Multi Agent Systems: Technologies and Applications towards Human-Centered (Springer Tracts in Human-Centered Computing)

✍ Scribed by Shibakali Gupta (editor), Indradip Banerjee (editor), Siddhartha Bhattacharyya (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 237
• Category: Library

✦ Synopsis

The book presents latest multi-agent technologies in human-centered computing (HCC) to provide a new research direction to enrich the human socio computations. Nowadays, the research in the field of multi-agent system (MAS) has gained a wide spread recognition due to its interdisciplinary nature and a vast versatile application domain including engineering, social science, economics, mathematics, operational research, etc. It has been proved that agents in MAS are the most appropriate technological paradigm for providing the most optimal solution for different kinds of complex real world problems that may be industrial or it might be specifically related to social problems. Keeping these features in mind, we planned to tune the research of latest multi-agent technologies and tried to compose its effect on HCC corridor. The primary audience of this book are research students of computer science, information technology and it will be also very helpful for software professionals to get developmental ideas to boost their computing activities.

✦ Table of Contents

Preface
Contents
Editors and Contributors
1 Introduction to Multi-agent Systems
1 Introduction
2 Literature Review
3 Conclusions
References
2 Human–Computer Interactions Through Multi-agent Systems: Design and Implementations
1 Introduction
2 Previous Works
2.1 Collaborative Concepts of MAS and HCI
2.2 Self-Governing Versatile Automated Systems
2.3 Intelligent Air Traffic System
2.4 Home Automation System
2.5 Mixed Reality-Oriented Framework
2.6 Psychological Aspects of Human–Agent Conversation
2.7 Tele-Health-Oriented Automated Systems
2.8 Demonstration-Oriented Training
3 Musical Scale Predictor with HCI Using Multi-agent
3.1 Mathematical Theory of Mind (ToM) Replicas in Musical Scale Prediction
4 Conclusion
References
3 Social Behavior and Reasoning Through Multi-Agent Systems
1 Introduction
2 Literature Survey
3 Fuzzy Logic-Based Multi-agent System
3.1 Fuzzy Vaccination Drive
4 Conclusions and Future Scope
References
4 Prevalence of Multi-Agent System Consensus in Cloud Computing
1 Introduction
2 Agent
2.1 Autonomy
2.2 Pro-activity
2.3 Re-activity
2.4 Communication and Cooperation
2.5 Negotiation
2.6 Learning
3 Intelligent Agent (IA)
3.1 Simple Reflex Agent
3.2 Model-Based Reflex Agent
3.3 Goal-Based Agent
3.4 Utility-Based Agent
3.5 Learning Agent
4 Related Work
5 Applications and Objective Measures of Multi-agent Systems
6 Cloud Computing and Multi-Agent Systems
7 Consensus
7.1 Consensus in Distributed System
7.2 Consensus Protocol Properties
7.3 Consensus Subjected to Communication Restraint
7.4 Leader-Following Consensus
7.5 Group Consensus
7.6 Consensus Supporting Trigger Method
7.7 Time-Limited Consensus
7.8 Multi-Consensus and Multi-Tracking
8 Conclusion
References
5 The Role of Multi-Agent Systems in IoT
1 Introduction
2 What is IoT?
3 IoT Architecture
3.1 Hardware Layer
3.2 Communication Layer
3.3 Platform Layer
3.4 Applications Layer
3.5 Security Layer
4 Internet of Things Application Domains
5 Why Use the MAS Concept for IoT-Based Systems?
6 Types of Agents in a Multi-Agent System Based on IoT
7 Simulation Multi-Agent System for IoT
8 Security in Multi-Agent System Based on IoT
9 The Use of Multi-Agent Systems in IoT Applications
9.1 Multi-Agent Systems in IoT-Based Smart City
9.2 Smart Home Based on Multi-Agent System
9.3 A Multi-agent Framework for IIoT
9.4 Smart Healthcare Based on Multi-agent System
9.5 Smart Grid Based on Multi-agent System
10 Conclusion
References
6 Agent-Based Human-Supportive Applications
1 Introduction and Literature Review
1.1 Classification
1.2 Multi-Agent System
2 Supply Chain Management
2.1 Performatives and Protocols for Negotiation
2.2 Criteria for Performative Definition and Selection
3 Expert System Design
3.1 Structure
3.2 Advantages
3.3 Application
3.4 Problems
3.5 Latest Tools for ES
3.6 Research Aspects
4 Workflow and Business Process Management System
4.1 Benefits
4.2 Disadvantages
4.3 Use of Agents
4.4 Classification
4.5 Examples and Scope of Research
5 Process Control Management
5.1 Functionalities on Different Layers
5.2 Implementation of Agents
6 Production Systems
6.1 Approaches
6.2 Phases of the Approach
6.3 Example of a Distributed Manufacturing System
7 Conclusions
References
7 Multi-Agent System Applications in Health Care: A Survey
1 Introduction
2 Background
3 Literature Survey
3.1 Architecture of Multi-Agent System
3.2 Modeling of Multi-Agent System
3.3 Planning of Multi-Agent System
3.4 Review of Multi-Agent System Applied in Healthcare Domain
3.5 Multi-Agent System Applied in Health Care
4 Multi-Agent Systems Used in Health Care
4.1 Medical Data Management
4.2 Decision Support Systems
4.3 Planning and Resource Allocation
4.4 Remote Care
4.5 Composite Systems
5 A New Scheme for Medical Diagnosis Using Mas
5.1 Model 1 (Describes the Patient Treatment at Home)
5.2 Model 2 (Describes the Patient Treatment at Hospital)
6 Conclusion
References
8 Applications of Multi-agent Systems in Intelligent Health Care
1 Introduction
1.1 Artificial Intelligence
1.2 Distributed Artificial Intelligence
2 Multi-agent Systems in Distributed Artificial Intelligence
2.1 Concept
2.2 Multi-agent Systems (MAS)
2.3 Approaches of Multi-agent Systems’ Architecture
2.4 Usage of MAS in Recent Times
2.5 Fields of MAS Application
3 MAS in Health Care
3.1 Discussion on Expert Systems
3.2 Differences Between Expert Systems and Artificial Intelligence
3.3 How MAS is Useful in Healthcare Domain
3.4 Fields of Application in Health Care
3.5 MAS for E-Health/Telemedicine
3.6 Data to be Considered
3.7 Methodologies and Applications to Achieve Accurate Results
4 Recent Trends in Application of MAS in Health Care and Its Necessity
5 Conclusion
References
9 Multi-agent Reinforcement Learning for Stock Market Strategy Analysis
1 Introduction
2 Purpose of Our Study
3 Multi-agent Reinforcement Learning
3.1 Multi-agent Reinforcement Learning Applications
3.2 Simulation Analysis of Optimal Execution
4 Models and Methods
4.1 Impact on Optimal Liquidation by Parameter κ
4.2 Different Deep Reinforcement Learning Approaches
4.3 Deep Reinforcement Learning Used for Trading Agent
4.4 Multi-agent Approach for Stock Market Strategy Analysis
5 Implementation of Multi-agent Systems for Stock Market
6 Performance Analysis of Multi-agent Systems
7 Conclusion and Future Work
References
10 Multi-agent Systems: Future Initiatives
1 Introduction
2 Intelligent MAS
References
Index