swarm-intelligence

Demonstrates flocking capabilities of a swarm network: each “dumb” particle is programmed with behavioral simple logic to align with neighboring particles and avoid set obstacles. This swarm of 200 particles become a “superorganism” much like a slime mold, ant colony, flock of birds, or a fish vortex.

Rendering shows the paths created by each particle that avoids the red orbs and eventually align together at the end. Software: Maya+brainbugz plugin

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Automated world: Amazon unveils its eighth generation fulfillment center

Shiny happy ant-intelligence-driven warehouse robots.

On Cyber Monday, the biggest online shopping day of the year, Amazon reveals its newest generation fulfillment center utilizing robotics, vision systems and high-end technology to speed up order delivery times for customers

[read more] [more about Kiva] [via bruces]

From honeybee swarms we’ve learned that groups can reliably make good decisions in a timely matter as long as they seek diversity of knowledge. By studying termite mounds we’ve seen how even small contributions to a shared project can create something useful. Finally, flocks of starlings have shown us how, without direction from a single leader, members of a group can coordinate their behavior with amazing precision simply by paying attention to their nearest neighbor.
—  Peter Miller, The Smart Swarm
Watch on hhk4.tumblr.com

‘Shoal’ by Trokia … f'ing insane!!!!

Warehouse Robots Get Smarter With Ant Intelligence

Amazon may have just gotten its claws into Kiva Systems, but there’s more than one company out there looking to automate warehouses with smart little robots. At the Fraunhofer Institute for Material Flow and Logistics, researchers are looking for ways to make warehouse robots smarter and more efficient by getting them to communicate and cooperate like a swarm of ants.

A swarm is just exactly what you want with warehouse robots. There are a lot of them, and they’re all identical and interchangeable, cooperating to complete complex tasks by combining simple actions. The big difference between a swarm of (say) ants and a swarm of (say) robots is that the ants don’t have any high-level control: each ant has its own little tiny brain, and even though ants have specific tasks that they are directed (or bred) to perform, they decide on an individual level how to go about carrying out their instructions.

What Fraunhofer is trying to do is mimic the ant swarm system with robots. For example, instead of having one central computer control the movements every robot (as with Kiva), Fraunhofer’s system utilizes robots that make their own decisions with onboard computers. Each robot communicates with all the other robots in the swarm simultaneously using WLAN, and they use algorithms based on a model for how ants forage for food to cooperatively decide which of them should go where and do what. […]

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Stigmergy

The term “stigmergy” was introduced by French biologist Pierre-Paul Grassé in 1959 to refer to termite behavior. He defined it as: “Stimulation of workers by the performance they have achieved.” It is derived from the Greek words στίγμα stigma “mark, sign” and ἔργον ergon “work, action”, and captures the notion that an agent’s actions leave signs in the environment, signs that it and other agents sense and that determine and incite their subsequent actions. Later on, a distinction was made between the stigmergic phenomenon, which is specific to the guidance of additional work, and the more general, non-work specific incitation, for which the term sematectonic communication was coined by E. O. Wilson, from the Greek words σῆμα sema “sign, token”, and τέκτων tecton “craftsman, builder”: “There is a need for a more general, somewhat less clumsy expression to denote the evocation of any form of behavior or physiological change by the evidences of work performed by other animals, including the special case of the guidance of additional work.”

Stigmergy was first observed in social insects. For example, ants exchange information by laying down pheromones (the trace) on their way back to the nest when they have found food. In that way, they collectively develop a complex network of trails, connecting the nest in the most efficient way to the different food sources. When ants come out of the nest searching for food, they are stimulated by the pheromone to follow the trail towards the food source. The network of trails functions as a shared external memory for the ant colony.

In computer science, this general method has been applied in a variety of techniques called ant colony optimization, which search for solutions to complex problems by depositing “virtual pheromones” along paths that appear promising.

Other eusocial creatures, such as termites, use pheromones to build their complex nests by following a simple decentralized rule set. Each insect scoops up a ‘mudball’ or similar material from its environment, invests the ball with pheromones, and deposits it on the ground, initially in a random spot. However, termites are attracted to their nestmates’ pheromones and are therefore more likely to drop their own mudballs on top of their neighbors’. The larger the heap of mud becomes, the more attractive it is, and therefore the more mud will be added to it (positive feedback). Over time this leads to the construction of pillars, arches, tunnels and chambers.

Stigmergy has even been observed in bacteria, various species of which differentiate into distinct cell types and which participate in group behaviors that are guided by sophisticated temporal and spatial control systems.

Stigmergy also occurs with social movements, such as the arc from Wikileaks’ cable release in Summer 2010 to the developments in global Occupy movement. [8] The steady rise of Wikipedia and the Open Source software movement has been one of the big surprises of the 21st century, threatening stalwarts such as Microsoft and Britannica, while simultaneously offering insights into the emergence of large-scale peer production and the growth of gift economy.

(I have been working on something in conjunction with swarm intelligence, hence this.)

R&D: Swarm Urbanism - Neil Leach

This article was first published on the Architectural Design ‘Digital Cities’ Edition.

