Artificial Swarm Intelligence

@inproceedings{Rosenberg2019ArtificialSI,
  title={Artificial Swarm Intelligence},
  author={Louis B. Rosenberg and Gregg Willcox},
  booktitle={Intelligent Systems with Applications},
  year={2019}
}
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to… 
View via Publisher
11s1ty2quyfy2qbmao3bwxzc-wpengine.netdna-ssl.com
11 Citations
Background Citations
4
Methods Citations
1

11 Citations

Dense Neural Network used to Amplify the Forecasting Accuracy of real-time Human Swarms

This study explores whether the rich behavioral data collected during "swarming" can be used to further increase the accuracy of swarm-based forecasts and develops a dense neural network to process the data collected and generate a Conviction Index for each forecast that estimates its expected accuracy.

Prioritizing Policy Objectives in Polarized Groups using Artificial Swarm Intelligence

This study compares the output of traditional methods such as Majority vote and Borda count to the Swarm method on a set of divisive policy issues and finds that the rankings generated using ASI and the Borda Count methods are often rated as significantly more satisfactory than those generated by the Majority vote system.

Group Sales Forecasting, Polls vs. Swarms

This study explores sales forecasting performed by human groups and compares the accuracy of group forecasts generated by traditional polling to those made using real-time Artificial Swarm Intelligence (ASI), a technique which has been shown to amplify the forecasting accuracy of human teams in a wide range of fields.

A Graph-Transformational Approach to Swarm Computation

It is shown that cellular automata and particle swarms can be simulated by graph-transformational swarms and an illustrative example of a simple ant colony the ants of which forage for food choosing their tracks randomly based on pheromone trails is given.

Embodiment and metaphorising in the learning of mathematics

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…

TELKOMNIKA Telecommunication Computing Electronics and Control

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 is created.

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

Bi-Fitness Swarm Optimizer: Blockchain Assisted Secure Swarm Intelligence Routing Protocol for MANET

The proposed work has zone-wise Authentication by using BLAKE-3 hashing algorithm and deployment of guard nodes throughout the network and Sensitivity Aware Data Encryption algorithm is used to evaluate the sensitivity level of a packet and encrypted using blockchain based SALSA 20.

Software Architecture for Distributed Edge Computing Systems

A software architectural framework is proposed to facilitate sustainability and evolvability in ecosystems of Systems of Systems where all systems will be designed for knowledge-based automation, driving the decentralization of computing, communications and business processes.

A Dynamic Cyber Security Situational Awareness Framework for Healthcare ICT Infrastructures

A Cyber Security Situational Awareness Framework that aims to improve the security and resilience of the overall HCII and considers bio inspired Swarm Intelligence and its inherent features with the main principles of the Risk and Privacy assessment and management and Incident handling to ensure security and resilient of healthcare service delivery.

35 References

Artificial Swarms find Social Optima : (Late Breaking Report)

  • Louis B. RosenbergG. Willcox
  • Computer Science
    2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
  • 2018
“swarming” was significantly more effective at enabling groups to converge on the Social Optima than three common voting methods: (i) Plurality voting, (ii) Borda Count and (iii) Condorcet pairwise voting.

Artificial Swarm Intelligence vs Vegas Betting Markets

This study compares the predictive ability of ASI systems against large betting markets when forecasting sporting events and finds that the ASI system outperformed Vegas by chance, indicating a significant result.

Artificial Swarm Intelligence amplifies accuracy when predicting financial markets

It is suggested that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy of financial forecasts.

Amplifying the Social Intelligence of Teams Through Human Swarming

Statistical analysis found that the groups working as ASI swarms had significantly higher social sensitivity than individuals working alone or groups working together by plurality vote (p<O.OOI), which suggests that when groups reach decisions as real-time ASi swarms, they make better use of their social intelligence than when working Alone or by traditional group vote.

Human swarming, a real-time method for parallel distributed intelligence

  • Louis B. Rosenberg
  • Computer Science
    2015 Swarm/Human Blended Intelligence Workshop (SHBI)
  • 2015
Modeled after biological swarms, the UNU platform enables online groups to work together in synchrony, forging a unified dynamic system that can quickly answer questions and make decisions by exploring a decision-space and converging on a preferred solution.

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms,…

Amplifying prediction accuracy using Swarm A.I.

The present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems online.

Human Swarms, a real-time method for collective intelligence

Modeled after biological swarms, the UNUM platform enables online groups to work in real-time synchrony, collaboratively exploring a decision-space and converging on preferred solutions in a matter of seconds.

Artificial Swarm Intelligence, a Human-in-the-Loop Approach to A.I

UNU is introduced, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI), Modeled after biological swarms, UNU enables large groups of networking users to work together inreal time synchrony, forging a unified dynamic system that can quickly answer questions and make decisions.

Dense Neural Network used to Amplify the Forecasting Accuracy of real-time Human Swarms

This study explores whether the rich behavioral data collected during "swarming" can be used to further increase the accuracy of swarm-based forecasts and develops a dense neural network to process the data collected and generate a Conviction Index for each forecast that estimates its expected accuracy.