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Figure – Two simplicies a and b connected by the 2-dimensional face, the triangle {1;2;3}. In the analysis of the time-line of The Guardian newspaper (link) the system used feature vectors based on frequency of words and them computed similarity between documents based on those feature vectors. This is a purely statistical approach that requires great computational power and that is difficult for problems that have large feature vectors and many documents. Feature vectors with 100,000 or more items are common and computing similarities between these documents becomes cumbersome. Instead of computing distance (or similarity) matrices between documents from feature vectors, the present approach explores the possibility of inferring the distance between documents from the Q-analysis description. Q-analysis is a very natural notion of connectivity between the simplicies of the structure and in the relation studied, documents are connected to each other through shared sets of tags entered by the journalists. Also in this framework, eccentricity is defined as a measure of the relatedness of one simplex in relation to another [7].
David M.S. Rodrigues and Vitorino Ramos, “Traversing News with Ant Colony Optimisation and Negative Pheromones” [PDF], accepted as preprint for oral presentation at the European Conference on Complex Systems – ECCS‘14 in Lucca, Sept. 22-26, 2014, Italy.
Abstract: The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system. Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora [1]. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data. A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed [2, 3] that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants [4].
In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q-analysis description [5, 6, 7] of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents. The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal improves the quality of the results. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence. This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system.
Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Hamiltonian cycles, Text Mining, Textual Corpora, Information Retrieval, Knowledge Discovery, Sentiment Analysis, Q-Analysis, Data Mining, Journalism, The Guardian.
References:
[1] Alexander Strehl, Joydeep Ghosh, and Raymond Mooney. Impact of similarity measures on web-page clustering. In Workshop on Artifcial Intelligence for Web Search (AAAI 2000), pages 58-64, 2000.
[2] David M. S. Rodrigues, Jorge Louçã, and Vitorino Ramos. From standard to second-order Swarm Intelligence phase-space maps. In Stefan Thurner, editor, 8th European Conference on Complex Systems, Vienna, Austria, 9 2011.
[3] Vitorino Ramos, David M. S. Rodrigues, and Jorge Louçã. Second order Swarm Intelligence. In Jeng-Shyang Pan, Marios M. Polycarpou, Micha l Wozniak, André C.P.L.F. Carvalho, Hector Quintian, and Emilio Corchado, editors, HAIS’13. 8th International Conference on Hybrid Artificial Intelligence Systems, volume 8073 of Lecture Notes in Computer Science, pages 411-420. Springer Berlin Heidelberg, Salamanca, Spain, 9 2013.
[4] Elva J.H. Robinson, Duncan Jackson, Mike Holcombe, and Francis L.W. Ratnieks. No entry signal in ant foraging (hymenoptera: Formicidae): new insights from an agent-based model. Myrmecological News, 10(120), 2007.
[5] Ronald Harry Atkin. Mathematical Structure in Human A ffairs. Heinemann Educational Publishers, 48 Charles Street, London, 1 edition, 1974.
[6] J. H. Johnson. A survey of Q-analysis, part 1: The past and present. In Proceedings of the Seminar on Q-analysis and the Social Sciences, Universty of Leeds, 9 1983.
[7] David M. S. Rodrigues. Identifying news clusters using Q-analysis and modularity. In Albert Diaz-Guilera, Alex Arenas, and Alvaro Corral, editors, Proceedings of the European Conference on Complex Systems 2013, Barcelona, 9 2013.
In order to solve hard combinatorial optimization problems (e.g. optimally scheduling students and teachers along a week plan on several different classes and classrooms), one way is to computationally mimic how ants forage the vicinity of their habitats searching for food. On a myriad of endless possibilities to find the optimal route (minimizing the travel distance), ants, collectively emerge the solution by using stigmergic signal traces, or pheromones, which also dynamically change under evaporation.
Current algorithms, however, make only use of a positive feedback type of pheromone along their search, that is, if they collectively visit a good low-distance route (a minimal pseudo-solution to the problem) they tend to reinforce that signal, for their colleagues. Nothing wrong with that, on the contrary, but no one knows however if a lower-distance alternative route is there also, just at the corner. On his global search endeavour, like a snowballing effect, positive feedbacks tend up to give credit to the exploitation of solutions but not on the – also useful – exploration side. The upcoming potential solutions can thus get crystallized, and freeze, while a small change on some parts of the whole route, could on the other-hand successfully increase the global result.
Figure – Influence of negative pheromone on kroA100.tsp problem (fig.1 – page 6) (values on lines represent 1-ALPHA). A typical standard ACS (Ant Colony System) is represented here by the line with value 0.0, while better results could be found by our approach, when using positive feedbacks (0.95) along with negative feedbacks (0.05). Not only we obtain better results, as we found them earlier.
There is, however, an advantage when a second type of pheromone (a negative feedback one) co-evolves with the first type. And we decided to research for his impact. What we found out, is that by using a second type of global feedback, we can indeed increase a faster search while achieving better results. In a way, it’s like using two different types of evaporative traffic lights, in green and red, co-evolving together. And as a conclusion, we should indeed use a negative no-entry signal pheromone. In small amounts (0.05), but use it. Not only this prevents the whole system to freeze on some solutions, to soon, as it enhances a better compromise on the search space of potential routes. The pre-print article is available here at arXiv. Follows the abstract and keywords:
Vitorino Ramos, David M. S. Rodrigues, Jorge Louçã, “Second Order Swarm Intelligence” [PDF], in Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, Springer-Verlag, Volume 8073, pp. 411-420, 2013.
Abstract: An artificial Ant Colony System (ACS) algorithm to solve general purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem‘s (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP complete combinatorial Optimization Problems, running on symmetrical TSPs. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Grüter [28] where “No entry” signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.
Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Combinatorial Optimization problems, Symmetrical Travelling Salesman Problems (TSP).
Figure – Hybrid Artificial Intelligent Systems new LNAI (Lecture Notes on Artificial Intelligence) series volume 8073, Springer-Verlag Book [original photo by my colleague David M.S. Rodrigues].
New work, new book. Last week one of our latest works come out published on Springer. Edited by Jeng-Shyang Pan, Marios M. Polycarpou, Emilio Corchado et al. “Hybrid Artificial Intelligent Systems” comprises a full set of new papers on this hybrid area on Intelligent Computing (check the full articles list at Springer). Our new paper “Second Order Swarm Intelligence” (pp. 411-420, Springer books link) was published on the Bio-inspired Models and Evolutionary Computation section.
Nocturnal moth trails – Fluttering wings leave lacy trails as moths beat their way to a floodlight on a rural Ontario lawn. The midsummer night’s exposure, held for 20 seconds, captured some of the hundreds of insects engaged in a nocturnal swarm. [Photo: Steve Irvine, National Geographic, 2013, link]
Ever tried to solve a problem where its own problem statement is changing constantly? Have a look on our approach:
Vitorino Ramos, David M.S. Rodrigues, Jorge Louçã, “Spatio-Temporal Dynamics on Co-Evolved Stigmergy“, in European Conference on Complex Systems, ECCS’11, Vienna, Austria, Sept. 12-16 2011.
Abstract: Research over hard NP-complete Combinatorial Optimization Problems (COP’s) has been focused in recent years, on several robust bio-inspired meta-heuristics, like those involving Evolutionary Computation (EC) algorithmic paradigms. One particularly successful well-know meta-heuristic approach is based on Swarm Intelligence (SI), i.e., the self-organized stigmergic-based property of a complex system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. This line of research recognized as Ant Colony Optimization (ACO), uses a set of stochastic cooperating ant-like agents to find good solutions, using self-organized stigmergy as an indirect form of communication mediated by artificial pheromone, whereas agents deposit pheromone-signs on the edges of the problem-related graph complex network, encompassing a family of successful algorithmic variations such as: Ant Systems (AS), Ant Colony Systems (ACS), Max-Min Ant Systems (Max–Min AS) and Ant-Q.
Albeit being extremely successful these algorithms mostly rely on positive feedback’s, causing excessive algorithmic exploitation over the entire combinatorial search space. This is particularly evident over well known benchmarks as the symmetrical Traveling Salesman Problem (TSP). Being these systems comprised of a large number of frequently similar components or events, the principal challenge is to understand how the components interact to produce a complex pattern feasible solution (in our case study, an optimal robust solution for hard NP-complete dynamic TSP-like combinatorial problems). A suitable approach is to first understand the role of two basic modes of interaction among the components of Self-Organizing (SO) Swarm-Intelligent-like systems: positive and negative feedback. While positive feedback promotes a snowballing auto-catalytic effect (e.g. trail pheromone upgrading over the network; exploitation of the search space), taking an initial change in a system and reinforcing that change in the same direction as the initial deviation (self-enhancement and amplification) allowing the entire colony to exploit some past and present solutions (environmental dynamic memory), negative feedback such as pheromone evaporation ensure that the overall learning system does not stables or freezes itself on a particular configuration (innovation; search space exploration). Although this kind of (global) delayed negative feedback is important (evaporation), for the many reasons given above, there is however strong assumptions that other negative feedbacks are present in nature, which could also play a role over increased convergence, namely implicit-like negative feedbacks. As in the case for positive feedbacks, there is no reason not to explore increasingly distributed and adaptive algorithmic variations where negative feedback is also imposed implicitly (not only explicitly) over each network edge, while the entire colony seeks for better answers in due time.
In order to overcome this hard search space exploitation-exploration compromise, our present algorithmic approach follows the route of very recent biological findings showing that forager ants lay attractive trail pheromones to guide nest mates to food, but where, the effectiveness of foraging networks were improved if pheromones could also be used to repel foragers from unrewarding routes. Increasing empirical evidences for such a negative trail pheromone exists, deployed by Pharaoh’s ants (Monomorium pharaonis) as a ‘no entry‘ signal to mark unrewarding foraging paths. The new algorithm comprises a second order approach to Swarm Intelligence, as pheromone-based no entry-signals cues, were introduced, co-evolving with the standard pheromone distributions (collective cognitive maps) in the aforementioned known algorithms.
To exhaustively test his adaptive response and robustness, we have recurred to different dynamic optimization problems. Medium-size and large-sized dynamic TSP problems were created. Settings and parameters such as, environmental upgrade frequencies, landscape changing or network topological speed severity, and type of dynamic were tested. Results prove that the present co-evolved two-type pheromone swarm intelligence algorithm is able to quickly track increasing swift changes on the dynamic TSP complex network, compared to standard algorithms.
Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Combinatorial Optimization problems, Dynamical Symmetrical Traveling Salesman Problems (TSP).
Fig. – Recovery times over several dynamical stress tests at the fl1577 TSP problem (1577 node graph) – 460 iter max – Swift changes at every 150 iterations (20% = 314 nodes, 40% = 630 nodes, 60% = 946 nodes, 80% = 1260 nodes, 100% = 1576 nodes). [click to enlarge]
David M.S. Rodrigues, Jorge Louçã, Vitorino Ramos, “From Standard to Second Order Swarm Intelligence Phase-space maps“, in European Conference on Complex Systems, ECCS’11, Vienna, Austria, Sept. 12-16 2011.
Abstract: Standard Stigmergic approaches to Swarm Intelligence encompasses the use of a set of stochastic cooperating ant-like agents to find optimal solutions, using self-organized Stigmergy as an indirect form of communication mediated by a singular artificial pheromone. Agents deposit pheromone-signs on the edges of the problem-related graph to give rise to a family of successful algorithmic approaches entitled Ant Systems (AS), Ant Colony Systems (ACS), among others. These mainly rely on positive feedback’s, to search for an optimal solution in a large combinatorial space. The present work shows how, using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedback’s achieves better results than single positive feedback systems. This follows the route of very recent biological findings showing that forager ants, while laying attractive trail pheromones to guide nest mates to food, also gained foraging effectiveness by the use of pheromones that repelled foragers from unrewarding routes. The algorithm presented here takes inspiration precisely from this biological observation.
The new algorithm was exhaustively tested on a series of well-known benchmarks over hard NP-complete Combinatorial Optimization Problems (COP’s), running on symmetrical Traveling Salesman Problems (TSP). Different network topologies and stress tests were conducted over low-size TSP’s (eil51.tsp; eil78.tsp; kroA100.tsp), medium-size (d198.tsp; lin318.tsp; pcb442.tsp; att532.tsp; rat783.tsp) as well as large sized ones (fl1577.tsp; d2103.tsp) [numbers here referring to the number of nodes in the network]. We show that the new co-evolved stigmergic algorithm compared favorably against the benchmark. The algorithm was able to equal or majorly improve every instance of those standard algorithms, not only in the realm of the Swarm Intelligent AS, ACS approach, as in other computational paradigms like Genetic Algorithms (GA), Evolutionary Programming (EP), as well as SOM (Self-Organizing Maps) and SA (Simulated Annealing). In order to deeply understand how a second co-evolved pheromone was useful to track the collective system into such results, a refined phase-space map was produced mapping the pheromones ratio between a pure Ant Colony System (where no negative feedback besides pheromone evaporation is present) and the present second-order approach. The evaporation rate between different pheromones was also studied and its influence in the outcomes of the algorithm is shown. A final discussion on the phase-map is included. This work has implications in the way large combinatorial problems are addressed as the double feedback mechanism shows improvements over the single-positive feedback mechanisms in terms of convergence speed and on major results.
