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...
Збережено в:
| Формат: | Online |
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| Мова: | Англійська |
| Опубліковано: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Предмети: | |
| Онлайн доступ: | ONIX_20230714_9783036579207_81 |
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| _version_ | 1869531373753073664 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-101382 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1013822024-03-30T12:51:30Z Swarms and Network Intelligence Altshuler, Yaniv Pereira, Francisco Camara David, Eli generative design automated learning evolutionary learning co-design genetic programming human behavior socioeconomic status data analysis social media crowd-sourcing wisdom of the crowd social learning Bayesian models risk Docker Swarm leader election privilege escalation defense evasion cloud collective intelligence crowdsourcing policymaking public policy e-participation literature review deep learning cybersecurity artificial intelligence swarm intelligence adversarial AI information theory entropy models neural networks communication multi-agent deep reinforcement learning partial observability distributed estimation Sparse Bayesian Learning exploration swarm multi-agent systems consensus D-optimal design mobile crowdsensing UAV control graph network maximum-entropy learning mobile robotics swarms crowd dynamics natural algorithms locusts n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science 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. 2023-07-14T14:28:50Z 2023-07-14T14:28:50Z 2023 book ONIX_20230714_9783036579207_81 9783036579207 9783036579214 https://directory.doabooks.org/handle/20.500.12854/101382 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7478 https://mdpi.com/books/pdfview/book/7478 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7921-4 10.3390/books978-3-0365-7921-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036579207 9783036579214 234 Basel open access |
| spellingShingle | generative design automated learning evolutionary learning co-design genetic programming human behavior socioeconomic status data analysis social media crowd-sourcing wisdom of the crowd social learning Bayesian models risk Docker Swarm leader election privilege escalation defense evasion cloud collective intelligence crowdsourcing policymaking public policy e-participation literature review deep learning cybersecurity artificial intelligence swarm intelligence adversarial AI information theory entropy models neural networks communication multi-agent deep reinforcement learning partial observability distributed estimation Sparse Bayesian Learning exploration swarm multi-agent systems consensus D-optimal design mobile crowdsensing UAV control graph network maximum-entropy learning mobile robotics swarms crowd dynamics natural algorithms locusts n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science Swarms and Network Intelligence |
| title | Swarms and Network Intelligence |
| title_full | Swarms and Network Intelligence |
| title_fullStr | Swarms and Network Intelligence |
| title_full_unstemmed | Swarms and Network Intelligence |
| title_short | Swarms and Network Intelligence |
| title_sort | swarms and network intelligence |
| topic | generative design automated learning evolutionary learning co-design genetic programming human behavior socioeconomic status data analysis social media crowd-sourcing wisdom of the crowd social learning Bayesian models risk Docker Swarm leader election privilege escalation defense evasion cloud collective intelligence crowdsourcing policymaking public policy e-participation literature review deep learning cybersecurity artificial intelligence swarm intelligence adversarial AI information theory entropy models neural networks communication multi-agent deep reinforcement learning partial observability distributed estimation Sparse Bayesian Learning exploration swarm multi-agent systems consensus D-optimal design mobile crowdsensing UAV control graph network maximum-entropy learning mobile robotics swarms crowd dynamics natural algorithms locusts n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | generative design automated learning evolutionary learning co-design genetic programming human behavior socioeconomic status data analysis social media crowd-sourcing wisdom of the crowd social learning Bayesian models risk Docker Swarm leader election privilege escalation defense evasion cloud collective intelligence crowdsourcing policymaking public policy e-participation literature review deep learning cybersecurity artificial intelligence swarm intelligence adversarial AI information theory entropy models neural networks communication multi-agent deep reinforcement learning partial observability distributed estimation Sparse Bayesian Learning exploration swarm multi-agent systems consensus D-optimal design mobile crowdsensing UAV control graph network maximum-entropy learning mobile robotics swarms crowd dynamics natural algorithms locusts n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20230714_9783036579207_81 |