Distributed Artificial Intelligence: A Modern Approach

✍ Scribed by Satya Prakash Yadav (editor), Dharmendra Prasad Mahato (editor), Nguyen Thi Dieu Linh (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 337
• Category: Library

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Distributed Artificial Intelligence
1.1 Introduction
1.2 Why Distributed Artificial Intelligence?
1.3 Characteristics of Distributed Artificial Intelligence
1.4 Planning of DAI Multi-Agents
1.5 Coordination among Multi-Agents
1.5.1 Forestalling Mobocracy or Confusion
1.5.2 Meeting Overall Requirements
1.5.3 Distributed Skill, Resources, and Data
1.5.4 Dependency among the Agents
1.5.5 Efficiency
1.6 Communication Modes among the Agents
1.7 Categories of RPC
1.8 Participation of Multi-Agents
1.8.1 Fully Cooperative Architecture
1.8.2 Partial Cooperative Architecture
1.9 Applications of DAI
1.9.1 Electricity Distribution
1.9.2 Telecommunications Systems
1.9.3 Database Technologies for Service Order Processing
1.9.3.1 Concurrent Engineering
1.9.3.2 Weather Monitoring
1.9.3.3 Intelligent Traffic Control
1.10 Conclusion
References
Chapter 2 Intelligent Agents
2.1 Introduction
2.2 Need for Evolving Agents in Evolutionary Software Systems
2.2.1 Change of Requirements
2.2.2 Need for an Evolving System
2.2.3 Software System
2.2.4 Evolving Software System
2.3 Agents
2.3.1 Evolving Agents
2.3.2 Agent Architecture
2.3.3 Application Domain
2.3.3.1 Types of Agents
References
Chapter 3 Knowledge-Based Problem-Solving: How AI and Big Data Are Transforming Health Care
3.1 Introduction
3.2 The Role of AI, Big Data, and IoT in Health Care
3.3 Image-Based Diagnosis
3.4 Big Data Analytics Process Using Machine Learning
3.5 Discussion
3.6 Conclusion
References
Chapter 4 Distributed Artificial Intelligence for Document Retrieval
4.1 Introduction
4.2 Proposed Research
4.2.1 Improving Precision
4.3 General-Purpose Ranking
4.4 Structure-Weighted Ranking
4.5 The Structure-Weighted/Learned Function
4.6 Improving Recall and Precision
4.6.1 Stemming
4.6.2 Relevance Feedback
4.6.3 Thesaurus
4.7 Preliminary Results
4.8 Scope for Distributed AI in This Process
4.9 Benefits of Decentralized Search Engines
4.10 Discussion
4.11 Conclusion
References
Chapter 5 Distributed Consensus
5.1 Introduction
5.2 Nakamoto Consensus
5.2.1 Nakamoto Consensus Working
5.2.1.1 Proof of Work
5.2.1.2 Block Selection
5.2.1.3 Scarcity
5.2.1.4 Incentive Structure
5.2.2 Security of Bitcoin
5.2.3 The PoW Algorithm
5.2.4 Proof of Stake
5.2.5 Proof of Burn
5.2.6 Difficulty Level
5.2.7 Sybil Attack
5.2.7.1 Eclipse Attack
5.2.8 Hyperledger Fabric: A Blockchain Development
5.3 Conclusions and Discussions
References
Chapter 6 DAI for Information Retrieval
6.1 Introduction
6.2 Distributed Problem-Solving
6.3 Multiagents
6.4 A Multiagent Approach for Peer-to-Peer-Based Information Recoupment Systems
6.4.1 A Mediator-Free Framework
6.4.2 Agent-View Algorithm
6.4.3 Distributed Search Algorithms
6.5 Blackboard Model
6.6 DIALECT 2: An Information Recoupment System
6.6.1 The Control in Blackboard Systems
6.6.2 Control in DIALECT 2
6.6.2.1 The Linguistic Parser
6.6.2.2 The Reformation Module
6.7 Analysis and Discussion
6.8 Conclusion
References
Chapter 7 Decision Procedures
7.1 Motivation
7.2 Introduction
7.3 Distributed Artificial Intelligence
7.4 Applying Artificial Intelligence to Decision-Making
7.5 Automated Decision-Making by AI
7.5.1 Impact of Automated Decision System
7.5.2 Forms of Automated Decision System
7.5.3 Application of Automated Decision System
7.5.4 Cyber Privacy Concerns
7.5.5 Discussion and Future Impact
7.6 Cooperation in Multi-Agent Environments
7.6.1 Notations and Workflow
7.6.2 Action Independence
7.7 Game Theory Scenario
7.8 Data-Driven or AI-Driven
7.8.1 Human Judgment
7.8.2 Data-Driven Decision-Making
7.8.3 Working of Data-Driven Decisions
7.8.4 AI-Driven Decision-Making
7.8.5 Leveraging Human and AI-Driven Workflows Together
7.9 Calculative Rationality
7.10 Meta-Level Rationality and Meta-Reasoning
7.11 The Role of Decision Procedures in Distributed Decision-Making
7.12 Advantages of Distributed Decision-Making
7.13 Optimization Decision Theory
7.13.1 Multi-Level (Hierarchical) Algorithms
7.14 Dynamic Programming
7.15 Network Flow
7.16 Large-Scale Decision-Making (LSDM)
7.16.1 Key Elements in an LSDM Model
7.17 Conclusion
Reference
Chapter 8 Cooperation through Communication in a Distributed Problem-Solving Network
8.1 Introduction
8.2 Distributed Control System
8.2.1 Design Decisions
8.2.2 Host Node Software Communication
8.2.3 Convolutional Software Node Network
8.2.4 Assessment of Distributed Situation
8.2.5 Computer-Aided Control Engineering (CACE)
8.2.6 Knowledge Base
8.2.7 Training Dataset
8.3 Motivation and Development of the ICE Architecture
8.3.1 History of ICE Model
8.3.1.1 Operators on Information States
8.3.1.2 Relations to Observable Quantum Mechanics
8.3.1.3 The Influence of Sociology and Intentional States
8.3.2 Requirements of a Theory of Animal and Robotics Communication
8.4 A Brief Conceptual History of Formal Semantics
8.4.1 Tarski Semantics
8.4.2 Possible World Semantics
8.4.3 Semantics of Temporal Logic
8.4.4 Limitations of Kripke Possible World Semantics
8.5 Related Work
8.6 Dynamic Possible World Semantics
8.7 Situation Semantics and Pragmatics
8.8 Modeling Distributed AI Systems as a Distributed Goal Search Problem
8.9 Discussion
8.10 Conclusion
References
Chapter 9 Instantiating Descriptions of Organizational Structures
9.1 Introduction
9.1.1 Example of Organizational Structure
9.1.2 Purpose
9.1.3 Components
9.1.3.1 Obligations
9.1.3.2 Assets
9.1.3.3 Information
9.1.3.4 Apparatuses
9.1.3.5 Experts and Subcontractors
9.1.4 Relation between Components
9.1.4.1 Correspondence
9.1.4.2 Authority
9.1.4.3 Area, Proximity, and so on
9.1.5 Description of the Organizational Structures with EFIGE
9.1.6 The Constraint Solution Algorithm
9.1.6.1 Requirement Propagation
9.1.6.2 Imperative Utility
9.2 Comparative Study of Organization Structure
9.3 Conclusion
References
Chapter 10 Agora Architecture
10.1 Introduction
10.1.1 Characteristics of System for which Agora Is Useful
10.2 Architecture of Agora
10.3 Agora’s Virtual Machine
10.3.1 Element Cliques (EC)
10.3.2 Knowledge Source (KS)
10.3.3 Mapping of KS into Mach layer
10.3.4 Frameworks
10.3.4.1 Typical Framework Tools
10.3.4.2 Knowledge Base: CFrame
10.4 Examples of Systems Built Using Agora
10.4.1 Intelligent Transport System (ITS)
10.4.1.1 Architecture of Agora ITS Framework
10.4.1.2 Agora ITS Applications
10.4.2 CMU Speech Recognition System
10.5 Application of Agora as a Minimal Distributed Protocol for E-Commerce
10.5.1 Basic Protocol
10.5.2 Accounts
10.5.3 Transactions
10.5.4 Properties of Agora Protocol
10.5.4.1 Minimal
10.5.4.2 Distribution
10.5.4.3 Authentication
10.5.4.4 Security
10.5.5 Enhanced Protocol to Regulate Fraud
10.5.5.1 New Message
10.5.5.2 Batch Processing
10.5.5.3 Selection of Parameter
10.5.5.4 Online Arbitration
References
Chapter 11 Test Beds for Distributed AI Research
11.1 Introduction
11.2 Background
11.3 Tools and Methodology
11.3.1 MACE
11.3.1.1 MACE System
11.3.2 Actor Model
11.3.3 MICE Testbed
11.3.4 ARCHON
11.3.4.1 Multiagent Environment
11.3.4.2 The ARCHON Architecture
11.3.5 Distributed Vehicle Monitoring Testbed (DVMST)
11.3.6 AGenDA Testbed
11.3.6.1 Architectural Level
11.3.6.2 System Development Level
11.3.6.3 Other Testbeds for DAI
11.4 Conclusion
References
Chapter 12 Real-Time Framework Competitive Distributed Dilemma
12.1 Introduction
12.2 Real-Time Route Guidance Distributed System Framework
