leading the Age of Intelligence covers the emerging scientific research on the theory and technology of intelligence, bringing together disciplines such as neuroscience, cognitive science, and artificial intelligence to study the nature of intelligence, the functional simulation of intelligent behavior, and the development of new intelligent technologies. The book presents this complex, interdisciplinary area of study in an accessible volume, introducing foundational concepts and methods, and presenting the latest trends and developments. Chapters cover the Foundations of neurophysiology, Neural computing, Mind models, Perceptual intelligence, Language cognition, Learning, Memory, Thought, Intellectual development and cognitive structure, Emotion and affect, and more. This volume synthesizes a very rich and complex area of research, with an aim of stimulating new lines of enquiry.
(source: Nielsen Book Data)

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)

• Introduction
• Foundation of Neuro-Physiology
• 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)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• Introduction
• Foundation of Neuro-Physiology
• 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)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• 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.

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 behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Logic Foundation of Artificial Intelligence
• Constraint Reasoning
• Qualitative Reasoning
• Case-Based Reasoning
• Probabilistic Reasoning
• Inductive Learning
• Support Vector Machine
• Explanation-Based Learning
• Reinforcement Learning
• Rough Set
• Association Rules
• Knowledge Discovery
• Distributed Intelligence
• Evolutionary Computation.
• (source: Nielsen Book Data)

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 behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Keynote Presentations.- Case-Based Reasoning Tomorrow: Provenance, the Web, and Cases in the Future of Intelligent Information Processing.- Knowledge Mining Biological Network Models.- Multivariate Bandits and Their Applications.- Image Semantic Analysis and Understanding.- Semantic Web Services.- Collaboration in Agent Grid Based on Dynamic Description Logics.- Requirement Driven Service Composition: An Ontology-Based Approach.- Multi-agent and Workflow-Based Web Service Management Model.- Semantic Approach for Service Oriented Requirements Modeling.- Automatic Reasoning.- Extend Atomic Action Definitions of DDL to Support Occlusions and Conditional Post-conditions.- Preservative Translations between Logical Systems.- The Description Logic for Relational Databases.- Non-Functional Requirements Elicitation and Incorporation into Class Diagrams.- Case-Based Reasoning.- Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support.- Event Extraction for Legal Case Building and Reasoning.- Applications of CBR in Oil Well Drilling: A General Overview.- Data Mining.- Associated Clustering and Classification Method for Electric Power Load Forecasting.- Two Improvement Strategies for PSO.- Mining Temporal Patterns of Technical Term Usages in Bibliographical Data.- Automated Empirical Selection of Rule Induction Methods Based on Recursive Iteration of Resampling Methods.- Web Mining.- Adaptive Web-Based Instruction for Enhancing Learning Ability.- Extracting Comparative Commonsense from the Web.- Detecting Temporal Pattern and Cluster Changes in Social Networks: A Study Focusing UK Cattle Movement Database.- Unstructured P2P-Enabled Service Discovery in the Cloud Environment.- Web Search.- Using Global Statistics to Rank Retrieval Systems without Relevance Judgments.- Rule Learning with Negation: Issues Regarding Effectiveness.- Integrating Web Videos for Faceted Search Based on Duplicates, Contexts and Rules.- An Efficient Data Indexing Approach on Hadoop Using Java Persistence API.- Knowledge Representation.- Knowledge Engineering for Non-engineers.- Attribute Exploration Algorithms on Ontology Construction.- Intelligent Business Transaction Agents for Cross-Organizational Workflow Definition and Execution.- Knowledge Granularity and Representation of Knowledge: Towards Knowledge Grid.- Natural Language Processing.- Combining the Missing Link: An Incremental Topic Model of Document Content and Hyperlink.- A General Approach to Extracting Full Names and Abbreviations for Chinese Entities from the Web.- An English-Arabic Bi-directional Machine Translation Tool in the Agriculture Domain.- A Laplacian Eigenmaps Based Semantic Similarity Measure between Words.- Image Processing.- A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data.- A Novel Distribution of Local Invariant Features for Classification of Scene and Object Categories.- Adult Image Detection Combining BoVW Based on Region of Interest and Color Moments.- Pattern Recognition.- Multimedia Speech Therapy Tools and Other Disability Solutions as Part of a Digital Ecosystem Framework.- Noise Estimation and Noise Removal Techniques for Speech Recognition in Adverse Environment.- Proximity User Identification Using Correlogram.- Erratum: An Efficient Data Indexing Approach on Hadoop Using Java Persistence API.
• (source: Nielsen Book Data)

