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Book Cover
E-book
Author Cao, Longbing, 1969- author.

Title Metasynthetic computing and engineering of complex systems / Longbing Cao
Published London : Springer, [2015]
©2015

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Description 1 online resource (xiv, 348 pages)
Series Advanced information and knowledge processing
Advanced information and knowledge processing.
Contents Machine generated contents note: 1. Complex Systems -- 1.1. Introduction -- 1.2. System Complexities -- 1.3. System Transparency -- 1.3.1. Black Boxes -- 1.3.2. White Boxes -- 1.3.3. Glass Boxes -- 1.3.4. Grey Boxes -- 1.4. System Classification -- 1.5. Complex Agent Systems -- 1.5.1. Multiagent Systems -- 1.5.2. Large-Scale Systems -- 1.5.3. Large-Scale Multiagent Systems -- 1.5.4. Open Complex Agent Systems -- 1.6. Hybrid Intelligent Systems -- 1.6.1. Concept -- 1.6.2. Hybridization Strategies -- 1.6.3. Design Strategies -- 1.6.4. Typical Hybrid Applications -- 1.7. Evolution of Intelligent Systems -- 1.8. Open Giant Intelligent Systems -- 1.9. Computing and Engineering Complex Systems -- 1.10. Summary -- References -- 2. Ubiquitous Intelligence -- 2.1. Introduction -- 2.2. Data Intelligence -- 2.2.1. What Is Data Intelligence? -- 2.2.2. Aims of Involving Data Intelligence -- 2.2.3. Aspects of Data Intelligence -- 2.3. Domain Intelligence -- 2.3.1. What Is Domain Intelligence? -- 2.3.2. Aims of Involving Domain Intelligence -- 2.3.3. Aspects of Domain Intelligence -- 2.4. Network Intelligence -- 2.4.1. What Is Network Intelligence? -- 2.4.2. Aims of Involving Network Intelligence -- 2.4.3. Aspects of Network Intelligence -- 2.5. Human Intelligence -- 2.5.1. What Is Human Intelligence? -- 2.5.2. Aims of Involving Human Intelligence -- 2.5.3. Aspects of Human Intelligence -- 2.6. Organizational Intelligence -- 2.6.1. What Is Organizational Intelligence? -- 2.6.2. Aims of Involving Organizational Intelligence -- 2.6.3. Aspects of Organizational Intelligence -- 2.7. Social Intelligence -- 2.7.1. What Is Social Intelligence? -- 2.7.2. Aims of Involving Social Intelligence -- 2.7.3. Aspects of Social Intelligence -- 2.8. Metasynthesis of Ubiquitous Intelligence -- 2.9. Summary -- References -- 3. System Methodologies -- 3.1. Introduction -- 3.2. Reductionism -- 3.3. Holism -- 3.4. Systematology -- 3.5. Summary -- References -- 4. Computing Paradigms -- 4.1. Introduction -- 4.2. Objects and Object-Oriented Methodology -- 4.3. Components and Component-Based Methodology -- 4.4. Services and Service-Oriented Methodology -- 4.5. Agents and Agent-Oriented Methodology -- 4.5.1. Goal-Oriented Requirements Analysis -- 4.5.2. Agent-Oriented Software Engineering -- 4.5.3. Issues in Agent-Oriented Software Engineering -- 4.6. Relations Among Agents, Objects, Components, and Services -- 4.7. Autonomic Computing -- 4.8. Organizational Computing -- 4.9. Behavior Computing -- 4.10. Social Computing -- 4.11. Cloud/Service Computing -- 4.12. Metasynthetic Computing -- References -- 5. Metasynthesis -- 5.1. Introduction -- 5.2. Open Complex Giant Systems -- 5.3. OCGS System Complexities -- 5.4. Knowledge and Intelligence Emergence -- 5.5. Theoretical Framework of Metasynthesis -- 5.6. Problem-Solving Process in M-Space -- 5.7. Social Cognitive Intelligence Emergence in M-Space -- 5.7.1. Individual Cognitive Model -- 5.7.2. Social Cognitive Interaction Model -- 5.7.3. Cognitive Intelligence Emergence -- 5.8. Thinking Pitfalls in M-Interactions -- 5.9. M-Computing: Engineering OCGS -- 5.10. Discussions -- References -- 6. OSOAD Methodology -- 6.1. Introduction -- 6.2. Organizational Abstraction -- 6.2.1. Actors -- 6.2.2. Environment -- 6.2.3. Interaction -- 6.2.4. Organizational Rules -- 6.2.5. Organizational Structure -- 6.2.6. Organizational Goal -- 6.2.7. Organizational Dynamics -- 6.3. Organization-Oriented Analysis -- 6.3.1. Challenges for Current Organization-Related Software Engineering -- 6.3.2. What is Organization-Oriented Analysis? -- 6.4. Agent Service-Oriented Design -- 6.4.1. Agent Service, Services of Agent, and Services of Service -- 6.4.2. Why Agent Service-Oriented Design? -- 6.4.3. What is Agent Service-Oriented Design? -- 6.4.4. Agent Service-Oriented Architectural Design -- 6.4.5. Agent Service-Oriented Detailed Design -- 6.5. Building Organization and Service-Oriented Software Engineering -- 6.6. Summary -- References -- 