Surfaces and essences

[…] Analogy is the core of all thinking. – This is the simple but unorthodox premise that Pulitzer Prize-winning author Douglas Hofstadter and French psychologist Emmanuel Sander defend in their new work. Hofstadter has been grappling with the mysteries of human thought for over thirty years. Now, with his trademark wit and special talent for making complex ideas vivid, he has partnered with Sander to put forth a highly novel perspective on cognition. We are constantly faced with a swirling and intermingling multitude of ill-defined situations. Our brain’s job is to try to make sense of this unpredictable, swarming chaos of stimuli. How does it do so? The ceaseless hail of input triggers analogies galore, helping us to pinpoint the essence of what is going on. Often this means the spontaneous evocation of words, sometimes idioms, sometimes the triggering of nameless, long-buried memories.

Why did two-year-old Camille proudly exclaim, “I undressed the banana!”? Why do people who hear a story often blurt out, “Exactly the same thing happened to me!” when it was a completely different event? How do we recognize an aggressive driver from a split-second glance in our rear-view mirror? What in a friend’s remark triggers the offhand reply, “That’s just sour grapes”?  What did Albert Einstein see that made him suspect that light consists of particles when a century of research had driven the final nail in the coffin of that long-dead idea? The answer to all these questions, of course, is analogy-making – the meat and potatoes, the heart and soul, the fuel and fire, the gist and the crux, the lifeblood and the wellsprings of thought. Analogy-making, far from happening at rare intervals, occurs at all moments, defining thinking from top to toe, from the tiniest and most fleeting thoughts to the most creative scientific insights.

Like Gödel, Escher, Bach before it, Surfaces and Essences will profoundly enrich our understanding of our own minds. By plunging the reader into an extraordinary variety of colorful situations involving language, thought, and memory, by revealing bit by bit the constantly churning cognitive mechanisms normally completely hidden from view, and by discovering in them one central, invariant core – the incessant, unconscious quest for strong analogical links to past experiences – this book puts forth a radical and deeply surprising new vision of the act of thinking. […] intro to “Surfaces and Essences – Analogy as the fuel and fire of thinking” by Douglas Hofstadter and Emmanuel Sander, Basic Books, NY, 2013 [link] (to be released May 1, 2013).

Blindfold patterns

Picture – The idea of Viera da Silva’s art as a kind of code to be decoded comes across most clearly in The Chess Game – “O jogo de Xadrez” (above, Oil on Canvas, 1943). […] The checkered pattern of the chessboard extends beyond the table not only to the players themselves but also to the very landscape itself […] … Vieira da Silva would have loved The Matrix films […] (more).

Last night I decided to do something new. To play and broadcast live on Twitter, two chess games, blindfold. A 1st one with white pieces, another playing black. For that, I have chosen Chess Titans (link) has my contender,  a computer program most people can also access and try out over their PC’s. Chess Titans is a computer chess game developed by Oberon Games and vastly included in Windows Vista and Windows 7.  While broadcasting the game live, I added some of my thoughts while playing both games. Even if in brief, that was what I was feeling at the moment: what I was planning, and in what adversary menaces I mostly decided to spent my time.

For those reasons, what follows are those on-the-fly live comments, uncut, made at each moment, while I was thinking. No extra analysis is included here today. It will be more interesting for those who will read me on the future, I guess. This could give a precise idea what happened each time I have made a move, how I react it to some computer moves, and how some of my errors happened as you will see. How my mind went in one direction, or several, depending on the position. Those comments are highlighted by brackets () below, and were twitted live as they arrived to me. Besides, two subsequent comment brackets do not mean two subsequent twitter live chess thinking comments. Sometimes, several minutes have passed between those different thoughts.

As a final note, Chess Titans played each move in around 15-35 seconds, and in difficult positions, rarely, up to 2-3 minutes (I have chosen to play against the maximum level, 10). Playing blindfold, I have spent around 3-4 minutes for regular moves, like exchanging pieces, tweeting, etc, and mostly around 10-15 minutes for some positions, in quite difficult combinatorial patterns. First game playing white, endured 1h and a half (lost it) ,while the second almost 4h and 30 minutes within 58 moves. Here they are:

Game (1) Vitorino Ramos vs. Chess Titans level max.=10 [Sicilian] (LIVE on Twitter 23:00 GMT – 00:24 GMT, Dec. 20, 2012) Duration: 1h 24m.