Keywords: Stigmergy, Co-Evolution, Self-Organization, Swarm Intelligence, Foraging, Cooperative Learning, Combinatorial Optimization problems, Symmetrical Traveling Salesman Problems (TSP), phase-space.
Fig. – Comparing convergence results between Standard algorithms vs. Second Order Swarm Intelligence, over TSP fl1577 (click to enlarge).
Video by Yoav Ben-dov www.ybd.net [Hanoi, Vietnam, 24 Feb. 2009] – A nice example of self-organization as described by complexity theory. There are no fixed “top-down” laws (i.e. traffic lights), and yet the incredible traffic flows continuously. In complexity terms, the collective motion emerges from the multiple local interactions between the “agents” (drivers and pedestrians), mediated by horn sounds, eye contact, and body gestures.
“All men can see these tactics whereby I conquer, but what none can see is the strategy out of which victory is evolved.” ~ Sun Tzu, “The Art of War“.
During the October 1973 Arab-Israeli War (Yom Kippur War) highly strategic manoeuvres occurred on the Suez canal. It was crucial to surpass it on time. Rather quickly. The war was fought on October, between Israel and a coalition of Arab states led by Egypt and Syria, and it began when the coalition launched a joint surprise attack on Israel on Yom Kippur, the holiest day in Judaism, which coincided with the Muslim holy month of Ramadan. Egyptian and Syrian forces crossed ceasefire lines to enter the Israeli-held Sinai Peninsula and Golan Heights respectively, which had been captured and occupied since the 1967 Six-Day War [Wikipedia]. The conflict led to a near-confrontation between the two nuclear superpowers, the United States and the Soviet Union, both of whom initiated massive resupply efforts to their allies during the war.
Anyway, the war began with a massive and successful Egyptian crossing of the Suez Canal during the first three days, after which they dug in, settling into a stalemate. The Egyptian army put great effort into finding a quick and effective way of breaching the Israeli defences. But the Israelis had built a large 18 meter high sand walls with a 60 degree slope and reinforced with concrete at the water line. Egyptian engineers initially experimented with explosive charges and bulldozers to clear the obstacles, before a junior officer proposed using high pressure water cannons. The idea was tested and found to be a sound one, and several high pressure water cannons were imported from Britain and East Germany [Wikipedia]. The water cannons effectively breached the sand walls using water from the canal.
photo – Egyptian forces crossing the Suez Canal on October 7, 1973 [Source: Wikipedia]
After that success, however, a swift passage of the entire army over the Suez canal was needed. The problem was that the Egyptian army had to make a rather quick passage with several different convoys of tanks and regular logistic trucks, over very tiny bridges (fig.) as quick as possible. Some say, that the Egyptian general in charge did not halt any of the convoys, in order to give precedence to some in particular. Instead, contrary to logic, he gave an order for them to continuously flow, without having any official at the bridge entrance to organize them. Any right convoy tank that felt that the other left convoy truck should enter first, he would stop some seconds, and only after that, should make his own bridge passage over Suez. What’s history now, is that the decision, was entirely left to them, locally… and fluid. No “traffic lights” at all, … contrary to the usual hard strict regulaments of any army we know today. If that’s not wise tactics, tell me what it is?! …
Book – Carlo and Luigi Usai, “Stigmergy – The ultimate fantasy tale“, UniBook, Italy, 2011.
(in Italian from UniBook) […] Un libro destinato a lasciare un segno nelle Saghe Fantasy: il piccolo Curado si trova, suo malgrado, immerso in una serie di avventurose vicende attraverso il regno della Magia di un mondo Fantasy che richiama i migliori dei racconti della serie. Questo è il primo di una serie di volumi. Dal genio degli scrittori Fantasy Carlo Usai e Luigi Usai. Stigmergy è il primo episodio di una saga che è destinata a non lasciare indifferenti gli appassionati del genere. Stigmergy è stato già tradotto in lingua inglese, francese, spagnola e portoghese. Sono in corso le traduzioni in lingua cinese mandarino, arabo, tedesco, albanese, persiano e rumeno.
Luigi Usai è nato a Cagliari e vive a Verona, dove attualmente sta conseguendo la laurea Magistralis in Scienze Filosofiche. Ha già pubblicato “Riflessioni sul metodo cartesiano e la pratica musicale” col Gruppo Albatros. Stigmergy è il suo secondo lavoro. Carlo Usai è nato a Cagliari e vive a Verona, dove attualmente, dopo un lungo periodo di studi sul mondo Fantasy, decide di dare il suo contributo a questo filone narrativo. […]
Painting – Paul Klee, detail from “U struji sest pragova“, 1929.
“Nous avons une notion palpable de la métamorphose de la chenille. Nous, certainement, mais non la chenille.” ~ Edgar Allan Poe / “Le principe de l´evolution est beaucoup plus rapide en informatique que chez le bipède.” ~ Jean Dion / “Let chaos storm!… Let cloud shapes swarm!… I wait for form.” ~ Robert Frost
[…] In his notebooks the painter Paul Klee repeatedly insisted, and demonstrated by example, that the processes of genesis and growth that give rise to forms in the world we inhabit are more important than the forms themselves. ‘Form is the end, death’, he wrote. ‘Form-giving is movement, action. Form-giving is life’ (Klee 1973: 269). This, in turn, lay at the heart of his celebrated ‘Creative Credo’ of 1920: ‘Art does not reproduce the visible but makes visible’ (Klee 1961: 76). It does not, in other words, seek to replicate finished forms that are already settled, whether as images in the mind or as objects in the world. It seeks, rather, to join with those very forces that bring form into being. Thus the line grows from a point that has been set in motion, as the plant grows from its seed. Taking their cue from Klee, philosophers Gilles Deleuze and Félix Guattari argue that the essential relation, in a world of life, is not between matter and form, or between substance and attributes, but between materials and forces (Deleuze and Guattari 2004: 377). It is about the way in which materials of all sorts, with various and variable properties, and enlivened by the forces of the Cosmos, mix and meld with one another in the generation of things. And what they seek to overcome in their rhetoric is the lingering influence of a way of thinking about things, and about how they are made and used, that has been around in the western world for the past two millennia and more. It goes back to Aristotle. To create any thing, Aristotle reasoned, you have to bring together form (morphe) and matter (hyle). In the subsequent history of western thought, this hylomorphic model of creation became ever more deeply embedded. But it also became increasingly unbalanced. Form came to be seen as imposed, by an agent with a particular end or goal in mind, while matter – thus rendered passive and inert – was that which was imposed upon. […], in Tim Ingold, “Bringing Things to Life: Creative Entanglements in a World of Materials“, University of Aberdeen, July 2010 – Original version (April 2008 ) presented at ‘Vital Signs: Researching Real Life’, 9 September 2008, University of Manchester. (pdf link)
For some seconds, just imagine if bacteria had Twitter… As new research suggests microbial life can – in fact – be even richer: highly social, intricately networked, and teeming with interactions. So it’s probably time for you to say hello to… several trillion of your inner body friends. So much so, that the metabolic activity performed by these bacteria is equal to that of a virtual organ, leading to gut bacteria being termed a “forgotten” organ [O’Hara and Shanahan, “The gut flora as a forgotten organ“. EMBO reports 7, 688 – 693 (01 Jul 2006)]. My question however is, are they doing all these going beyond regular communication?
Flocks of migrating birds and schools of fish are familiar examples of spatial self-organized patterns formed by living organisms through social foraging. Such aggregation patterns are observed not only in colonies of organisms as simple as single-cell bacteria, as interesting as social insects like ants and termites as well as in colonies of multi-cellular vertebrates as complex as birds and fish but also in human societies [14]. Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective swarm intelligence. For example, termite colonies 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 defence without any central decision-making ability [8,53].(*)
Slime mould is another perfect example. These are very simple cellular organisms with limited motile and sensory capabilities, but in times of food shortage they aggregate to form a mobile slug capable of transporting the assembled individuals to a few feeding area. Should food shortage persist, they then form into a fruiting body that disperses their spores using the wind, thus ensuring the survival of the colony [30,44,53]. New research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions [47]. Bassler [3] and other researchers have determined that bacteria communicate using molecules comparable to pheromones. By tapping into this cell-to-cell network, microbes are able to collectively track changes in their environment, conspire with their own species, build mutually beneficial alliances with other types of bacteria, gain advantages over competitors, and communicate with their hosts – the sort of collective strategizing typically ascribed to bees, ants, and people, not to bacteria. Eshel Ben-Jacob [6] indicate that bacteria have developed intricate communication capabilities (e.g. quorum-sensing, chemotactic signalling and plasmid exchange) to cooperatively self-organize into highly structured colonies with elevated environmental adaptability, proposing that they maintain linguistic communication. Meaning-based communication permits colonial identity, intentional behavior (e.g. pheromone-based courtship for mating), purposeful alteration of colony structure (e.g. formation of fruiting bodies), decision-making (e.g. to sporulate) and the recognition and identification of other colonies – features we might begin to associate with a bacterial social intelligence. Such a social intelligence, should it exist, would require going beyond communication to encompass unknown additional intracellular processes to generate inheritable colonial memory and commonly shared genomic context. Moreover, Eshel [5,4] argues that colonies of bacteria are able to communicate and even alter their genetic makeup in response to environmental challenges, asserting that the lowly bacteria colony is capable of computing better than the best computers of our time, and attributes to them properties of creativity, intelligence, and even self-awareness.(*)
These self-organizing distributed capabilities were also found in plants. Peak and co-workers [37,2] point out that plants may regulate their uptake and loss of gases by distributed computation – using information processing that involves communication between many interacting units (their stomata). As described by Ball [2], leaves have openings called stomata that open wide to let CO2 in, but close up to prevent precious water vapour from escaping. Plants attempt to regulate their stomata to take in as much CO2 as possible while losing the least amount of water. But they are limited in how well they can do this: leaves are often divided into patches where the stomata are either open or closed, which reduces the efficiency of CO2 uptake. By studying the distributions of these patches of open and closed stomata in leaves of the cocklebur plant, Peak et al. [37] found specific patterns reminiscent of distributed computing. Patches of open or closed stomata sometimes move around a leaf at constant speed, for example. What’s striking is that it is the same form of mechanism that is widely thought to regulate how ants forage. The signals that each ant sends out to other ants, by laying down chemical trails of pheromone, enable the ant community as a whole to find the most abundant food sources. Wilson [54] showed that ants emit specific pheromones and identified the chemicals, the glands that emitted them and even the fixed action responses to each of the various pheromones. He found that pheromones comprise a medium for communication among the ants, allowing fixed action collaboration, the result of which is a group behaviour that is adaptive where the individual’s behaviours are not.(*)
In the offing… we should really look and go beyond regular communication to encompass unknown additional intracellular processes.
(*) excerpts from V. Ramos et al.: [a] Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes. (pdf) / [b] Computational Chemotaxis in Ants and Bacteria over Dynamic Environments. (pdf) / [c] (pdf) Societal Implicit Memory and his Speed on Tracking Dynamic Extrema. (pdf)
Figure – A swarm cognitive map (pheromone spatial distribution map) in 3D, at a specific time t. The artificial ant colony was evolved within 2 digital grey images based on the following work. The real physical “thing” can be seen here.
[] Vitorino Ramos, The MC2 Project [Machines of Collective Conscience]: A possible walk, up to Life-like Complexity and Behaviour, from bottom, basic and simple bio-inspired heuristics – a walk, up into the morphogenesis of information, UTOPIA Biennial Art Exposition, Cascais, Portugal, July 12-22, 2001.
Synergy (from the Greek word synergos), broadly defined, refers to combined or co-operative effects produced by two or more elements (parts or individuals). The definition is often associated with the holistic conviction quote that “the whole is greater than the sum of its parts” (Aristotle, in Metaphysics), or the whole cannot exceed the sum of the energies invested in each of its parts (e.g. first law of thermodynamics) even if it is more accurate to say that the functional effects produced by wholes are different from what the parts can produce alone. Synergy is a ubiquitous phenomena in nature and human societies alike. One well know example is provided by the emergence of self-organization in social insects, via direct (mandibular, antennation, chemical or visual contact, etc) or indirect interactions. The latter types are more subtle and defined as stigmergy to explain task coordination and regulation in the context of nest reconstruction in Macrotermes termites. An example, could be provided by two individuals, who interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time. In other words, stigmergy could be defined as a particular case of environmental or spatial synergy. Synergy can be viewed as the “quantity” with respect to which the whole differs from the mere aggregate. Typically these systems form a structure, configuration, or pattern of physical, biological, sociological, or psychological phenomena, so integrated as to constitute a functional unit with properties not derivable from its parts in summation (i.e. non-linear) – Gestalt in one word (the English word more similar is perhaps system, configuration or whole). The system is purely holistic, and their properties are intrinsically emergent and auto-catalytic.
A typical example could be found in some social insect societies, namely in ant colonies. Coordination and regulation of building activities on these societies do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony. Recruitment of social insects for particular tasks is another case of stigmergy. Self-organized trail laying by individual ants is a way of modifying the environment to communicate with nest mates that follow such trails. It appears that task performance by some workers decreases the need for more task performance: for instance, nest cleaning by some workers reduces the need for nest cleaning. Therefore, nest mates communicate to other nest mates by modifying the environment (cleaning the nest), and nest mates respond to the modified environment (by not engaging in nest cleaning).
Swarms of social insects construct trails and networks of regular traffic via a process of pheromone (a chemical substance) laying and following. These patterns constitute what is known in brain science as a cognitive map. The main differences lies in the fact that insects write their spatial memories in the environment, while the mammalian cognitive map lies inside the brain, further justified by many researchers via a direct comparison with the neural processes associated with the construction of cognitive maps in the hippocampus.
But by far more crucial to the present project, is how ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. There again, stigmergy is at work: ants deposit items at initially random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering organization and brood sorting a type of self-organization and adaptive behaviour, being the final pattern of object sptial distribution a reflection of what the colony feels and thinks about that objects, as if they were another organism (a meta- global organism).
As forecasted by Wilson [E.O. Wilson. The Insect Societies, Belknam Press, Cambridge, 1971], our understanding of individual insect behaviour together with the sophistication with which we will able to analyse their collective interaction would advance to the point were we would one day posses a detailed, even quantitative, understanding of how individual “probability matrices” (their tendencies, feelings and inner thoughts) would lead to mass action at the level of the colony (society), that is a truly “stochastic theory of mass behaviour” where the reconstruction of mass behaviours is possible from the behaviours of single colony members, and mainly from the analysis of relationships found at the basic level of interactions.
The idea behind the MC2 Machine is simple to transpose for the first time, the mammalian cognitive map, to a environmental (spatial) one, allowing the recognition of what happens when a group of individuals (humans) try to organize different abstract concepts (words) in one habitat (via internet). Even if each of them is working alone in a particular sub-space of that “concept” habitat, simply rearranging notions at their own will, mapping “Sameness” into “Neighborness“, not recognizing the whole process occurring simultaneously on their society, a global collective-conscience emerges. Clusters of abstract notions emerge, exposing groups of similarity among the different concepts. The MC2 machine is then like a mirror of what happens inside the brain of multiple individuals trying to impose their own conscience onto the group.
Through a Internet site reflecting the “words habitat”, the users (humans) choose, gather and reorganize some types of words and concepts. The overall movements of these word-objects are then mapped into a public space. Along this process, two shifts emerge: the virtual becomes the reality, and the personal subjective and disperse beliefs become onto a social and politically significant element. That is, perception and action only by themselves can evolve adaptive and flexible problem-solving mechanisms, or emerge communication among many parts. The whole and their behaviours (i.e., the next layer in complexity – our social significant element) emerges from the relationship of many parts, even if these later are acting strictly within and according to any sub-level of basic and simple strategies, ad-infinitum repeated.
The MC2 machine will reveal then what happens in many real world situations; cooperation among individuals, altruism, egoism, radicalism, and also the resistance to that radicalism, memory of that society on some extreme positions on time, but the inevitable disappearance of that positions, to give rise to the convergence to the group majority thought (Common-sense?), eliminating good or bad relations found so far, among in our case, words and abstract notions. Even though the machine composed of many human-parts will “work” within this restrict context, she will reveal how some relationships among notions in our society (ideas) are only possible to be found, when and only when simple ones are found first (the minimum layer of complexity), neglecting possible big steps of a minority group of visionary individuals. Is there (in our society) any need for a critical mass of knowledge, in order to achieve other layers of complexity? Roughly, she will reveal for instance how democracies can evolve and die on time, as many things in our impermanent world.
