Swarms of tiny, cute robots will one day bring you your phone, like this
These tiny “Zooids” work together at high speed to act as tools, display information or move things around like little ants. Their creators, a multi-university group of roboticists, propose them as the beginning of a new type of “swarm user interface.”
By Devin Coldewey

Each robot has little wheels, a touch sensor and gyros, and an optical sensor on top that monitors its position by watching patterns coming from a projector overhead. They don’t operate independently, rather taking instructions on where to go from the computer tracking them.

They can manipulate real-life objects themselves, as well: in the video, they push a phone closer to a user, and one can imagine them tidying a desk when nobody is around, or coming out of a hole in the wall to collect crumbs and put your slippers away.

the internet turns 25 today.
now can we just stop for a minute and marvel at this amazing tool in the palm of our hands? 3,5 billion people, almost half of this earth`s population, have access to this net that connects us, that stores and makes available a major part of humanity´s collected wisdom, brilliant thoughts and inspirations of history´s sages, great thinkers, scientists, philosophers, artists, experts in every field. 

the internet brings forth swarm intelligence, it enables everyone to stand on the shoulders of giants. there is scientific research and empirical data available for free if you want to dig deep into any topic of interest, but there are also short and sweet videoclips that explain even the most complicated topics in a simple and entertaining way, so you can approach whatever you’re interested in  - even as an absolute beginner. you can try out and learn about all activities that ever tickled your fancy - from experts! but you can also just kick back and enjoy music, radio plays, movies, art, made by people who devote their life to this.

but the internet does not only enable us to receive data, information and entertainment, we can also use it as a mouthpiece and send whatever we want into the world. our thoughts, our art, our truth. we can connect with likeminded people, collaborate, support causes we believe in.

we can use this democratization of media to enhance communication, empathy and fairness, to stay in touch with family and friends all around the world and to make new aquaintances that probably never would’ve crossed our way without this technology. 

but the internet and the mobile technologies can also divide us if we use them in the wrong way. if we let them trivialize and dismember our communication into sms-lenghs pieces, if we prefer to dive into virtual realities instead of connecting with the people we’re with. if we use it to show off and not to connect, inform, entertain or inspire. if we engage in a rat race of superficial beauty and hyper consumption. if we spread hate. if we misuse data transparency to supress and not to enhance fairness and justice.

the internet is in the palm of your hands. what will you use it for?

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

My scientific work has primarily focused on understanding the phenomenon of swarm intelligence (SI): the solving of cognitive problems by a group of individuals who pool their knowledge and process it through social interactions. It has long been recognized that a group of animals, relative to a solitary individual, can do such things as capture large prey more easily and counter predators more effectively. More recently it has been realized that a group of animals, with the right organization, can also solve cognitive problems with an ability that far exceeds the cognitive ability of any single animal. Thus SI is a means whereby a group can overcome some of the cognitive limitations of its members. SI is a rapidly developing topic that has been investigated mainly in social insects (ants, termites, social wasps, and social bees) but has relevance to other animals, including humans. Wherever there is collective decision-making—for example, in democratic elections, committee meetings, and prediction markets—there is a potential for SI.
—  Thomas D. Seeley
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

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.)



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.


final day

Self-propelled particles (SPP), also referred to as self-driven particles, is a concept used by physicists to describe autonomous agents, which convert energy from the environment into directed or persistent motion. Everyday life examples of such agents are walking, swimming or flying animals. Other biological systems include bacteria, cells, algae and other micro-organisms. Generally, directed propulsion in biological systems is referred to as chemotaxis. One can also think of artificial systems such as robots or specifically designed particles such as swimming Janus colloids, nanomotors, walking grains, and others.
—  Wikipedia
Short review of Clerc and Kennedy 2002

I just finished reading Maurice Clerc and James Kennedy’s 2002 paper “The Particle Swarm: Explosion, Stability, and Convergence in a Multidimensional Complex Space”.  And what a great paper it was!  They quickly decompose the dynamic system to the interactions of velocity and distance from the “best” or optimum-to-date, and show that when these relationships are represented in 2 dimensional real space, there’s a discontinuity at a certain value of the acceleration constant which arises from the eigenvalues of the matrix representation of the system.  

Clerc and Kennedy are able not only to show exactly why this discontinuity arises, but also how to build a constraint coefficient that can effectively guarantee convergence on a local optimum, without specifying any problem-specific variables.

I was very impressed by the paper.  It was well laid out, simply explained, and was not excessively verbose.  More to the point, I now understand just what is going on in chapter 8 of Kennedy and Eberhart’s “Swarm Intelligence” book, and I’m happy about that.  It was not strictly necessary to read the 2002 paper to get the results described in the book, but the paper did certainly illuminate the factors leading to explosion of the system.

Tonight after work, I’m going to actually implement Clerc’s Type 1 constraint coefficient, and will probably rewrite the current implementation from scratch while I’m at it.  Aside from the challenges of putting together a proper visualization solution for the particle swarm (I’ll probably use Prefuse, having worked with it in the past), I also need to implement the standard set of unconstrained real-valued benchmark functions.  Namely, De Jong’s functions, Schaffer’s, Griewank, Rosenbrock, and Rastragin.  These functions are typically used for testing evolutionary optimization algorithms.

Once I have these benchmarks, and finish implementing the generalized particle swarm model, I can get on with my real area of interest, which is investigating the social influence aspect of the algorithm.