Archive for the ‘Complexity’ Category

Changing or increasing functionality of circuits in biological evolution is a form of computational learning. – Leslie Valiant

The title of this post comes from Prof. Leslie Valiant‘s The ACM Alan M. Turing award lecture titled “The Extent and Limitations of Mechanistic Explanations of Nature”.

Prof. Leslie G. Valiant

Click on the image above to watch the lecture

[Image Source: CACM “Beauty and Elegance”]

Short blurb: Though the lecture came out sometime in June-July 2011, and I have shared it (and a paper that it quotes) on every online social network I have presence on, I have no idea why I never blogged about it.

The fact that I have zero training (and epsilon knowledge of) in biology that has not stopped me from being completely fascinated by the contents of the talk and a few papers that he cites in it. I have tried to see the lecture a few times and have also started to read and understand some of the papers he mentions. Infact, the talk has inspired me enough to know more about PAC Learning than the usual Machine Learning graduate course might cover. Knowing more about it is now my “full time side-project” and it is a very exciting side-project to say the least!


Getting back to the title: One of the motivating questions about this work is the following:

It is widely accepted that Darwinian Evolution has been the driving force for the immense complexity observed in life or how life evolved. In this beautiful 10 minute video Carl Sagan sums up the timeline and the progression:

There is however one problem: While evolution is considered the driving force for such complexity, there isn’t a satisfactory explanation of how 13.75 billion years of it could have been enough. Many have often complained that this reduces it to a little more than an intuitive explanation. Can we understand the underlying mechanism of Evolution (that can in turn give reasonable time bounds)? Valiant makes the case that this underlying mechanism is of computational learning.

There have been a number of computational models that have been based on the general intuitive idea of Darwinian Evolution. Some of these include: Genetic Algorithms/Programming etc. However, people like Valiant amongst others find such methods useful in an engineering sense but unsatisfying w.r.t the question.

In the talk Valiant mentions that this question was asked in Darwin’s day as well. To which Darwin proposed a bound of 300 million years for such evolution to occur. This immediately fell into a problem as Lord Kelvin, one of the leading physicists of the time put the figure of the age of Earth to be 24 million years. Now obviously this was a problem as evolution could not have happened for more than 24 million years according to Kelvin’s estimate. The estimate of the age of the Earth is now much higher. ;-)

The question can be rehashed as: How much time is enough? Can biological circuits evolve in sub-exponential time?

For more I would point out to his paper:

Evolvability: Leslie Valiant (Journal of the ACM – PDF)

Towards the end of the talk he shows a Venn diagram of the type usually seen in complexity theory text books for classes P, NP, BQP etc but with one major difference: These subsets are fact and not unproven:

Fact: Evolvability \subseteq SQ Learnable \subseteq PAC Learnable

*SQ or Statistical Query Learning is due to Michael Kearns (1993)

Coda: Valiant claims that the problem of evolution is no more mysterious than the problem of learning. The mechanism that underlies biological evolution is “evolvable target pursuit”, which in turn is the same as “learnable target pursuit”.


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In a number of seminars at a lot of universities or industry interactions one of the hot topics these days is efficient wireless power transfer and the pressing need and desirability of it. It is even more interesting given that wireless power is nothing new at all. One of the earliest patents in this area was given in 1900 to the legendary Nikola Tesla (Patent number: 649621) and there has been a discussion on it ever since. Probably now is the time to really realize Tesla’s vision with the number of devices of daily usage growing rapidly.

[Left Nikola Tesla | Right Alanson Sample, Intel Engineer Demonstrating WREL (Image Source)]

Intel has been at present working on what they call the Wireless Resonant Energy Link, which is based on the work of some MIT physicists. In the image above, an Intel engineer is seen demonstrating powering of a 60 W bulb wirelessly. Doing so requires more power than what is needed to charge a laptop. The implications of this technology can be immense. However the adverse effects of such technology on humans remain to be seen but are not viewed as a major impediment to its development.

Another area that is being discussed extensively these days is claytronics, catoms or simply programmable matter. Let’s take a brief digression into this before coming back to the original topic.

Claytronics: Claytronics seems to be one of the most futuristic and promising application areas of the intersection of Robotics, Swarm Intelligence and Computer Science among others. Claytronics is a field concerning reconfigurable nanoscale robots (which are called Claytronic Atoms or Catoms) which can operate as a swarm and can be desgined to form much more complex elements and perform complex tasks. These sub-millmeter computers eventually would have the ability to move around, communicate with other computers, and even electrostatically attach to each other to allow the swarm to take different shapes.