1. e4, c5 2. c3, Nf6 3. Qc2, e5 4. Ne2, d5 5. exd5, Qxd5 (hmm … 6. d4 or 6.Ng3) 6. d4, Nc6 (7. c4 8. d5 but feeling problems later with his Nb4, Qa4+, Bd7!) 7. dxe5, Nxe5 8. Nf4, Qd7 9. Na3 (for 10. Bb5!), 9. …, Qe7 (was expecting 9. … a6) (10. Be3 seems too bad. Maybe 10. Be2 or Qe2. Or the line 10. Bb5+, Bd7, BxB, Nexd7+, Be3, Ng4 hmm … then Nd5!! ok … 10. Bb5+) 10. Bb5+, Bd7 11. 0-0, (better than BxB+ I guess cause of a future Ng4 by him), 11. …, g5

Chess diagram – crucial position after his 11. …, g5 move. White (me) to play.

(too risky maybe 12. Re1, gxN, Bxf4, Nf6-g4, f3 difficult for me to compute the rest) (12. Re1, gxN, Bxf4, Nf6-g4, f3, … hmm … Nexf3+ ?!!!)
(how about h3; 12. Re1, gxN, Bxf4, Nf6-g4, h3) (hmm???? 12. Re1, gxN, Bxf4, Nf6-g4, h3, Nxf2, Kxf2, Neg4+ ~ hmm) (we also have intermediate variants like, Bxb5, Nxb5, Q moves and gains one tempo by attacking the Knight on b5) (ok, no prob, here I go. This will be bloody …)

12. Re1 12. …, Nf3+ (Oooohhh NO!!!! damn, calculated this more ahead, not now. So stupid) 13. gxf3, Qxe1+ 14. Kg2, gxf4 (now he has Rg8++) (Bxf4 for Rg8+, Bg3 he has QxRa1, bad, bad) 15. Bxd7+, Nxd7 (h3 is an escape now for my King) (16. Rb1, Rg8+, Kh3, Qf1+, Kh4, Be7+ and I think I’m lost) (k, let’s sacrifice the Rook in a1) 16. Bxf4, Qxa1 (at least I have some counter-game now) 17. Qe4+, Be7 (Bd6 will not work due to Rg8+ followed by Qf1+ I guess…, damn, should have played 17. Qe2+!!) (Nb5 menacing Nc7+ or Nd6+ does not work either. Follows Rg8+, Kh3, Qf1+ and Q takes Nb5) (and for Qxb7 he has the robust Rb8 answer) (…. k, the end. Give up. Chess Titans level 10 won 1st game – 2nd game follows) 0-1

After two big blunders on the first game above (the bad 12. Re1 instead of a normal 12. Nd3 – check 1st diagram above -, and 17. Qe4+ instead of 17. Qe2+, since controlling f1 was crucial)  the second game did not started well also. After 6 moves I was already losing 1 pawn. Yet, still did manage to open the game and get the initiative a few moves later (around 14. …, Re8+). I feel OK with open and highly combinatorial games as these (normally it’s when I play better), but I forgot one simple fact: I was playing blindfold. Four and an half hours later I guess I’m happy to have managed to drawn a quite interesting and complex game, playing black pieces. What a long and stressful headache. Here:

Game (2) Chess Titans level max.=10 vs. Vitorino Ramos [English opening] (LIVE on Twitter 00:45 GMT – 05:12 GMT, Dec. 20, 2012) Duration: 4h 27m.