Description: 'swarm urbanism’ is a term used in urban design to understand the formation of a traditional city. Cities form themselves without any apparent visible force beneath them. What if there is a rule, a hidden force driving people to built their city and when the city is somewhat built it starts to influence its people how it is to be built? According to Neil in his own term: “The city operates as a dynamic, adaptive system, based on interactions with neighbors, informational feedback loops, pattern recognition and indirect control…Moreover, like any other population composed of a large number of smaller discrete elements, such as colonies of ants, flocks of birds, networks of neurons or even the global economy, it displays a bottom-up collective intelligence that is more sophisticated than the behavior of its parts. In short, the city operates through a form of ‘swarm intelligence’.” In Melbourne, a group of young professionals invented a software named 'Kokkugia’ through which this concept of 'swarm intelligence’ can be modeled within a computational framework. Several research is still going on. Again in Neils term in his last paragraph of the writing : “The task of design therefore would be to anticipate what would have evolved over time from the interaction between inhabitants and city…the task of design is to ‘fast forward’ that process of evolution, so that we envisage – in the ‘future perfect’ tense – the way in which the fabric of the city would have evolved in response to the impulses of human habitation…Quite how such a relationship could be modelled digitally remains an interesting challenge for urban designers.”

Source: http://neilleach.files.wordpress.com/2009/09/swarm-urbanism_056-063_lowres.pdf.

(Submitted by: Nahid Haimonty)

Swarm intelligence helps to configure an environment that is increasingly confronted with the task of organizing highly engineered and interconnected systems and also with the task of modelling complex correlations.
— 

Sebastian Vehlken

Zootechnologies: Swarming as a Cultural Technique

Theory, Culture & Society 30(6) 110–131, p. 111

MANTIS 077 + SWARM INTELLIGENCE

DOWNLOAD  STREAM  ITUNES  RSS

hour 1 / DVNT

Mentally Unfocused – Something Out of Nothing [dub]
The Eerier Child – Thir13en Ghosts [Mindtrick Records]
Sunken Foal – Pearl Bearings [Acroplane]
FZV – 54rlbrks [Anathematica Recordings]
FZV – muffdub [Anathematica Recordings]
Xi – Nitelite [XLR8R]
Bas Mooy – Play Dead (Remix 1 the short edit) [Planet Rhythm]
Marcel Dettman – Shift (Norman Nodge remix) [Ostgut]
Coefficient – Inflation Expansion [Labrynth] forthcoming
Dark Vektor – Wires [Titan´s Halo Records]
Point B – Headland (Dead Sound remix) [Combat Recordings]
Threnody – Dirt Box [dub]
Valta and Minikin – Bad Man [Rag and Bone]
Blackmass Plastics – Blue Velvet [Furioso] forthcoming
Monoloc – Nohouse (Xhin remix) [Sleaze]
Blackmass Plastics – Lickshit [Furioso] forthcoming
Midfield General – Coatnoise (Dave Clarke remix) [SKINT]
Inigo Kennedy – Albedo [Asymmetric]
Coefficient – Nonlinear Phase Event [Labrynth] forthcoming
Altered Natives vs LFO – LFO [alterednatives.bandcamp.com]
Inigo Kennedy – Skedaddle [Asymmetric]
Justin Berkovi – Backshredding (Forward Strategy Group remix) [Perc Trax]
The Exaltics – Journey to Jupiter [Solar One Music]
Automatic Tasty – Free All Parties Now [dub]

hour 2 / SWARM INTELLIGENCE showcase

Swarm Intelligence – Intro
Swarm Intelligence – Once Bitten [Invisible Agent] forthcoming
Swarm Intelligence – Locus [dub]
Swarm Intelligence – Repart [dub]
Swarm Intelligence – Fighting Talk (VIP Mix) [dub]
Swarm Intelligence – On the Edge [Invisible Agent] forthcoming
Swarm Intelligence – Arretax [dub]
Swarm Intelligence – Gomb [dub]
Swarm Intelligence – Phases [Transporta]
Swarm Intelligence – Hcdoer [dub]
Swarm Intelligence – Skrote [!Kaboogie]
Swarm Intelligence – Squenl [Stasis] forthcoming
Swarm Intelligence – Black Parsed [Stasis] forthcoming
Swarm Intelligence – Adverb [dub]
Lady Grew – DC Napoleon (Swarm Intelligence remix) [Ghetto Quietly] forthcoming
Swarm Intelligence – Fousoc [Section 27] forthcoming
Beastie Boys – Intergalactic (Swarm Intelligence Mash-Up) [dub]
Swarm Intelligence – Pygmi [dub]

MANTIS RADIO 077 + SWARM INTELLIGENCE // DARKFLOOR

Stigmergy : It is a mechanism of indirect coordination between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent. In that way, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity.

Stigmergy is a form of self-organization. It produces complex, seemingly intelligent structures, without need for any planning, control, or even direct communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even individual awareness of each other.

The term “stigmergy” was introduced by French biologist Pierre-Paul Grassé in 1959 to refer to termite behavior. He defined it as: “Stimulation of workers by the performance they have achieved.” It is derived from the Greek words στίγμα stigma “mark, sign” and ἔργον ergon “work, action”, and captures the notion that an agent’s actions leave signs in the environment, signs that it and other agents sense and that determine and incite their subsequent actions.

Swarm Intelligence (SI)

Prof Mark Bishop has been involved in foundational theoretical work in the development of a novel conceptual Swarm Intelligence framework.

Example algorithm: Ant colony optimization

In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food (see Ant communication).

Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher on shorter paths than longer ones. Pheromone evaporation also has the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained.

Thus, when one ant finds a good (i.e., short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads all the ants following a single path. The idea of the ant colony algorithm is to mimic this behavior with “simulated ants” walking around the graph representing the problem to solve.

The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based Routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of variations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each “ant” (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then you pay for the cinema before you know how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).

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