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Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!” ~ The Red Queen, at “Through the looking-glass, and what Alice found there“, Charles Lutwidge Dogson, 1871.

Your move. Alice was suddenly found in a new strange world. And quickly needed to adapt. As C.L. Dogson (most known as Lewis Carroll) brilliantly puts it, all the running you can do, does not suffices at all. This is a world (Wonderland) with different “physical” laws or “societal norms”. Surprisingly, those patterns appear also to us quite familiar, here, on planet Earth. As an example, the quote above is mainly the paradigm for Biological Co-Evolution, in the form of the Red-Queen effect.

In Wonderland (1st book), Alice follows the white rabbit, which end-ups driving her on this strange habitat, where apparently “normal” “physical” laws do not apply. On this second book however, Alice now needs to overcome a series of great obstacles – structured as phases in a game of chess – in order to become a queen.  Though, as she moves on, several other enigmatic personages appear. Punctuated as well as surrounded by circular arguments and logical paradoxes, Alice must keep on, in order to found the “other side of the mirror“.

There are other funny parallel moves, also. The story goes on that Lewis Carroll gave a copy of “Alice in Wonderland” to Queen Victoria who then asked him in return to send her his next book as she fancied the first one. The joke is that the book was (!) … “An Elementary Treatise on Determinants, With Their Application to Simultaneous Linear Equations and Algebraic Equations (link)”. Lewis Carroll then went on to write a follow-on to Alice in Wonderland entitled “Through the Looking-Glass, and what Alice found there” that features a chess board on his first pages, and where chess was used to gave her, Queen Victoria, a glimpse on what Alice explored on this new world.

In fact, the diagram on the first pages contains not only the entire book chapters of this novel as well how Alice moved on. Where basically, each move, moves the reader to a new chapter (see below) representing it. The entire book could be found here in PDF format.  Besides the beauty and philosophical value of Dogson‘s novel on itself, and his repercussions on nowadays co-Evolution research as a metaphor, this is much probably the first “chess-literature” diagram ever composed. Now, of course, pieces are not white and black, but instead white and red (note that pieces in c1 – queen – and c6 – king – are white). Lewis Carroll novel, then goes on like this: White pawn (Alice) to play, and win in eleven moves.

However, in order to enter this world you must follow the “rules” of this new world. “Chess” in here is not normal, as Wonderland was not normal to Alice’s eyes. Remember: If you do all do run you could do, you will find yourself at the same place. Better if you could run twice as fast! First Lewis Carroll words on his second book (at the preface / PDF link above) advise us:

(…) As the chess-problem, given on a previous page, has puzzled some of my readers, it may be well to explain that it is correctly worked out, so far as the moves are concerned. The alternation of Red and White is perhaps not so strictly observed as it might be, and the ‘castling’ of the three Queens is merely a way of saying that they entered the palace; but the ‘check’ of the White King at move 6, the capture of the Red Knight at move 7, and the final ‘check-mate’ of the Red King, will be found, by any one who will take the trouble to set the pieces and play the moves as directed, to be strictly in accordance with the laws of the game. (…) Lewis Carroll, Christmas,1896.

Now, the solution, could be delivered in various format languages. But here is one I prefer. It was encoded on classic BASIC, running on a ZX Spectrum emulator. Here is an excerpt:

1750 LET t$=”11. Alice takes Red Queen & wins(checkmate)”: GO SUB 7000 (…)
9000 REM
9001 REM ** ZX SPECTRUM MANUAL Page 96 Chapter 14. **
9002 REM
9004 RESTORE 9000 (…)
9006 LET b=BIN 01111100: LET c=BIN 00111000: LET d=BIN 00010000
9010 FOR n=1 TO 6: READ p$: REM 6 pieces
9020 FOR f=0 TO 7: REM read piece into 8 bytes
9030 READ a: POKE USR p$+f,a
9040 NEXT f
9100 REM bishop
9110 DATA “b”,0,d,BIN 00101000,BIN 01000100
9120 DATA BIN 01101100,c,b,0
9130 REM king
9140 DATA “k”,0,d,c,d
9150 DATA c,BIN 01000100,c,0
9160 REM rook
9170 DATA “r”,0,BIN 01010100,b,c
9180 DATA c,b,b,0
9190 REM queen
9200 DATA “q”,0,BIN 01010100,BIN 00101000,d
9210 DATA BIN 01101100,b,b,0
9220 REM pawn
9230 DATA “p”,0,0,d,c
9240 DATA c,d,b,0
9250 REM knight
9260 DATA “n”,0,d,c,BIN 01111000
9270 DATA BIN 00011000,c,b,0

(…) full code on [link]

This is BASIC-ally Alice’s story …

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Video – Awesome choice by Tim Burton. It fits him like a glove. Here is the official Tim Burton’s Alice in Wonderland teaser trailer (just uploaded yesterday). Alice in Wonderland is directed by visionary director Tim Burton, of everything from Pee-Wee’s Big Adventure to Beetlejuice to Batman to Edward Scissor hands to Mars Attacks to Sleepy Hollow to Charlie and the Chocolate Factory to Sweeney Todd most recently. This is based on Lewis Carroll’s beloved series of books that were first published in 1865. Disney is bringing Tim Burton’s Alice in Wonderland to both digital 3D and 2D theaters everywhere on March 5th, 2010 early next year (more). Finally, just one personal thought. Soon, Tim Burton’s will stand for cinema, as what Jules Verne represented in literature.

In 1973, under several ongoing works on Co-Evolution and Evolutionary theory, L. van Alen proposed a new hypothesis: the Red Queen effect [1]. According to him, several different species will migth propably undergo and submit themselves to a continuous re-adapation [2,3], being it genetic or synaptic, only to end themselves at the point they started. A kind of arms races between species [4], potentially leading to specialization, as well as evolutionary Punctuated equilibria [5,6].

Van Alen chose the name “Red Queen” in allusion to the romance “Alice in Wonderland”, from Charles Lutwidge Dodgson (better known as Lewis Carroll) published in 1865. Over this country (Wonderland) it was usual to run as quick as you could, just to end yourself at the same place. The dialogs between Alice and the Red Queen are sintomatic:

[…] ‘Now! Now!’ cried the Queen. ‘Faster! Faster!’ And they went so fast that at last they seemed to skim through the air, hardly touching the ground with their feet, till suddenly, just as Alice was getting quite exhausted, they stopped, and she found herself sitting on the ground, breathless and giddy. The Queen propped her up against a tree, and said kindly, ‘You may rest a little, now. Alice looked round her in great surprise. ‘Why, I do believe we’ve been under this tree the whole time! Everything’s just as it was!’ ‘Of course it is,’ said the Queen. ‘What would you have it?’. ‘Well, in our country, said Alice, still panting a little, ‘you’d generally get to somewhere else – if you ran very fast for a long time as we’ve been doing.’ ‘A slow sort of country!’ said the Queen. ‘Now, here, I see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!‘ […]

Meanwhile, since 2007 (even much earlier!) I have taken Alice into my own arms. In fact, she is not heavy at all. If you feel you should keep running, some should, have a read on “Co-Cognition, Neural Ensembles and Self-Organization“, extended abstract for a seminar talk at ISR – Institute for Systems and Robotics, Technical Univ. of Lisbon (IST), May 31, 2007. Written at Granada University, Spain, 29 May 2007.

[1] van Alen, L. (1973), “A New Evolutionary Law“, Evolutionary Theory, 1, pp. 1-30.
[2] Cliff D., Miller G.F. (1995), “Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations“, in F. Moran, A. Moreno, J.J. Merelo and P. Cachon (editors) Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life (ECAL95). Lecture Notes in Artificial Intelligence 929, Springer- Verlag, pp.200-218.
[3] Cliff D., Miller G.F. (1996), “Co-Evolution of Pursuit and Evasion II: Simulation Methods and Results“. In P. Maes et al. (Eds.), From Animals to Animats IV, Procs. of the Fourth Int. Conf. on Simulation of Adaptive Behaviour, MIT Press, pp. 506-515.
[4] Dawkins R., Krebs J.R. (1979), “Arms Races between and within Species“. In Procs. of the Royal Society of London: Biological Sciences, nº. 205, pp. 489-511.
[5] Eldredge, N., Gould, S. J., “Punctuated equilibria: an alternative to phyletic gradualism“. In: Models In Paleobiology (Ed. by T. J. M. Schopf), 1972.
[6] Gould, S. J., & Eldredge, N., “Punctuated equilibria: the tempo and mode of evolution reconsidered“. Paleobiology, 3, 115-151, 1977.

