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Swarms and Network Intelligence

This reprint covers a wide range of topics related to collective intelligence, exploring the interplay between swarm intelligence, network intelligence, and other emerging technologies. The first set of chapters focuses on the behavior and mechanisms of swarming. One chapter describes a locust-inspi...

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Bibliographic Details
Main Author: Altshuler, Yaniv
Other Authors: Pereira, Francisco Camara, David, Eli
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Locusts
Bayesian Models
Models
Crowd Dynamics
Information Theory
E-participation
Evolutionary Learning
Distributed Estimation
Crowdsourcing
Automated Learning
Swarm
Human Behavior
D-optimal Design
Multi-agent
N/a
Adversarial Ai
Data Analysis
Collective Intelligence
Multi-agent Systems
Graph Network
Deep Learning
Natural Algorithms
Neural Networks
Mobile Crowdsensing
Literature Review
Uav Control
Mobile Robotics
Consensus
Crowd-sourcing
Social Media
Communication
Swarm Intelligence
Docker Swarm
Risk
Cloud
Deep Reinforcement Learning
Maximum-entropy Learning
Information Technology Industries / Bicssc
Partial Observability
Privilege Escalation
Computer Science / Bicssc
Generative Design
Wisdom Of The Crowd
Entropy
Artificial Intelligence
Exploration
Defense Evasion
Public Policy
Co-design
Social Learning
Sparse Bayesian Learning
Genetic Programming
Policymaking
Leader Election
Swarms
Cybersecurity
Socioeconomic Status
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:This reprint covers a wide range of topics related to collective intelligence, exploring the interplay between swarm intelligence, network intelligence, and other emerging technologies. The first set of chapters focuses on the behavior and mechanisms of swarming. One chapter describes a locust-inspired model of collective marching on rings, while another demonstrates the experimental validation of entropy-driven swarm exploration under sparsity constraints using sparse Bayesian learning. These studies provide new insights into the principles of swarming and its potential applications in fields such as robotics and mobile crowdsensing. The next set of chapters discusses the integration of swarm intelligence with other emerging technologies such as deep learning and graph theory. These studies show how swarm intelligence can be combined with other advanced technologies to solve complex problems and improve decision-making processes. The reprint also covers the topic of network intelligence, including the study of social network analysis, Twitter user activity, and crowd-sourced financial predictions. These studies provide insights into how network intelligence can be harnessed to understand social dynamics and improve decision-making processes in various domains. The reprint concludes with a chapter that proposes a generative design approach for the efficient mathematical modeling of complex systems.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (234 p.)
ISBN:9783036579207
books978-3-0365-7921-4
9783036579214

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