A Generic Model for Swarm Intelligence and Its Validations
December 12, 2017 Β· Declared Dead Β· π WSEAS Transactions on Information Science and Applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Wenpin Jiao
arXiv ID
1712.04182
Category
cs.AI: Artificial Intelligence
Citations
1
Venue
WSEAS Transactions on Information Science and Applications
Last Checked
4 months ago
Abstract
The modeling of emergent swarm intelligence constitutes a major challenge and it has been tackled in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individu-als determine their behaviors based on their internal contradictions whilst they associate and interact to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the de-velopment of individuals' internal contradictions and the interactions taking place between individuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configurations of individuals' internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applicability. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted