From the author of “Rock, Paper, Scissors – Game Theory in everyday life” dedicated to evolution of cooperation in nature (published last year – Basic Books), a new book on related areas is now fresh on the stands (released Dec. 7,  2009): “The Perfect Swarm – The Science of Complexity in everyday life“. This time Len Fischer takes us into the realm of our interlinked modern lives, where complexity rules. But complexity also has rules. Understand these, and we are better placed to make sense of the mountain of data that confronts us every day.  Fischer ranges far and wide to discover what tips the science of complexity has for us. Studies of human (one good example is Gum voting) and animal behaviour, management science, statistics and network theory all enter the mix.

One of the greatest discoveries of recent times is that the complex patterns we find in life are often produced when all of the individuals in a group follow similar simple rules. Even if the final pattern is complex, rules are not. This process of “Self-Organization” reveals itself in the inanimate worlds of crystals and seashells, but as Len Fisher shows, it is also evident in living organisms, from fish to ants to human beings, being Stigmergy one among many cases of this type of Self-Organized behaviour, encompassing applications in several Engineering fields like Computer science and Artificial Intelligence, Data-Mining, Pattern Recognition, Image Analysis and Perception, Robotics, Optimization, Learning, Forecasting, etc. Since I do work on these precise areas, you may find several of my previous posts dedicated to these issues, such as Self-Organized Data and Image Retrieval systems, Stigmergic Optimization, Computer-based Adaptive Dynamic Perception, Swarm-based Data Mining, Self-regulated Swarms and Memory, Ant based Data Clustering, Generative computer-based photography and painting, Classification, Extreme Dynamic Optimization, Self-Organized Pattern Recognition, among other applications.

For instance, the coordinated movements of fish in schools, arise from the simple rule: “Follow the fish in front.” Traffic flow arises from simple rules: “Keep your distance” and “Keep to the right.” Now, in his new book, Fisher shows how we can manage our complex social lives in an ever more chaotic world. His investigation encompasses topics ranging from “swarm intelligence” (check links above) to the science of parties (a beautiful example by ICOSYSTEM inc.) and the best ways to start a fad. Finally, Fisher sheds light on the beauty and utility of complexity theory. For those willing to understand a miriad of some basic examples (Fischer gaves us 33 nice food-for-thought examples in total) and to have a well writen introduction into this thrilling new branch of science, referred by Stephen Hawking as the science for the current century (“I think complexity is the science for the 21st century”), Perfect Swarm will be indeed an excelent companion.

Journalism is dying, they say. I do agree. And while the argue continues, many interested on the issue are now debating what really is the reason. The question is…, there is no reason at all, there are many. Intricate ones. Do ponder on this: while newspapers are facing the immense omnipresent and real-time competition from TV channels, TV on itself is dying also (while unexpectedly, … Radio is surging). On many broadcasted programs, TV anchors are now more important than the invited people who, on that subject (supposedly) worked hardly over years to provide that precise innovative content. As in large supermarkets and great malls, package by these means have turned more important than the content in itself. This related business editorial pressure for news quickness have become so intensive and aggressive, that contents are replaced every second without judge and once in the air hardly described, discussed,  opposed or dessicated. So at large,  TV CEO’s producers think that people are no longer waiting for a new interesting content to appear, they are instead waiting for the anchor which passes them down as they were peanuts. Peanuts are good, but in excess – we all agree – are damn awful. And many do so,  as an old passive addiction. Which means that in the long run, nothing remains (fact for both sides); … And if they give me no opportunity at all to check content carefully, if I happen to be on the mood to, … So, I move on. Buy this precise simple way, media cannibalizes itself.

We all know that attention spam is getting narrower these days, and, e.g., yes… greater literature classics are no longer read. So, Media CEO’s say – “they have no time“. But, really … do mind that gap. Think twice. If the whole environment suddenly recognizes (being this one of the major questions – see below) that they are getting enough of peanuts (and they really are), they will urge for beef-steaks. In fact, eating 1000 void peanuts takes more time to consume than one large good beef! And there is a difference, … the beef remains on our body for several hours, not seconds.

It’s promptly becoming a paradox, since Media CEO’s on their blindness competition refuge on saying that they - us readers - have no time (when in mediocrity no solution is found, easiest way is to repeat a mantra), and we (mostly of us) keep zapping news as never before. However, they never realized that we keep zapping it, because no news – by these means -  are of interest. They really all have become the same. And once they appear all the same, they all soon disappear from our minds. … We all in some aspects all wonder, what  really happened to  research journalism, stories about new complex issues, strong content, explained in detail but still provided in simple eloquent ways? Come on, this long-tailed huge market niche, once yours, is now void!