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)
Time-lapse imaging in live zebrafish embryos reveals that cerebellar granule cells migrate in chain-like structures as discovered by a recent article [1] [Köster et al., PLoS, Nov. 2009]. Figure above – Granule cells taken from the cerebellum of a pigeon (above, B) are shown in this 1899 drawing by legendary neuroscientist Santiago Ramón y Cajal.
Did talk about sticky objects and self-organization in the past, how positive and negative feedback’s stigmergic-like agents integrated could promote changes and learning over a complex system. Same happens to bacteria as also ants. On the other hand, we do know memes are also sticky (e.g. Chip Heath, Dan Heath, “Made to Stick: Why Some Ideas Survive and Others Die“, Random House, ISBN 978-1-4000-6428-1, January 2007). What’s new however, is that there are increasing proofs that our own brains my follow similar mechanisms (as Douglas Hofstadter in the past did made some analogies with how brains could work and how ant colonies raid different environments). In this recent new study, Köster and colleagues [1] [PLoS, Nov. 2009] reveal crucial pieces of this puzzle, showing how (neuronal) cells orient themselves to migrate together (like bacteria, above). The team studied zebrafish, one of the workhorses of developmental neurobiology, because its transparent body allows researchers to track movements of cells inside of it. As explained by Mason Inman [2]:
[…] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot. […] Slime molds provide a textbook example of self-organization. They live as single cells until food becomes scarce. Then, they broadcast chemical signals that trigger their mass assembly into a fruiting body, with some cells forming a stalk and others turning into spores that cast about in the winds to spread far and wide. […] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot.[…]
[…] But the mechanisms behind this coordinated movement – in particular, how each cell adjusts its inner workings to move to the right place at the right time – are only now starting to be revealed, using imaging that tracks these cells in live animals as they develop. […] To figure out what triggers the cells to line up and move together, the authors looked at what other kinds of cells were in the neighborhood. Many studies have shown that support cells, known as glial cells, often help guide neurons during these kinds of migrations. But during the first few days of the zebrafish embryo’s development, Köster and colleagues found, there were no glial cells along the granular cells’ migration route. That means these cells must go it alone, the team reasoned, with their own mechanism for signaling between each other to line up into chains and make their move. […] Although the study focused on just one type of brain cell, the findings could explain how many types of neurons find their way to their proper spots as the brain develops. There are still some pieces of the puzzle missing, however. While the findings explain how the granule cells are able to coordinate and follow their neighbors, it’s still not clear how the first few cells to head out on the journey – those at the front of the “conga line” – get oriented in the right direction. This suggests there must be some kind of signal from surrounding cells to get them headed in the right direction, the authors argue – yet another level of organization. […] , in Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS. [2]
[1] Rieger S, Senghaas N, Walch A, Köster RW (Nov., 2009) Cadherin-2 Controls Directional Chain Migration of Cerebellar Granule Neurons. PLoS Biology.
[2] Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS Biology.
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.
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.
Fig. – For a small sum Londoners may leave messages for friends in public spaces. When writing on “notificator” messages moves up behind window, remaining in view for two hours. Known as the “notificator,” the new machine was installed in streets, stores, railroad stations or other public places where individuals may leave messages for friends. Appeared in the American Modern Mechanix magazine (August, 1935) (via Modern Mechanix blog) + Maikelnai’s blog).
World’s first Twitter? Hell no! First World Twitter is kind of old, … it were Public Bathrooms !! As well as Hobo signs.
Figure – A sequential clustering task of corpses performed by a real ant colony. In here 1500 corpses are randomly located in a circular arena with radius = 25 cm, where Messor Sancta workers are present. The figure shows the initial state (above), 2 hours, 6 hours and 26 hours (below) after the beginning of the experiment (from: Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999).
The following research paper exploits precisely this phenomena into digital data.
[] Vitorino Ramos, Fernando Muge, Pedro Pina, Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies, in Javier Ruiz-del-Solar, Ajith Abraham and Mario Köppen (Eds.), Frontiers in Artificial Intelligence and Applications, Soft Computing Systems – Design, Management and Applications, 2nd Int. Conf. on Hybrid Intelligent Systems, IOS Press, Vol. 87, ISBN 1 5860 32976, pp. 500-509, Santiago, Chile, Dec. 2002.
Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects “swarm intelligence” resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. 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 (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems. Nevertheless, the present algorithm could be applied to any type of numeric data.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Chemoton § Vitorino Ramos' research notebook 
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