Artificial Swarm Intelligence

Rosenberg, Louis; Willcox, Gregg

doi:10.1007/978-3-030-29516-5_79

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…Artificial Swarm Intelligence appears to be an interesting research direction to explore, either aiming at designing "artificial ants", for instance or setting up swarms of networked humans. In fact, recent progress has been made in the case of "human swarms" or "hive minds" [56] but not so much in the context of mathematics education yet, as far as we know.…”

Section: Open Ends and Interdisciplinary Challengesmentioning

confidence: 99%

Embodiment and metaphorising in the learning of mathematics

Soto-Andrade

Díaz-Rojas

Valdés-Zorrilla

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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In this paper we undertake a critical reading of the Ouroboric (circular) relationship between the guiding metaphors of artificial intelligence and those of natural human intelligence, which has evolved from a vicious circle, triggered by the metaphorising of cognition as information processing, to a virtuous one, fostered by the advent of the embodied and enactive turn in cognitive science. We describe then our own experimental and theoretical approach to mathematical thinking and learning, where metaphorization plays a key role, besides embodiment and enaction. We comment on some concrete examples of mathematical thinking with different types of learners, in combinatorics, arithmetic, geometry and probability, where idiosyncratic metaphorization emerges and enacting and embodiment make a dramatic difference. We outline finally some significant challenges to contemporary AI, cognitive sciences, and mathematics proper, suggested by our examples. These challenges involve metaphorising as the tip of the iceberg in human cognition, metaphorising in intelligent systems, Ouroboric circularity, mathematical collective improvisation as an analogue of musical improvisation, group creativity and human swarm intelligence.

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Section: Open Ends and Interdisciplinary Challengesmentioning

confidence: 99%

Embodiment and metaphorising in the learning of mathematics

Soto-Andrade

Díaz-Rojas

Valdés-Zorrilla

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The actions of birds when traveling from one position to another influenced this algorithm. As it is the case with animals, they travel instinctively to migrate or search for food in the form of groups; this algorithm often assumes the existence of entities with mathematical values (numbers of matrices) and these values are constantly changing to achieve the optimal values or the optimal solution to a mathematical problem which is difficult for us to solve by conventional classical methods [17].…”

Section: Particle Swarm Algorithmmentioning

confidence: 99%

Compare between PSO and artificial bee colony optimization algorithm in detecting DoS attacks from network traffic

Mohammad

Jawhar

2022

TELKOMNIKA

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Our world today relies heavily on informatics and the internet, as computers and communications networks have increased day by day. In fact, the increase is not limited to portable devices such as smartphones and tablets, but also to home appliances such as: televisions, refrigerators, and controllers. It has made them more vulnerable to electronic attacks. The denial of service (DoS) attack is one of the most common attacks that affect the provision of services and commercial sites over the internet. As a result, we decided in this paper to create a smart model that depends on the swarm algorithms to detect the attack of denial of service in internet networks, because the intelligence algorithms have flexibility, elegance and adaptation to different situations. The particle swarm algorithm and the bee colony algorithm were used to detect the packets that had been exposed to the DoS attack, and a comparison was made between the two algorithms to see which of them can accurately characterize the DoS attack.

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“…Swarm intelligence (SI) is a specialized form of collective intelligence used to improve group decision-making in a wide range of biological species, from swarming bees and schooling fish to flocking birds. In recent years, a technology called artificial swarm intelligence (ASI) has been developed to enable similar benefits in networked human groups [41,42]. A software platform called swarm was used in this study to enable networked human agents to make assessments by working together using the ASI technology.…”

Section: Related Work and Key Conceptsmentioning

confidence: 99%

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications

et al. 2022

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Radiologists today play a central role in making diagnostic decisions and labeling images for training and benchmarking artificial intelligence (AI) algorithms. A key concern is low inter-reader reliability (IRR) seen between experts when interpreting challenging cases. While team-based decisions are known to outperform individual decisions, inter-personal biases often creep up in group interactions which limit nondominant participants from expressing true opinions. To overcome the dual problems of low consensus and interpersonal bias, we explored a solution modeled on bee swarms. Two separate cohorts, three board-certified radiologists, (cohort 1), and five radiology residents (cohort 2) collaborated on a digital swarm platform in real time and in a blinded fashion, grading meniscal lesions on knee MR exams. These consensus votes were benchmarked against clinical (arthroscopy) and radiological (senior-most radiologist) standards of reference using Cohen’s kappa. The IRR of the consensus votes was then compared to the IRR of the majority and most confident votes of the two cohorts. IRR was also calculated for predictions from a meniscal lesion detecting AI algorithm. The attending cohort saw an improvement of 23% in IRR of swarm votes (k = 0.34) over majority vote (k = 0.11). Similar improvement of 23% in IRR (k = 0.25) in 3-resident swarm votes over majority vote (k = 0.02) was observed. The 5-resident swarm had an even higher improvement of 30% in IRR (k = 0.37) over majority vote (k = 0.07). The swarm consensus votes outperformed individual and majority vote decision in both the radiologists and resident cohorts. The attending and resident swarms also outperformed predictions from a state-of-the-art AI algorithm.

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Artificial Swarm Intelligence

Cited by 21 publications

References 25 publications

Embodiment and metaphorising in the learning of mathematics

Embodiment and metaphorising in the learning of mathematics

Compare between PSO and artificial bee colony optimization algorithm in detecting DoS attacks from network traffic

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications

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