1. c4, e5 (English) 2. Nc3, f5 3. g3, c6 4. e4, fxe4 5. Qh5+ (that 4. …, fxe4 was too bad from me. Childish error. Did not see the typical Qh5+ trap, g6, Qxe5+ followed by QxRh8. It should have been 4. …, d6) 5, …, Ke7 6. Qxe5+, Kf7 7. Be2, Qf6 8. Qxe4, Bc5 9. Nf3, Nh6 10. d4 (hmm prepares Ng5+ ??!) 10. …, Bb4 11. Bg5, Qf5 12. Qxf5+, Nxf5 13. Bd3, d6 14. a3, Re8+ 15. Be2, Bxc3+ 16. bxc3, h6 17. Bd2, g5 18. h4, g4 19. Nh2, h5 20. Bf4, b5 21. cxb5, cxb5 22. f3, Bb7 23. Rf1, gxf3 24. Nxf3, Nd7 25. Kd1, a6 26. Ng1, Kg6 27. Re1, Rac8 28. a4 (hmm … Bxh5+ is dangerous if I move the rock in column c, like 28. …, Rxc3), 28. …, Nf6 29. axb5, axb5, 30. Ra7, Bc6 (did calculate Ba8 and Bd5 but hmm, I need d5 for my knight. His bishop on f4 must die) 31. Bd3, Nd5 32. Ne2

Chess diagram – position after his 32. Ne2 move. Black (me) to play. I’m 1 pawn down but with the initiative.

(I can’t take on c3 right? Nxc3, Nxc3, Bf3+, and then he goes back with Knight to e2, gee…) (hard position to mentally calculate) (32. …, b4 ?????) (damn, let me simplify all this…) 32. …, Ra8 33. Rxa8, Rxa8 34. Bxd6 (geee, that 31. Bd3 was so well played) 34. …, Ra1+ (will try to drawn him with successive pressure and checks, I guess) 35. Kd2, Ra2+ 36. Kc1 (yep, he prepares to play Bb1, I guess) 36. …, Nde3 37. Nf4+, Kf7 38. Nxh5, Ra1+ 39. Bb1 (only move for him. If not I change the rocks in e1 with time and then  his bishop on d6) 39. …, Be4

Chess diagram – position after my 39. …, Be4 move, pinning b2. White (computer) to play. I’m now 3 pawns down.

(Pinning. Guess this would end with 2 knights and 1 pawn against 1 knight and 4 pawns!!) 40. Kb2, Rxb1+ 41. Rxb1, Bxb1 42. Kxb1, Nxd6 43. Nf4 …

Chess diagram – position after his 43. Nf4 move. Black to play. Now I must stop two different white pawn clusters, on each side. Hard final.

(must be careful, now) (I guess I will do the obvious) (hmm, does not work, 43 …. Ne4 44. Ne2!) (wait, then King on f6, f5, g4 pressing g3) (k, here I go) 43. …, Ne4 44. Ne2, (now, I must think of my pawn on b5, hmm) (he has Ka2, a3 etc) (I have Nc4-d6, hope this helps, … here I go) 44. …, Kf6 45. Kb2, Kf5 46. h5 (?????!!!) 46. …, Kg5 47. h6 (?? He wants my King outside the centre, is that it? … I must take it) 47. …, Kxh6 48. Kb3 (yep, now I have problems on the other side) 48. …, Nd6 49. Kb4 (now my aim will be to arrive on f3 with my King) 49. …, Kg5 50. Kc5, Nec4 (freezing everything!) 51. d5 (hmm, I get it, he wants to reach Kd4 and Kd3. Anyway, I will go for the one in g3) 51. …, Kg4 52. Kc6

(what?????? he is just waiting) (hmm … wait, makes some sense. If 52…, Kf3 then 53. Nd4+, Kxg3 54. Nxb5, Nxb5 55. Kxb5 and I would have 1 knight against 2 pawns and my King far away) (hmm, hard call) (52…, Kf3 or not 52…, Kf3 ??!!!) (Kf3 followed by Ke3 and Kd3 etc does not work also, I think) (… hmm, wait, it might if he does not go Kc5, Kd4. If he goes I will the other way around by Kf4, Ke5)

52. …, Kf3 53. Nd4+, Kxg3 54. Kc5, Kf4 55. Nxb5, Ke5 (and it’s a drawn, I guess) 56. Kb4, Nxb5 57. Kxc4 57. …, Nxc3 ( if he goes 58. d6 then 58. …, Nd5! 59. d7, Nb6+ followed by Nxd7!!) 58. Kxc3, Kxd5 ½–½ (uuuufff, managing to draw blindfold, is a good result I guess :)

One of my conclusions: never play blindfold again in a open and highly combinatorial position, namely when you have a pair of knights. That, could make you dizzy and sick. Another (among, many others): never live tweet chess again. You will loose a lot of dumb followers (which turns-out to be healthy) and simultaneously attract all kinds of weirdos, and guru-like spam on-line marketeers. Vieira da Silva was right. It extends beyond the table. Like lake ripples when a stone is thrown.