7. Visual Modeling -- 7.1. Introduction -- 7.2. Actor Model -- 7.2.1. Actor Classification -- 7.2.2. Role Model -- 7.3. Environment Model -- 7.3.1. Characteristics of Agent Environment -- 7.3.2. Classification of Agent Environment -- 7.3.3. POMDPAEI Model -- 7.4. Modeling Organizational Rules -- 7.4.1. Structural Rules -- 7.4.2. Problem-Solving Rules -- 7.4.3. Rule Combinations -- 7.5. Modeling Organizational Structure -- 7.5.1. GAIRE Model -- 7.6. Organizational Dynamics Analysis -- 7.7. Interaction Ontology -- 7.7.1. Interaction Protocols -- 7.7.2. Organizational Patterns -- 7.7.3. Interaction Levels -- 7.7.4. Interaction Rules -- 7.8. Interaction Protocols Engineering -- 7.8.1. Analysis -- 7.8.2. Interaction Protocol Ontology -- 7.8.3. Specifications of Interaction Protocol -- 7.8.4. Interaction Metaprotocols -- 7.9. Modeling Interaction Patterns -- 7.9.1. Pattern Description Template -- 7.9.2. Case Study: Contract Net Protocol -- 7.10. Agent-Environment Interaction -- 7.10.1. What is Agent-Environment Interaction? -- 7.10.2. Modeling Based on Markov Decision Process -- 7.10.3. Modeling Based on the Science of Complexity -- 7.10.4. Dynamic System Theory -- 7.10.5. Case Study: Markov State Chain -- 7.11. Summary -- References -- 8. Formal Modeling -- 8.1. Introduction -- 8.2. First-Order Linear-Time Temporal Logics -- 8.2.1. Formal Assertions -- 8.2.2. Real-Time Temporal Logics -- 8.3. Temporal Specification -- 8.4. Formulae for Organizational Abstraction -- 8.4.1. Actor -- 8.4.2. Environment -- 8.4.3. Rule -- 8.4.4. Properties and Keywords -- 8.5. Modeling Roles -- 8.6. Modeling Interaction -- 8.7. FIPA ACL Message Specifications -- 8.7.1. ACL Protocol Description Language -- 8.7.2. Modeling ACL Messages -- 8.8. Modeling Organizational Goal -- 8.9. Summary -- References -- 9. Integrative Modeling -- 9.1. Introduction -- 9.2. Integrating Functional and Nonfunctional Requirements -- 9.2.1. Functional Requirements Analysis -- 9.2.2. Nonfunctional Requirements Analysis -- 9.2.3. Analyzing Integrative Requirements -- 9.3. Visual Modeling -- 9.3.1. Goal-Oriented Visual Modeling -- 9.4. Formal Specifications -- 9.5. Integrative Modeling Framework -- 9.5.1. Business-Oriented Functional Requirements -- 9.5.2. Business-Oriented Nonfunctional Requirements -- 9.5.3. Integrative Modeling -- 9.6. Summary -- References -- 10. Agent Service-Oriented Architectural Design -- 10.1. Introduction -- 10.2. Agent Service Model -- 10.2.1. Agent Model -- 10.2.2. Service Model -- 10.3. Agent Service Design Patterns -- 10.3.1. Agent Architecture Patterns -- 10.3.2. Structural and Functional Service Patterns -- 10.4. Agent Service-Oriented Integration Architectures -- 10.4.1. Integration Levels and Techniques -- 10.4.2. Architectures for Application Integration -- 10.5. Agent Service-Oriented Integration Strategies -- 10.5.1. Multiagent + Web Services -- 10.5.2. Multiagent + Service-Oriented Computing -- 10.6. Agent Service Management and Communications -- 10.7. Agent Service Coordination -- 10.7.1. Coordination Methods -- 10.7.2. Coordination Modeling and Patterns -- 10.8. Case Study -- 10.9. Summary -- References -- 11. Agent Service-Oriented Detailed Design -- 11.1. Introduction -- 11.2. Agent Service Ontology -- 11.2.1. Extracting Problem-Solving Ontology -- 11.2.2. Developing Agent Service Ontology -- 11.3. Representation of Agent Services -- 11.3.1. Agent Service Specification -- 11.3.2. Case Study: Algorithm Registration Agent Service -- 11.4. Agent Service Endpoint Interfaces -- 11.4.1. Designing Agent Service Interfaces
-- 11.4.2. Case Study: Algorithm Service Interface -- 11.5. Directory of Agent Services -- 11.6. Communication of Agent Services -- 11.7. Transport of Agent Services -- 11.8. Mediation of Agent Services -- 11.9. Discovery of Agent Services -- 11.10. Modeling Coordination -- 11.11. Other Strategic Issues -- 11.11.1. Design with Agent Service-Oriented Principles -- 11.11.2. Create a Custom Ontological Directory -- 11.11.3. Define a Schema Management Strategy -- 11.11.4. Always Relate XML to Data -- 11.12. Summary -- References -- 12. Ontological Engineering -- 12.1. Introduction -- 12.2. Ontology Profiles -- 12.2.1. From Ontology to Ontological Engineering -- 12.2.2. Domain-Specific Business Ontology -- 12.2.3. Problem-Solving Ontology -- 12.2.4. Ontological Commitment -- 12.3. Ontological Semantic Relationships -- 12.4. Ontological Representation -- 12.4.1. Ontology