12.3 Experts Cooperating
12.4 A Distributed Problem-Solving Perspective
12.5 Caveats for Cooperation
12.6 Task Sharing
12.7 Result-Sharing
12.8 Task-Sharing and Result-Sharing: A Comparative Analysis
12.9 Conclusion
References
Chapter 13 Comparative Studied Based on Attack Resilient and Efficient Protocol with Intrusion Detection System Based on Deep Neural Network for Vehicular System Security
13.1 Introduction
13.2 Related Work
13.3 Background
13.3.1 Processing Phase
13.3.2 Training Phase
13.4 Intrusion Detection System
13.5 IDS with Machine Learning
13.6 Proposed Technique
13.6.1 Proposed Deep Neural Network Intrusion Detection System
13.6.2 Training the Deep Neural Network Structure
13.6.2.1 ANN Parameters
13.6.2.2 Input Layer’s Neurons
13.6.2.3 Hidden Layer’s Neurons
13.6.2.4 Output Layer’s Neurons
13.6.2.5 Transfer Function
13.7 Simulation Parameters
13.7.1 Average End-to-End Delay
13.7.2 Average Energy Consumption
13.7.3 Average Network Throughput
13.7.4 Packet Delivery Ratio (PDR)
13.8 Conclusion
References
Chapter 14 A Secure Electronic Voting System Using Decentralized Computing
14.1 Introduction
14.2 Background and Motivation
14.2.1 Secret Ballot
14.2.2 One Man, One Vote
14.2.3 Voter Eligibility
14.2.4 Transparency
14.2.5 Votes Accurately Recorded and Counted
14.2.6 Reliability
14.3 Literature Survey
14.4 Main Contributions
14.4.1 Variables of the Contract
14.4.2 Preparing the Ballot
14.4.3 Vote Counting
14.5 E-Voting and Blockchain
14.5.1 Cryptography
14.6 Use of Blockchain in Voting System
14.7 Result and Analysis
14.8 Conclusion
References
Chapter 15 DAI for Document Retrieval
15.1 Introduction
15.2 Artificial Intelligence
15.2.1 Some Real-Life Examples of AI
15.2.2 Advantages of AI
15.2.3 Information Retrieval
15.2.4 Information Retrieval Assessment
15.3 Distributed Artificial Intelligence
15.3.1 Introduction to Distributed Artificial Intelligence
15.3.2 Distributed Artificial Intelligence Tools
15.3.3 Complete Document and Document Interchange Format
15.3.4 Data Network Architecture for Distributed Information Retrieval
15.3.5 Types of DAI
15.3.6 Challenges in Distributed AI
15.3.7 The Objectives of Distributed Artificial Intelligence
15.3.8 Areas in Which DAI Is Implemented
15.3.9 Software Agents
15.4 Conclusion
References
Chapter 16 A Distributed Artificial Intelligence: The Future of AI
16.1 Introduction
16.2 Background and Challenges of AI
16.2.1 Hardware for AI
16.2.2 Platform and Programming Languages for AI
16.2.3 Challenges of AI
16.3 Components and Proposed Environment of Distributed AI
16.3.1 Graphical Processing Unit (GPU)
16.3.2 Storage
16.3.3 High-Speed Reliable Network
16.3.4 Proposed Distributed Environment of DAI
16.4 Application of Distributed AI
16.4.1 Healthcare Systems
16.4.2 Agriculture Systems
16.4.3 E-Commerce
16.5 Future Scope
16.6 Conclusion
References
Chapter 17 Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks
17.1 Introduction
17.2 Network Model
17.3 Traffic Management Model
17.3.1 Mobile Agent Unit
17.3.2 Infrastructure Unit
17.4 Performance Evaluation
17.5 Conclusion
References
Chapter 18 Data Science and Distributed AI
18.1 Introduction
18.2 Inspiration
18.3 Distributed Sensor Networks
18.4 Associations Tested
18.4.1 Human-Based Network Experiments
18.4.2 Examinations with Machine Networks
18.5 An Abstract Model for Problem-Solving
18.5.1 The HSII Organization: A Production System Approach
18.5.2 Hearsay-II Multiprocessing Mechanisms
18.5.3 Nearby Context
18.4.4 Data Integrity
18.5.5 Contextual Analysis
18.5.6 HSII Multiprocessor Performance Analysis through Simulation
18.5.7 The HSII Speech Understanding System: The Simulation Configuration
18.6 Hierarchical Distribution of Work
18.7 Agora
18.8 Exploratory Outcomes for Image Processing
18.9 Summary and Conclusions
References
Index