On Water, Memory and Learning

Video – Water has Memory (from Oasis HD, Canada; link): just a liquid or much more? Many researchers are convinced that water is capable of “memory” by storing information and retrieving it. The possible applications are innumerable: limitless retention and storage capacity and the key to discovering the origins of life on our planet. Research into water is just beginning.

Water capable of processing information as well as a huge possible “container” for data media, that is something remarkable. This theory was first proposed by the late French immunologist Jacques Benveniste, in a controversial article published in 1988 in Nature, as a way of explaining how homeopathy works (link). Benveniste’s theory has continued to be championed by some and disputed by others. The video clip above, from the Oasis HD Channel, shows some fascinating recent experiments with water “memory” from the Aerospace Institute of the University of Stuttgart in Germany. The results with the different types of flowers immersed in water are particularly evocative.

This line of research also remembers me back of an old and quite interesting paper by a colleague, Chrisantha Fernando. Together with Sampsa Sojakka, both have proved that waves produced on the surface of water can be used as the medium for a Wolfgang Maass’ “Liquid State Machine” (link) that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Amazingly, Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. What follows is the abstract of their paper entitled “Pattern Recognition in a Bucket“, as well a PDF link onto it:

Figure – Typical wave patterns for the XOR task. Top-Left: [0 1] (right motor on), Top-Right: [1 0] (left motor on), Bottom-Left: [1 1] (both motors on), Bottom-Right: [0 0] (still water). Sobel filtered and thresholded images on right. (from Fig. 3. in in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003.

[…] Abstract. This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network. […] in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003. [PDF link]

Gum voting

Gum election in the public streets of Berlin – “Who sucks the worst? Vote with your gum“. Several weeks before the election on United States, this rather simple but extraordinary concept spread from NY city to San Francisco, from St. Louis to São Paulo, from Berlin to Sydney within a few days. This kind of remembers me one of my friend’s (Ivo et al’s) project – Stick Me!, due to some similar features. Even nowadays my own refrigerator has one Stick Me! sticker over it and I really enjoyed participating on it in the past via one very quick and humble “Stick Me Mate” proposal, while playing blitz chess with friends at a bar nearby my house.

A bunch of people (promoting Collective Intelligence?) is using the environment as a way to communicate (like over any chessboard). Communication is indirect, but still they communicate through the alterations and patterns they impose on the environment itself. Meanwhile, imposing a mark or sign somewhere, increases the probability of a second response later in time – a response to a stimulus (as ants put their pheromone marks on the ground). Though here however (on both projects) only positive feedback is used.

In fact, Mother Nature has conceived a very outstandingly simple and better strategy: their signs and cues vanish in time, simple as that! For instance, pheromone, a chemical substance segregated by ants and termites evaporates in time. Over here however, there is no evaporation at all working on (societal agents are not entitled to use negative feedbacks or using vanishing marks), which can curse it’s own dynamic – unless someone destroys the posters, of course. Amazon book recommendation system, works as well this way, that is by uniquely making use of positive feedbacks (people that bought this X book also as bought Y, etc). Unfortunately, Amazon system along with his wish lists could not integrate that someone who bought the X book did not bought Z (while others have done it), which basically leads to a snow-balling effect that does not self-organize in time (adapts) to new potential good-reading books. What you end up seeing is just the overall majority consensus, the “minimum common multiple” as I sometimes call it, who tends to over-look and underestimate some high potential new-coming solutions (over this precise context, good books coming in). Amazon should instead look carefully to some scientific works on collaborative filtering. Instead the consequences are this: check here for a real user feedback on what Amazon is suggesting, or in fact not suggesting at all.

Not only their system tends to adapt slowly, since the only thing it’s promoting is nothing else but memory (exploitation, which could be achieved by positive feedbacks), as he is not learning (exploration, which could be achieved by negative feedbacks), when we know that on the contrary, a delicate compromise between both is in fact of huge importance. The difficult but possible systemic trick is to remember the past as simultaneously innovating. If as a whole the system only remembers the past, no innovation is possible causing dramatic consequences when the “environment” changes. This could lead to stagnation. On the other hand, if too much systemic pressure is put on innovation itself, energy is lost, leading the system to explore the universe of possible solutions in a quite “”stupid” trial-and-error like random manner. Some dynamics between one thing (memory) and the other (learning) could be checked here (figs. 4,5,6,7 and 19), along with their speed.

After all a gum or a sticker is nothing else than a tag -as web blogging tags and internet tag clouds are. My question is – Could they vanish over time as I believe and propose they should? Having that question in mind, while looking at these precise public street projects, there are also other conceptual bridges we may found, as far as I recognize.

Let me refer at least 4, with the help of some passages below from other texts: (1) Hobo signs and codes (as well as the bottom-up like emergence of norms and ethical codes between them), (2) the role of Positive and Negative feedbacks briefly discussed above, (3) Swarm Intelligence and of course, (4) Stigmergy. In what specifically regards Hobo signs let me say that they are quite clever. Since they are done with chalk! So, rain and erosion could erase them, little by little, day by day. Thus, solutions that were good in the past, but no longer exist or that are partially vanished over time, tend to be replaced by new fresh ones, appropriated for the present, only loosing part of the whole systemic memory, serving us with new stimulus (we tend to respond to those fresh ones), allowing a continuous adaptation to reality. As I said in the past over a scientific invited lecture (not the right place to say it, though!), signs, quotes, delayed desynchronized dialogues and phrases over the doors of public bathrooms follow similar trends and tend to be stigmergic. In what regards the following four passages, I leave to you the connection between them (sorry for this now long food for thought post):

(1) […] 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 quote “the whole is greater than the sum of its parts” (Aristotle, in Metaphysics), 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 by Grassé 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 typical case of environmental synergy. Grassé showed that the coordination and regulation of building activities 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. Another illustration of how stimergy and self-organization can be combined into more subtle adaptive behaviors is recruitment in social insects. 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); that is stigmergy. […],

in 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.

(2) […] To cope with the difficulty of hobo life, hobos developed a system of symbols, or a code. Hobos would write this code with chalk or coal to provide directions, information, and warnings to other hobos. Some signs included “turn right here”, “beware of hostile railroad police”, “dangerous dog”, “food available here”, and so on. For instance: a cross signifies “angel food,” that is, food served to the hobos after a party. A triangle with hands signifies that the homeowner has a gun. Sharp teeth signify a mean dog. A square missing its top line signifies it is safe to camp in that location. A top hat and a triangle signify wealth. A spearhead signifies a warning to defend oneself. A circle with two parallel arrows means to get out fast, as hobos are not welcome in the area. Two interlocked humans signify handcuffs. (i.e. hobos are hauled off to jail). A Caduceus symbol signifies the house has a medical doctor living in it. A cat signifies that a kind lady lives here. A wavy line (signifying water) above an X means fresh water and a campsite. Three diagonal lines means it’s not a safe place. A square with a slanted roof (signifying a house) with an X through it means that the house has already been “burned” or “tricked” by another hobo and is not a trusting house. Two shovels, signifying work was available (Shovels, because most hobos did manual labor). […], in Hobo, Wikipedia.

(3) […] Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model (Stan Franklin, Coordination without Communication, talk at Memphis Univ., USA, 1996). […] (here)

Hobo or tramp markings at Algiers entrance to Canal Street Ferry across Mississippi River, New Orleans. Ferry is free for pedestrians or on bicycle. "X" means "OK", slashed circle "Good way to go". (via Wikipedia above).

