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What Do Ants Know That We Don’t?

This from Wired.

During the 130 million years or so that ants have been around, evolution has tuned ant colony algorithms to deal with the variability and constraints set by specific environments.

Ant colonies use dynamic networks of brief interactions to adjust to changing conditions. No individual ant knows what’s going on. Each ant just keeps track of its recent experience meeting other ants, either in one-on-one encounters when ants touch antennae, or when an ant encounters a chemical deposited by another.

Such networks have made possible the phenomenal diversity and abundance of more than 11,000 ant species in every conceivable habitat on Earth. So Anternet, and other ant networks, have a lot to teach us. Ant protocols may suggest ways to build our own information networks…

Speaking of what we can learn from ants, here is a project that I’m co-editing.

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An Evaluation of the Model of Stigmergy in a RoboCup Rescue Multiagent System

ADVANCES IN ARTIFICIAL INTELLIGENCE

A lot of scientists study the behavior of insect’s colony like ants, wasps and bees. Through these researches, it is possible to establish patterns used by a group of insects and apply these patterns in other domains. In this paper it will be showed the use of stigmergy in a rescue situation using the RoboCup Rescue simulator. We performed a set of experiments using a metaphor based on the behavior of an ant colony, where the communication between agents is done through the environment. We measured the performance of the ant-based algorithm, expecting to figure out the feasibility of using swarm intelligence in a rescue situation. We compared the results of using stigmergy against a multiagent system based on direct messages. The results showed that the use of stigmergy can outperform the use of direct messages.

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Stigmergy 3.0: From Ants to Economies

Marge and my intro now available as an uncorrected proof. Stay tuned for the rest of the papers comprising this special issue.

According to Andy Clark “[M]uch of what goes on in the complex world of humans, may thus, somewhat surprisingly, be understood in terms of so-called stigmergic algorithms” (Clark, 1996, p. 279; 1997, p. 186). Pierre-Paul Grassé, the brilliant mind who first conceptualized the notion probably wouldn’t disagree (Grassé, 1959). Grassé was as much a zoologist as he was an entomologist. Under his editorship the monumental (17-volume) Traité de Zoologie, Anatomie, Systématique, Biologie was guided.

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Particle swarm optimization

The latest issue of Swarm Intelligence is now available featuring this paper “A speculative approach to parallelization in particle swarm optimization.” The original formulation of PSO is due to Kennedy, J., Eberhart, R. C., with Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann.

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From Ants to Economies

Readers with some familiarity with the eclectic content found on this website will be aware that the humble ant features strongly. Here is an article that offers a brief and accessible discussion of an excellent symposium to be found in Behavioral Ecology that features Mark Moffett’s work.