Modeling Techniques -- 12.4.2. Representing Domain Ontologies -- 12.4.3. Representing Problem-Solving Ontologies -- 12.5. Ontological Semantic Aggregation and Transformation Cross Domains -- 12.5.1. Semantic Aggregation of Semantic Relationships -- 12.5.2. Semantic Aggregation of Ontological Items -- 12.5.3. Transformation Between Ontological Items -- 12.6. Summary -- References -- 13. OSOAD Case Study -- 13.1. Organization-Oriented System Analysis -- 13.2. Organizational Relationship Model -- 13.3. Organizational Rationale Model -- 13.4. Formal Analysis -- 13.5. Formal Refinement Using Scenario-Based Analysis -- 13.6. Agent Service-Driven Plug and Play -- 13.6.1. Plug and Play Modeling -- 13.6.2. Agent Service-Driven Plug and Play -- 13.6.3. Implementation
Note continued: 13.7. M-Space for Macroeconomic Decision Support -- 13.8. Summary -- References -- 14. Actionable Knowledge Discovery and Delivery -- 14.1. Introduction -- 14.2. Issues with Existing KDD -- 14.3. Gap Analysis -- 14.3.1. Gaps Between Delivered and Desired -- 14.3.2. Aspects for Narrowing Gaps -- 14.4. AKD Framework -- 14.4.1. AKD Problem Statement -- 14.4.2. Actionability Computing -- 14.4.3. AKD Concept Map -- 14.4.4. Ubiquitous Intelligence -- 14.5. Deployment -- 14.5.1. Opportunities -- 14.5.2. AKD Architectures -- 14.5.3. AKD Implementation -- 14.5.4. Knowledge Delivery -- 14.6. Example -- 14.7. Summary -- References -- 15. Learning Complex Behavioral and Social Data -- 15.1. Introduction -- 15.2. Complex Behavioral and Social Problems -- 15.2.1. Behavioral and Social System and Intelligence -- 15.2.2. Complexity of Behavioral and Social Systems -- 15.3. Non-IID Behavioral and Social Problems -- 15.3.1. Coupling -- 15.3.2. Heterogeneity -- 15.4. Issues in Classic Behavioral and Social Learning -- 15.4.1. Classic Behavior Analysis -- 15.4.2. Classic Social Media and Recommendation Systems -- 15.4.3. Classic Social Network Analysis -- 15.5. Non-IIDness Learning -- 15.6. Non-IIDness Learning Case Studies -- 15.6.1. Coupled Behavior Analysis -- 15.6.2. Coupled Item Recommendation -- 15.6.3. Term Coupling-Based Document Analysis -- 15.7. Summary -- References -- 16. Opportunities and Prospects -- 16.1. About Open Complex System Studies -- 16.2. About Metasynthetic Computing and Engineering -- References
Summary Provides a comprehensive overview and introduction to the concepts, methodologies, analysis, design and applications of metasynthetic computing and engineering. The author: Presents an overview of complex systems, especially open complex giant systems such as the Internet, complex behavioural and social problems, and actionable knowledge discovery and delivery in the big data era. Discusses ubiquitous intelligence in complex systems, including human intelligence, domain intelligence, social intelligence, network intelligence, data intelligence and machine intelligence, and their synergy through metasynthetic engineering. Explains the concept and methodology of human-centred, human-machine-cooperated qualitative-to-quantitative metasynthesis for understanding and managing open complex giant systems, and its computing approach: metasynthetic computing. Introduces techniques and tools for analysing and designing problem-solving systems for open complex problems and systems. Metasynthetic Computing and Engineering of Complex Systems uses the systematological methodology in addressing system complexities in open complex giant systems, for which it may not only be effective to apply reductionism or holism. The book aims to encourage and inspire discussions, design, implementation and reflection of effective methodologies and tools for computing and engineering open complex systems and problems. Researchers, research students and practitioners in complex systems, artificial intelligence, data science, computer science, and even system science, cognitive science, behaviour science, and social science, will find this book invaluable
Bibliography Includes bibliographical references and index
Notes Vendor-supplied metadata
Subject Systems engineering.
Large scale systems.
Computer systems.
systems engineering.
Robotics.
Artificial intelligence.
Software Engineering.
TECHNOLOGY & ENGINEERING -- Engineering (General)
TECHNOLOGY & ENGINEERING -- Reference.
Computer systems
Large scale systems
Systems engineering
Form Electronic book
ISBN 9781447165514
1447165519
1447165500
9781447165507