This volume comprises the 6th IFIP International Conference on Intelligent Infor- tion Processing. As the world proceeds quickly into the Information Age, it encounters both successes and challenges, and it is well recognized nowadays that intelligent information processing provides the key to the Information Age and to mastering many of these challenges. Intelligent information processing supports the most - vanced productive tools that are said to be able to change human life and the world itself. However, the path is never a straight one and every new technology brings with it a spate of new research problems to be tackled by researchers; as a result we are not running out of topics; rather the demand is ever increasing. This conference provides a forum for engineers and scientists in academia and industry to present their latest research findings in all aspects of intelligent information processing. This is the 6th IFIP International Conference on Intelligent Information Processing. We received more than 50 papers, of which 35 papers are included in this program as regular papers and 4 as short papers. We are grateful for the dedicated work of both the authors and the referees, and we hope these proceedings will continue to bear fruit over the years to come. All papers submitted were reviewed by two referees. A conference such as this cannot succeed without help from many individuals who contributed their valuable time and expertise.
(source: Nielsen Book Data)

• Keynote Papers.- Application of Information Processing Technology by Multiagent Simulation.- Cognitive Architectures and Multi-agent Social Simulation.- Full Papers.- A Co-operative Intelligent Assisting Agent Architecture for Web Searching and Desktop Management.- Prisoner's Dilemma Game on Network.- Addressing Constraint Failures in Agent Interaction Protocol.- Reasoning about Success and Failure in Intentional Agents.- Agent-Base Workflow Management System Architecture - An Example on Conference Info System.- Diverse Applications of an Automated Argument System Based on the Logic of Multiple-Valued Argumentation.- Dynamic Interaction Protocol Load in Multi-Agent System Collaboration.- Towards a Component-Based Architecture for TeleAgents.- Development of a Lightweight Middleware Technologies Supporting Mobile Agents.- The Roles of Active Perception in Intelligent Agent Systems.- Fairness in Cooperating Multi-agent Systems - Using Profit Sharing as an Example.- Risk Strategies and Risk Strategy Equilibrium in Agent Interactions Modeled as Normal Repeated 2 x2 Risk Games.- A Crowd Model for Emergency and Panic Situations: Structure, Characteristics and Behaviours.- Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering.- ARTISTE: Agent Organization Management System for Multi-Agent Systems.- Sensor Management Model Using Multi-agent in Small-Area Sensor Networks.- Conflict Resolution Method for Multi-context Situation.- Multi-auction Approach for Solving Task Allocation Problem.- Using Classification Learning in Companion Modeling.- Modeling a MultiAgent Mobile Robotics Test Bed Using a Biologically Inspired Artificial Immune System.- Deploying Multi-Agents for Intelligent Aspect-Oriented Web Services.- Multi-Agent System Using G-XMDR for Data Synchronization in Pervasive Computing Environments.- An Interval-Based Knowledge Model and Query Language for Temporal Information.- A Petri-Net-Based Modeling Framework for Automated Negotiation Protocols in Electronic Commerce.- Agent Based Interaction Model for Electronic Health Record System.- Implementation of MedVI Agent a Medical Vocabulary Interpreter for Medical Agents.- Multi-agent Cooperation: A Description Logic View.- Enhancing Bidding Strategies in CDAs by Adaptive Judgement of Price Acceptability.- Dealing with Objectives in a Constraint-Based Extension to AgentSpeak(L).
• (source: Nielsen Book Data)

The Paci?c-Rim International Workshop on Multi-Agents (PRIMA) is held - nually, and is one of the principal research forums in the practice and theory of autonomous agents and multi-agent systems. Autonomous agents and mul- agent systems are computational systems in which several (semi-) autonomous agents interact with each other or work together to perform some set of tasks or satisfy some set of goals. These systems may involve computational agents that are homogeneous or heterogeneous, they may involve activities on the part of agentshavingcommongoalsordistinctgoals, andtheymayinvolveparticipation on the part of humans and intelligent agents. PRIMA is the ?rst Paci?c Rim workshop related to autonomous agents and multi-agentsystems. AlthoughwealreadyhaveseveralworkshopsinPaci?cRim countries, such as MACC (Multi-agent Systems and Cooperative Computation) inJapanfrom1991, andtheAustralianWorkshoponDistributedArti?cialInt- ligence from 1995, there has been less interactionamong the countriescompared toEuropeandtheAmericas. Since1998, thePRIMAseriesofworkshophasbeen conducted annually. PRIMA 2005, the ?rst of its kind to be held in Malaysia, followed a strong tradition of workshops that began in 1998 as a platform to enable researchers from the Paci? c-Rim countries to share and collaborate their research ?ndings in the areas of autonomous agents and multi-agent systems. In additionto thetraditionaltechnicalpaperpresentationandposterpaperpres- tations there weretwo additionalactivities speci?cally for the bene?t ofdoctoral students, and industry participants: (1) A Doctoral Mentoring Session (2) Agent School The Chairs of the workshop take this opportunity to thank the members of the Local Organizing Committee, the Programme Committee, the Doctoral - lection Committee, and all the additional paper reviewers.
(source: Nielsen Book Data)