Newspapers do have this wonderful singularity. They still have journalists (at least some, if they had enough vision to nourish them). They could provide insightful detailed backup stories, open questions, or debating new ones as no one can in public space. Moreover, they have time from their consumers. That, at least, is what I am feed-backing to Guardian every Sunday when I put my money over the news bench in change for this newspaper, along others like The Economist. But in face of these overall great news-without-sense turmoil cascade, probably one of these days, people will instead desire silence… or listening to their grandfathers knowledge, good-sense, and long-lived emotion (which keeps increasing believe me). They will relate to him, as never before.  Not newspapers. At least, he do provides content.

But once the media is set (and in some way, not all the way, medium is the message, as postulated by Marshall McLuhan), the great gold-run will be on, … guess what, … content. And on relationships among content! Journalism will be no longer under atomization. Or crystallized.

Fig. – Spatial distribution of 931 items (words taken from an article at ABC Spanish newspaper) on a 61 x 61 non-parametric toroidal grid, at t=106. 91 ants used type 2 probability response functions, with k1=0.1 and k2=0.3. Some independent clusters examples are: (A) anunció, bilbao, embargo, titulos, entre, hacer, necesídad, tras, vida, lider, cualquier, derechos, medida.(B) dirigentes, prensa, ciu. (C) discos, amigos, grandes. (D) hechos, piloto, miedo, tipo, cd, informes. (E) dificil, gobierno, justicia, crisis, voluntad, creó, elección, horas, frente, técnica, unas, tarde, familia, sargento, necesídad, red, obra … (among other word semantic clusters; check paper article below).

For long, media decided to do nothing, while new media including social media was coming in to the plateu, stronger as never before. Let me give you one example. In order to understand how relations between item news could enhnace newspaper reading and social awareness, back in 2002 I decided to make an experiment. Together with a colleague, we took one article of the Spanish ABC magazine (photo above). The article was about spanish political parties and corruption. It contained 931words (snapshot above). In order to extract semantic meaning from it as a pre-processing computer analysis, we started by applying Latent Semantic Analysis (LSA). Then, Swarm Intelligent algorithms were developed in order to have a glimpse on the relations among all those words on the newspaper article. Guess what? Some words like “big”, friends” and ”music discs” were segmented from the rest of the political related article (segregated it on a remote semantic “island”), that is, not only a whole conceptual semantic atlas of that entire news section was possible, as well as finding unrelated issues (which were uncorrelated semantic “islands”). Now, just imagine if this happens within a newspaper social network, live, 24 hours a day, while people grab for strong co-related content and discuss it as it happens. One strong journal article, could in facto, evolve to social collective knowledge and awareness as never before. That, in reality is something that classic journalism could use as and edge for their (nowadays awful) market approach. Providing not only good content, but along with it, an extra service not available anyware (which is in some way, priceless): The chance to provide co-related real-time meta-content. Not one view, but many aggregated views.  Edited real-world real-time good quality journalism which has the potential of an “endless” price, namely these days. On the other hand, what we now see is that news CEO’s along with some editors still keep their minds on 19th century journalism.  For worse, due to their legitimic panic. However, meanwhile, the world has indeed evolved.

[] Vitorino Ramos, Juan J. Merelo, Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning, in AEB´2002 – 1st Spanish Conference on Evolutionary and Bio-Inspired Algorithms, E. Alba, F. Herrera, J.J. Merelo et al. (Eds.), pp. 284-293, Centro Univ. de Mérida, Mérida, Spain, 6-8 Feb. 2002.

Social insect societies and more specifically ant colonies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases exceed the individual capabilities of a single ant. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy to tackle unsupervised clustering as well as data retrieval problems. The present ant clustering system (ACLUSTER) avoids not only short-term memory based strategies, as well as the use of several artificial ant types (using different speeds), present in some recent approaches. Moreover and according to our knowledge, this is also the first application of ant systems into textual document clustering.

(to obtain the respective PDF file follow link above or visit chemoton.org)

With the ubiquitous use of web-based and wireless Social Networks, people are increasingly using the term “Collective Intelligence“. However, I do have serious doubts they really understand what they meant. Some call it the wisdom of crowds or collective wisdom, others smart mobs, while others wealth of knowledge, world brain and so on. Moreover, turning things worse, there are those also, which tend to see it, or confound it with crowd-sourcing as well as prediction markets. Even if there are some loosely conceptual bridges between all them, it will be probably useful to know that the term was instead been born over the Artificial Intelligence research area, while exploiting stigmergic phenomena (see also Swarm Intelligence) among ensembles of cooperative agents. So what follows is a recent definition provided by Univ. of Alberta, Canada. This entry was added last month (Nov. 2009) at the Dictionary of Cognitive Science (Michael R.W. Dawson, David A. Medler Eds.):

Collective intelligence – is a term that refers to the computational abilities of a group of agents. With collective intelligence, a group is capable of accomplishing a task, or of solving an information processing problem, that is beyond the capabilities of an individual agent.

Collective intelligence depends on more than mere numbers of agents.  For a collective to be considered intelligent, the whole must be greater than the sum of its parts.  This idea has been used to identify the presence of collective intelligence by relating the amount of work done by a collective to the number of agents in the collection (Beni & Wang, 1991). If there is a linear increase in amount of work done as a function of the number of agents, then collective intelligence is not evident. However, if there is a nonlinear increase (e.g., an exponential increase) in the amount of work done as a function of the number of agents, then Beni and Wang argue that this is evidence that the collective is intelligent.

Collective intelligence is of interest in cognitive science because many colonies of social insects appear to exhibit this kind of intelligence, and this has inspired researchers to explore “porting” such processing to robot collectives. As far as robots are concerned, collective intelligence is exciting because it offers the possiblity of developing systems that are scalable (they don’t get disrupted when more agents are added) and flexible (they don’t get disrupted when some agents are damaged or fail) (Sharkey, 2006).

References:

1. Beni, G., & Wang, J. (1991, April 9-11). Theoretical problems for the realization of distributed robotic systems. Paper presented at the IEEE International Conference on Robotics and Automation, Sacramento, CA.
2. Sharkey, A. J. C. (2006). Robots, insects and swarm intelligence. Artificial Intelligence Review, 26(4), 255-268.

Swarm intelligence (SI) is a type of artificial intelligence based on the collective behavior of decentralized, self-organized systems.

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Read full Wikipedia Article.

Washington: In the never-ending battle to protect computer networks from intruders, security experts are deploying a new defence, modelled on one of nature’s hardiest creatures – the ant.

Unlike traditional security devices, which are static, these “digital ants” wander through computer networks looking for threats, such as “computer worms” – self-replicating programmes designed to steal information or facilitate unauthorised use of machines.

When a digital ant detects a threat, it doesn’t take long for an army of ants to converge at that location, drawing the attention of human operators who step in to investigate.

The concept, called “swarm intelligence”, promises to transform cyber security because it adapts readily to changing threats.

“In nature, we know that ants defend against threats very successfully,” explains Errin Fulp, computer science professor and expert in security and computer networks, at the Wake Forest University (WFU).

“They can ramp up their defence rapidly, and then resume routine behaviour quickly after an intruder has been stopped. We were trying to achieve that same framework in a computer system,” he says.

Current security devices are designed to defend against all known threats at all times, but the bad guys who write malware – software created for malicious purposes – keep introducing slight variations to evade computer defences.

As new variations are discovered and updates issued, security programmes gobble more resources, antivirus scans take longer and machines run slower – a familiar problem for most computer users.

Glenn Fink, research scientist at Pacific Northwest National Laboratory (PNNL) in Richland, Washington, came up with the idea of copying ant behaviour. PNNL, one of 10 Department of Energy (DoE) labs, conducts cutting-edge research in cyber security.

Fink was familiar with Fulp’s expertise developing faster scans using parallel processing – dividing computer data into batches like lines of shoppers going through grocery store checkouts, where each lane is focussed on certain threats.

He invited Fulp and Wake Forest graduate students Wes Featherstun and Brian Williams to join a project there this summer that tested digital ants on a network of 64 computers.

Swarm intelligence, the approach developed by PNNL and Wake Forest, divides up the process of searching for specific threats, says a WFU release.

“Our idea is to deploy 3,000 different types of digital ants, each looking for evidence of a threat,” Fulp says.

Fulp introduced a worm into the network, and the digital ants successfully found it. PNNL has extended the project this semester, and Featherstun and Williams plan to incorporate the research into their master’s theses.

Figure – My first Swarm Painting SP0016 (Jan. 2002). This was done attaching the following algorithm into a robotic drawing arm. In order to do it however, pheromone distribution by the overall ant colony were carefully coded into different kinds of colors and several robotic pencils (check “The MC2 Project [Machines of Collective Conscience]“, 2001, and “On the Implicit and on the Artificial“, 2002). On the same year when the computational model appeared (2000) the concept was already extended into photography (check original paper) – using the pheromone distribution as photograms (“Einstein to Map” in the original article along with works like “Kafka to Red Ants” as well as subsequent newspaper articles). Meanwhile, in 2003, I was invited to give an invited talk over these at the 1st Art & Science Symposium in Bilbao (below). Even if I was already aware of Jeffrey Ventrella outstanding work as well as Ezequiel Di Paolo, it was there where we first met physically.

[] Vitorino Ramos, Self-Organizing the Abstract: Canvas as a Swarm Habitat for Collective Memory, Perception and Cooperative Distributed Creativity, in 1st Art & Science Symposium – Models to Know Reality, J. Rekalde, R. Ibáñez and Á. Simó (Eds.), pp. 59, Facultad de Bellas Artes EHU/UPV, Universidad del País Vasco, 11-12 Dec., Bilbao, Spain, 2003.

Many animals can produce very complex intricate architectures that fulfil numerous functional and adaptive requirements (protection from predators, thermal regulation, substrate of social life and reproductive activities, etc). Among them, social insects are capable of generating amazingly complex functional patterns in space and time, although they have limited individual abilities and their behaviour exhibits some degree of randomness. Among all activities by social insects, nest building, cemetery organization and collective sorting, is undoubtedly the most spectacular, as it demonstrates the greatest difference between individual and collective levels. Trying to answer how insects in a colony coordinate their behaviour in order to build these highly complex architectures, scientists assumed a first hypothesis, anthropomorphism, i.e., individual insects were assumed to possess a representation of the global structure to be produced and to make decisions on the basis of that representation. Nest complexity would then result from the complexity of the insect’s behaviour. Insect societies, however, are organized in a way that departs radically from the anthropomorphic model in which there is a direct causal relationship between nest complexity and behavioural complexity. Recent works suggests that a social insect colony is a decentralized system composed of cooperative, autonomous units that are distributed in the environment, exhibit simple probabilistic stimulus-response behaviour, and have only access to local information. According to these studies at least two low-level mechanisms play a role in the building activities of social insects: Self-organization and discrete Stigmergy, being the latter a kind of indirect and environmental synergy. Based on past and present stigmergic models, and on the underlying scientific research on Artificial Ant Systems and Swarm Intelligence, while being systems capable of emerging a form of collective intelligence, perception and Artificial Life, done by Vitorino Ramos, and on further experiences in collaboration with the plastic artist Leonel Moura, we will show results facing the possibility of considering as “art”, as well, the resulting visual expression of these systems. Past experiences under the designation of “Swarm Paintings” conducted in 2001, not only confirmed the possibility of realizing an artificial art (thus non-human), as introduced into the process the questioning of creative migration, specifically from the computer monitors to the canvas via a robotic harm. In more recent self-organized based research we seek to develop and profound the initial ideas by using a swarm of autonomous robots (ARTsBOT project 2002-03), that “live” avoiding the purpose of being merely a simple perpetrator of order streams coming from an external computer, but instead, that actually co-evolve within the canvas space, acting (that is, laying ink) according to simple inner threshold stimulus response functions, reacting simultaneously to the chromatic stimulus present in the canvas environment done by the passage of their team-mates, as well as by the distributed feedback, affecting their future collective behaviour. In parallel, and in what respects to certain types of collective systems, we seek to confirm, in a physically embedded way, that the emergence of order (even as a concept) seems to be found at a lower level of complexity, based on simple and basic interchange of information, and on the local dynamic of parts, who, by self-organizing mechanisms tend to form an lived whole, innovative and adapting, allowing for emergent open-ended creative and distributed production.

by C. M. Fernandes, C. F. Lima, J.L.J. Laredo, A.C. Rosa, and J.J. Merelo

Abstract. This paper investigates an update strategy for the Univariate Marginal Distribution Algorithm (UMDA) probabilistic model inspired by the equations of the Ant Colony Optimization (ACO) computational paradigm. By adapting ACO’s transition probability equations to the univariate probabilistic model, it is possible to control the balance between exploration and exploitation  by tuning a single parameter. It is expected that a proper balance can improve the scalability of the algorithm on hard problems with bounded difficulties and experiments conducted on such problems with increasing difficulty and size confirmed these assumptions. These are important results because the performance is improved without increasing the complexity of the model, which is known to have a considerable computational effort.