Knight-Death-and-the-Devil-Albrecht-Duerer

Fig. – Knight, Death and the Devil (1513). This is one of three metal engravings by Albrecht Dürer in a series called Meisterstiche (since I have started this blog, I have also chosen a woodcut engraving done by Dürer, – his Rhinoceros – for several reasons, one being that it appeared in Europe for the fisrt time trough Lisbon in 1515). The others are Melancholia I and Saint Jerome in His Study. The engraving is dated 1513, two hundred years after the dissolution of the Knights Templar in 1313. We see a skull in the bottom left corner; the night in full armour (shining armor?) carries a lance; behing him is a pig-snouted horned devil and he is passing Death on his pale horse, who is carrying an hourglass. Under the knight’s horse runs a long-haired retriever, a hunting dog. Dürer called this picture Reuter, which is, Rider. (source).

Every evil leaves a sorrow in the memory, until the supreme evil, death,
wipes out all memories together with all life
“. Leonardo da Vinci.

Carlos Gershenson (Complexes blog), some days ago just uploaded a short (5 pp.) philosophical essay about life, death and artificial life (*) (aLife), which I vividly recommend. He starts his “What Does Artificial Life Tell Us About Death?” with this precise Leonardo’s quote (above). Among other passages it’s interesting to see how different notions of death are deduced from a limited set of different notions of life (in many situations, opposing terms could be used to define each other). Carlos points us out to six currents, or lines of thought:

• If we consider life as self-production (Varela et al., 1974; Maturana and Varela, 1980, 1987; Luisi, 1998), then death will the the loss of that self-production ability.
• If we consider life as what is common to all living beings (De Duve, 2003, p. 8), then death implies the termination of that commonality, distinguishing it from other living beings.
• If we consider life as computation (Hopfield, 1994), then death will be the end (halting?) of that computing process.
• If we consider life as supple adaptation (Bedau, 1998), death implies the loss of that adaptation.
• If we consider life as a self-reproducing system capable of at least one thermodynamic work cycle (Kauffman, 2000, p. 4), death will occur when the system will be unable to perform thermodynamic work.
• If we consider life as information (a system) that produces more of its own information than that produced by its environment (Gershenson, 2007), then death will occur when the environment will produce more information than that produced by the system.

I was aware of Kauffman’s “blender thought experiment”, however Gershenson adds much more into it. A variation. He goes on like this. Nice reading:

[…] Focussing on our understanding of death, this will depend necessarily on our understanding of life, and vice versa. Throughout history there have been several explanations to both life and death, and it seems unfeasible that a consensus will be reached. Thus, we are faced with multiple notions of life, which imply different notions of death. However, generally speaking, if we describe life as a process, death can be understood as the irreversible termination of that process. The general notion of life as a process or organization (Langton, 1989; Sterelny and Griffiths, 1999; Korzeniewski, 2001) has expelled vitalism from scientific worldviews. Moreover, there are advantages in describing living systems from a functional perspective, e.g. it makes the notion of life independent of its implementation. This is crucial for artificial life. Also, we know that there is a constant flow of matter and energy in living systems, i.e. their physical components can change while the identity of the organism is preserved. In this respect, one can make a variation of Kauffman’s “blender thought experiment” (Kauffman, 2000): if you put a macroscopic living system in a blender and press “on”, after some seconds you will have the same molecules that the living system had. However, the organization of the living system is destroyed in the blending. Thus, life is an organizational aspect of living systems, not so much a physical aspect. Death occurs when this organization is lost. […]

(*) even if, I do not recommend this Wikipedia entry. Extremely poor.

 

Dynamic Optimization Problems (DOP) solved by Swarm Intelligence (dynamic environment) - Vitorino Ramos

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

Swarm adaptive response over time, under sever dynamics

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

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

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

PDF paper.

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

 

Scilla's Treatise 1670Fig. – The famous frontispiece from Scilla’s treatise of 1670 defending the organic nature of fossils. The solid young man, representing the truth of sensory experi­ence, shows a fossil sea urchin in his right hand to a wraithlike figure represent­ing the former style of speculative thinking. With his left hand, the solid figure points to other fossils found in Sicily. The text proclaims: “Vain speculation un­deceived by the senses.” (from, Stephen Jay Gould, “The Structure Of Evolutionary Theory”, The Belknap Press of Harvard University Press”, Cambridge, Massachusetts, 2002).

Exaptation: 1. The use of a biological structure or function for a purpose other than that for which it initially evolved. 2. An evolutionary process in which a given adaptation is first naturally selected for, and subsequently used by the organism for something other than its original, intended purpose. 3. Exaptations – Features (such as feathers) that evolved by selection for one purpose (such as warmth) and were later adapted to a new purpose (such as flight). [more]. Exaptive: to show exaptation – featuring it.

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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