• Model Organization Constraints in Multi-Agent System.- A Multi-Agent System for Mobile Environments.- Agent Interaction Management and Application in a Virtual Collaborative Environment.- An Integrated Approach to Battlefield Situation Assessment.- Negotiation Based on Personality.- MIAM: A Robot Oriented Mobile Intelligent Agent Model.- Incorporating Elements from Camle in the Open Repository.- Representing Human Spatial Behavior by Self-Organizing Networks.- Multi-Agent System Development Kit.- Limitations in Auml's Roles Specification.- The Research of Geometric Constraint Soving Based on the Path Tracking Homotopy Iteration Method.- Fast Stereo Matching Method Using Edge Traction.- Hybrid Color Image Segmentation Based Fully Automatic Chroma-Keying System with Cluttered Background.- Research on Techniques of Approximate Recognition of Continuous Deformation of Images with Multi-Grey-Levels.- Recognition of Image with Natural Textures Based on Learning of Information Augmentation.- Improvements on CCA Model with Application to Face Recognition.- Performance of Several Types of Median Filters in Spectral Domain.- Fuzzy and Rough Set.- Algebraic Property of Rough Implication Based on Interval Structure.- Research on Information Requirement of First-Order Universal Implication Operators in Fuzzy Reasoning.- An Extended Rough Sets Approach to Analysis of CUDT.- Intrusion Detection Based on Organizational Coevolutionary Fuzzy Classifiers.- Simple Fuzzy Logic Rules Based on Fuzzy Decision Tree for Classification and Prediction Problem.- A Research on Knowledge Reduction of Information Systems Based on Sub-Consciousness.- Optimal Design of Conic-Cylindrical Gear Reduction Unit Using Fuzzy Physical Programming.- NLOMJ-Natural Language Object Model in Java.- Fingerprint Ridge Line Reconstruction.- Design and Implementation of Automated Data Mining Using Intelligent Agents in Object Oriented Databases.- A Bayesian Optimization Algorithm for UAV Path Planning.- Dilated Chi-Square: A Novel Interestingness Measure to Build Accurate and Compact Decision List.- Post Sequential Patterns Mining.- Extended Constraint Handling for CP-Networks.- Solving CSP by Lagrangian Method with Importance of Constraints.- Component Retrieval Using Conversational Case-Based Reasoning.- Mixed Parallel Execution of Algorithms for Satisfiability Problem.- Clustering Binary Codes to Express the Biochemical Properties of Amino Acids.- Natural Language Interface to Mobile Devices.- Research and Application in Web Usage Mining of the Incremental Mining Technique for Association Rule.- Face Recognition Technique Based on Modular ICA Approach.- Model-Based Debugging with High-Level Observations.- Mapping Search Results into Self-Customized Category Hierarchy.- Rank Aggregation Model for Meta Search.- On the Importance of Being Diverse.- Using Finite Domains in Constraint Satisfaction Problem.- Research on Radar Targets Recognition by Extracting 3-D Characteristic from ISAR Images.- Uncertain Reasoning and Decision Making.- Diagnosing Java Programs with Static Abstractions of Data Structures.- Intelligent Technology for Well Logging Analysis.- SEDatalog: A Set Extension of Datalog.- Session Identification Based on Time Interval in Web Log Mining.- Efficiently Mining Frequent Itemsets with Compact FP-Tree.- Towards Human Oriented WWW.- An Intelligent Diagnosis System Handling Multiple Disorders.- An Intelligent Knowledge-Based Recommendation System.- A Formal Concept Analysis Approach for Web Usage Mining.- Knowledge-Based Decision Support in Oil Well Drilling.- A New Method to Construct the Non-Dominated Set in Multi-Objective Genetic Algorithms.- Ensembles of Multi-Instance Neural Networks.- A Wordnet-Based Approach to Feature Selection in Text Categorization.- A New Support Vector Neural Network Inference System.- An Improved Vehicle Classification Method Based on Gabor Features.- An Incremental Algorithm about the Affinity-Rule Based Transductive Learning Machine for Semi-Supervised Problem.- A Short Tutorial on Reinforcement Learning.- Hardware Design of Two Weighted Neural Network and Application for Object Recognition.- Improvement of Web Data Clustering Using Web Page Contents.- A Prediction Approach to Well Logging.
• (source: Nielsen Book Data)

Data Mining is the process of posing queries and extracting useful information, patterns and trends previously unknown from large quantities of data [Thu, 00]. It is the process where intelligent tools are applied in order to extract data patterns [JM, 01]. This encompasses a number of different technical approaches, such as cluster analysis, learning classification and association rules, and finding dependencies. Agents are defined as software entities that perform some set of tasks on behalf of users with some degree of autonomy. This research work deals about developing a automated data mining system which encompasses the familiar data mining algorithms using intelligent agents in object oriented databases and proposing a framework. Because the data mining system uses the intelligent agents, a new user will be able to interact with the data mining system without much data mining technical knowledge. This system will automatically select the appropriate data mining technique and select the necessary field needed from the database at the appropriate time without expecting the users to specify the specific technique and the parameters. Also a new framework is proposed for incorporating intelligent agents with automated data mining. One of the major goals in developing this system is to give the control to the computer for learning automatically by using intelligent agents.
(source: Nielsen Book Data)