To appear soon at the European Congress on Artificial Life (ECAL 2009)

Carlos M. Fernandes

a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)

b) Swarm adaptive response over time, under severe dynamics, over the dynamic environment on the left (a).

Figs. – Check animated pictures in here. (a) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (b) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (left), describing a Dynamic Optimization (DO) Control Problem. Over each foraging step, the swarm self-regulates his population and keeps tracking the extrema (44 frames in total).

 [] Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, in Artificial Life X – Proc. of the Tenth Int. Conf. on the Simulation and Synthesis of Living Systems, L.M. Rocha, L.S. Yaeger, M.A. Bedau, D. Floreano, R.L. Goldstone and A. Vespignani (Eds.), MIT Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June 3-7, 2006.

PDF paper.

Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. Termite colonies – for instance – build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS’s were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different sub-populations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work. 

Abraham, Ajith; Grosan, Crina; Ramos, Vitorino (Eds.), Stigmergic Optimization, Studies in Computational Intelligence (series), Vol. 31, Springer-Verlag, ISBN: 3-540-34689-9, 295 p., Hardcover, 2006.

TABLE OF CONTENTS (short /full) / CHAPTERS:

[1] Stigmergic Optimization: Foundations, Perspectives and Applications.
[2] Stigmergic Autonomous Navigation in Collective Robotics.
[3] A general Approach to Swarm Coordination using Circle Formation.
[4] Cooperative Particle Swarm Optimizers: a powerful and promising approach.
[5] Parallel Particle Swarm Optimization Algorithms with Adaptive
 Simulated Annealing.
[6] Termite: a Swarm Intelligent Routing algorithm for Mobile
 Wireless ad-hoc Networks.
[7] Linear Multiobjective Particle Swarm Optimization.
[8] Physically realistic Self-Assembly Simulation system.
[9] Gliders and Riders: A Particle Swarm selects for coherent Space-time Structures in Evolving Cellular Automata.
[10] Stigmergic Navigation for Multi-agent Teams in Complex Environments.
[11] Swarm Intelligence: Theoretical proof that Empirical techniques are Optimal.
[12] Stochastic Diffusion search: Partial function evaluation in Swarm Intelligence Dynamic Optimization.

For some seconds, just imagine having these 50 m² – 8 meters tall artifact constructed (above) by tiny Giant Architects in a plaza over a big city near you. Over this youtube video several scientists have filled the big city unearthed with 10 tens of cement during 3 days. Then calmly (taking several weeks), have digg it to the bone. To have a clue on what I mean just imagine having all these at Times Square  plaza in New York! or at the front-door of the  Frank Gehry’s Guggenheim Museum in Bilbao (in fact a giant spider is also there – check photo below). Colonies of eu-social insects use stigmergy in order to do this, being a good reference the work done by Karsai back in 1999 at the Artificial Life MIT Press Journal (here is the abstract – unfornately I have it on paper but not scanned):

# István Karsai, “Decentralized Control of Construction Behavior in Paper Wasps: An Overview of the Stigmergy Approach“, Spring 1999, Vol. 5, No. 2, Pages 117-136.

Grassé [26] coined the term stigmergy (previous work directs and triggers new building actions) to describe a mechanism of decentralized pathway of information flow in social insects. In general, all kinds of multi-agent groups require coordination for their effort and it seems that stigmergy is a very powerful means to coordinate activity over great spans of time and space in a wide variety of systems. In a situation in which many individuals contribute to a collective effort, such as building a nest, stimuli provided by the emerging structure itself can provide a rich source of information for the working insects. The current article provides a detailed review of this stigmergic paradigm in the building behavior of paper wasps to show how stigmergy influenced the understanding of mechanisms and evolution of a particular biological system. The most important feature to understand is how local stimuli are organized in space and time to ensure the emergence of a coherent adaptive structure and to explain how workers could act independently yet respond to stimuli provided through the common medium of the environment of the colony.

Another interesting paper (available online) is the more recent work by Mason at the 8th Artificial Life conference, in 2002. Below I have selected part of the introductory text:

# Zachary Mason ,”Programming with Stigmergy: Using Swarms for Construction“, in Artificial Life VIII Conf., Standish, Abbass, Bedau (eds)(MIT Press), New South Wales, Australia, pp. 371-375, 2002.

(…) Termite nests are large and complex. A nest may be as much as 104 or 105 times as large as an individual termite (Boneabeau et al. 1997) a ratio unparalleled in the animal kingdom. The nests of the African termite sub-family Macrotermitinae are composed of many substructures, such as protective bulwarks, pillared brood chambers, spiral cooling vents, galleries of fungus gardens and royal chambers. For all the architectural sophistication of termite nests, termites themselves are blind, weak and apparently not responsive to a coordinating authority. This work attempts to borrow and generalize the termite construction-algorithm, permitting artificial, decentralized swarms to be programmed to build complex, composable structures.
How do small, blind termites manage to build (relatively) huge, intricate nests? Work on this question includes a simple, decentralized building model (Grasse 1959) (Grasse 1984), an empirical study of termite building behavior (Bruinsma 1979), a mathematical model of the synthesis of pillars in termite nests (Deneubourg 1977), and a model explaining how modest environmental variation can cause the same termite behaviors to generate qualitatively different structures (Boneabeau et al. 1997). Most relevant to this work is (Bruinsma 1979), which records three feedback mechanisms governing termite behavior. In the first, a termite picks up a soil pellet, masticates it into a paste and injects a termiteattracting pheremone into it. When the pellet is deposited, the pheremone stimulates nearby termites to pellet-gathering behavior and makes them more likely to deposit their pellets nearby. Second, small obstacles in the terrain stimulate pellet deposits and can seed pillars. Finally, a trail pheremone allows more workers to be drawn to a construction site. Termites and many social insects interact stigmergically - that is, communication is mediated through changes in the environment rather than direct signal transmission. Computer simulations have used stigmergy to reproduce termite’s pillar-making behavior and ant’s foraging and the spontaneous cemetery building. These applications rely of qualitative stigmergy | individual agents react to a continuous variations in the environment. An example of quantitative stigmergy is (G. Theraulaz 1995), a simulation of wasp nest building. Wasps build nests by depositing cells on a lattice. Whether an empty cell is lled depends on the adjacent cells. Because all wasps have the same deposit-triggers, multiple wasps are able to simultaneously work on a single nest without without ruining each others work. A set of deposit-triggers is coherent if each no stage in the building process can be confused with an earlier stage by making only local observations, thus obviating the need for centralized control.
The goal of this work is to generalize the construction methodologies of the social insects and create a language for stigmergically assembling complex structures. Such a language permit swarms of agents to erect interesting architectures without benefit of a central controller or explicit inter-agent communication. The primary advantage of this approach is that stigmergically controlled swarms have minimal communication and no coordination overhead. Also, very little processing is demanded of agents, and the swarm can tolerate a degree of agent error. On a more abstract plane, this work is an example of designing emergent behavior. (…)

Back in 2003 I was photographed by João Bracourt, a friend and professional photograph which among other things (web design + painting) travels around the world within big professional surf events (he is right now on it’s way to Indonesia), covering it for main surf magazines. Back then (Sept. 2003) we were enjoying ourselves with a big group late nigth at Bairro Alto, the main bar and restaurant district in Lisbon.

The t-shirt I’m wearing here is from COSI – Complexity in Social Sciences Summer School. One month earlier have been invited among other people to give a lecture in Spain about my work, there at COSI (Baeza, Andaluzia). After all these years the PPT file (Stigmergy as a possible exploratory walk up to collective life-like complexity and behaviour) is still available. As well as those from Gerard Weisbuch (Research Director of the Complex Networks and Cognitive Systems Team within the Statistical Physics Laboratory of the l’Ecole Normale Supérieure in Paris, France) and Rosaria Conte (head of the Division of Artificial Intelligence, Cognitive Modelling & Interaction at the Institute of Psychology of the Italian National Research Council), among others. Many other research materials concerning complexity and social sciences are still available at COSI’s 2003 main site.

(at Bairro Alto, Lisbon, Sept. 2003 - taken by João Bracourt)

(at Bairro Alto, Lisbon, Sept. 2003 - taken by João Bracourt)