(4) […] – Positive feedback, f+: in contrast to negative feedback, positive feedback generally promotes changes in the system (the majority of SO systems use them). The ex-plosive growth of the human population provides a familiar example of the effect of positive feedback. The snowballing auto catalytic effect of f+ takes an initial change in a system (due to amplification of fluctuations; a minimal and natural local cluster of objects could be a starting point) and reinforces that change in the same direction as the initial deviation. Self-enhancement, amplification, facilitation, and auto catalysis are all terms used to describe positive feedback. Another example could be provided by the clustering or aggregation of individuals. Many birds, such as seagulls nest in large colonies. Group nesting evidently provides individuals with certain benefits, such as better detection of predators or greater ease in finding food. The mechanism in this case is imitation : birds preparing to nest are attracted to sites where other birds are already nesting, while the behavioral rule could be synthesized as “I nest close where you nest“. The key point is that aggregation of nesting birds at a particular site is not purely a consequence of each bird being attracted to the site per se. Rather, the aggregation evidently arises primarily because each bird is attracted to others. On social insect societies, f+ could be illustrated by the pheromone reinforcement on trails, allowing the entire colony to exploit some past and present solutions. Generally, as in the above cases, positive feedback is imposed implicitly on the system and locally by each one of the constituent units. Fireflies flashing in synchrony follow the rule, “I signal when you signal”, fish traveling in schools abide by the rule, “I go where you go”, and so forth. In humans, the “infectious” quality of a yawn of laughter is a familiar example of positive feedback of the form, “I do what you do“. Seeing a person yawning , or even just thinking of yawning, can trigger a yawn. There is however one associated risk, generally if f+ acts alone without the presence of negative feedbacks, which per si can play a critical role keeping under control this snowballing effect, providing inhibition to offset the amplification and helping to shape it into a particular pattern. Indeed, the amplifying nature of f+ means that it has the potential to produce destructive explosions or implosions in any process where it plays a role. Thus the behavioral rule may be more complicated than initially suggested, possessing both an autocatalytic as well as an antagonistic aspect. In the case of fish, the minimal behavioral rule could be “I nest where others nest, unless the area is overcrowded“. In this case both the positive and negative feedback may be coded into the behavioral rules of the fish. Finally, in other cases one finds that the inhibition arises automatically, often simply from physical constraints. Since in SO systems their organization arises entirely from multiple interactions, it is of critical importance to question how organisms acquire and act upon information. Basically through two forms: a) information gathered from one’s neighbors, and b) information gathered from work in progress, that is, stigmergy. In the case of animal groups, these internal interactions typically involve information transfers between individuals. Biologists have recently recognized that information can flow within groups via two distinct pathways – signals and cues. Signals are stimuli shaped by natural selection specifically to convey information, whereas cues are stimuli that convey information only incidentally. The distinction between signals and cues is illustrated by the difference ant and deer trails. The chemical trail deposited by ants as they return from a desirable food source is a signal. Over evolutionary time such trails have been molded by natural selection for the purpose of sharing with nestmates information about the location of rich food sources. In contrast, the rutted trails made by deer walking through the woods is a cue, not shaped by natural selection for communication among deer but are a simple by-product of animals walking along the same path. SO systems are based on both, but whereas signals tends to be conspicuous, since natural selection has shaped signals to be strong and effective displays, information transfer via cues is often more subtle and based on incidental stimuli in an organism’s social environment. […], in Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes.

Stick Me! sticker in plain nature over Aljezur, Algarve (South of Portugal). Unknow author. "Copyrigthed nature" or a way of saying "I was here. I am connected. You could also be connected. We are all connected" ?!

Collective adaptive perception vs. memory in face of a sudden change

Transition behavior of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images (face of Einstein and a Map). Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels [in, V. Ramos, F. Almeida, “Artificial Ant Colonies in Digital Image Habitats – a mass behavior effect study on Pattern Recognition“, ANTS’00 Conf., Brussels, Belgium, 2000].

After “Einstein face” is injected as a substrate at t=0, 100 iterations occur. At this point you could recognize the face. Then, a new substrate (a new “environmental condition”) is imposed (Map image). The colony then adapts quickly to this new situation, losing their collective memory of past contours.

In white, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trough out the trails). It’s exactly this artificial evaporation and the computational ant collective group synergy reallocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and “perception” of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the successive alteration that they found on the Habitat. If you however, inject Einstein image again as a substrate, the whole ant society will converge again to it, but much faster than the first time, due to the residual memory distributed in the environment.

As a whole, the system is constantly trying to establish a proper compromise between memory (past solutions – via pheromone reinforcement) and novel ones in order to adapt (new conditions on the habitat, through pheromone evaporation). The right compromise, ables the system to tackle two contradictory situations: keeping some memory while learning something radically new. Antagonist features such as exploration and exploitation are tackled this way.