Catoms also referred to as programmable matter could reconfigure to form almost any shape, take any color or texture. Some interesting speculations include that catoms could be morphed to form replicas of humans (for virtual meetings) as well.  For a brief initiating idea have a look at the video below:

Work on this has been done by Prof Seth Goldstein and his group at Carnegie Mellon and is still on under the name the Claytronics Project. This work has been expanded upon by Intel researchers.

A senior researcher at Intel Jason Campbell has the following to say on just SOME possibilities that we could have in the future from programmable matter.

Think of a mobile device, My cell phone is too big to fit comfortably in my pocket and too small for my fingers. It’s worse if I try to watch movies or do my e-mail. But if I had 200 to 300 milliliters of catoms, I could have it take on the shape of the device that I need at that moment. For example, the catoms could be manipulated to create a larger keypad for text messaging. And when the device wasn’t being used, I could command it to form its smallest shape or even be a little squishy, so I can just drop it in my pocket.

Battery Powered Robots, An impediment to Research in SI based Robotic Systems:

There has been a lot of research going on swarm robotics. Taking just two examples, consider the work of James McLurkin of the CSAIL, MIT and the work at Ecole Polytechnique Federale de Lausanne (EPFL) in Lausanne, Switzerland. A lot of James’ work can be seen here, with a number of videos and papers available for download.

In the video below, a swarm of 278 miniature e-puck robots move around. All of them are battery powered. Battery powered robots can not only be a headache but a severe research impediment as the size of the swarm increases.

It thus would be very desirable that the swarm is wirelessly powered.

So, in short a lot of work is being done in the above two fields but what is also required is an intersection of the two, and this is exactly what Travis Deyle of Georgia Tech and Dr Matt Reynolds of Duke have done. Their work, Surface based wireless power transmission and bidirectional communication for autonomous robot swarms. presented at the IEEE ICRA this year details the construction of a 60cmx60cm surface that provides wireless power and bi-directional communication to an initial swarm of 5 line following robots. Each robot had a power consumption of about 200 mW.

[Image Courtesy : Travis Deyle]

An actual robot looks like the following in close up.

Wirelessly Powered Robot Swarm from Travis on Vimeo.

For more extensive details about the setup and circuit details have a look at their paper and the presentation slides.

Related Posts:

Morphogenesis and Swarm Robotics

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Well just a fortnight or so back I discovered that Dr Radford Neal, one of the top researchers in Statistics and Machine Learning was blogging. And today morning I discovered Dr Vitorino Ramos has been blogging for over a week now too!

This comes as a surprise, but a very pleasant one. I am very glad to have found his page, it promises to be a very different Web-Log and could indeed grow into one of the top blogs on Swarming, Self-Organization, Complexity and Distributed Systems as it would be by a leading expert in the field. It would be great to catch up on his work. In the past I have tried to write on some of his interesting work on my own page. My posts can be found here.

[Vitorino Ramos: Image Source]

Dr Ramos’ research areas are chiefly in Artificial Life, Artificial Intelligence, Bio-Inspired Computing, Collective Intelligence and Complex Systems. He obtained his PhD in 2004 and has published about 70 papers in the above fields and their broad application areas. So put simply it can be said that the IQ of the “blogosphere” has gone up a little with this addition.

For starters I would recommend his article on Financial Markets (given the situation today), talking about the herd mentality and the resulting amplification in dumb investors and its results and what it could result in. Most investors do not understand much of the market mechanism. This is a bare fact put most aptly in this cartoon I found on his blog, and his post goes much beyond that.

[Image Source]

Click to Enlarge

And going by the website and blog name, it seems that Dr Ramos is now interested in some sense in Tibor Ganti’s Chemoton Theory.

Quick Links:

1. Vitorino Ramos’ Homepage.

2. Dr Ramos’ Publications. (PDFs available online)

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Some posts back, i posted on Non-Human Art or Swarm Paintings, there I mentioned that those paintings were NOT random but were a Colony Cognitive Map.

This post will serve as the conceptual basis for the Swarm Paintings post, the next post and a few future posts on image segmentation.

Motivation: Some might wonder what is the point of writing about such a topic. And that it is totally unrelated to what i write about generally. No! That is not the case. Most of the stuff I write about is related in some sense. Well the motivation for reading thoroughly about this (and writing) maybe condensed into the following:

1. The idea of a colony cognitive map is used in SI/A-life experiments, areas that really interest me.

2. Understanding the idea of colony cognitive maps gives a much better understanding of the inherent self organization in insect swarms and gives a lead to understand self organization in general.

3. The parallel to colony cognitive maps, the cognitive maps follow from cognitive science and brain science. Again areas that really interest me as they hold the key for the REAL artificial intelligence evolution and development in the future.

The term “Colony Cognitive Map” as i had pointed earlier is in a way a parallel to a Cognitive Map in brain science (i use the term brain science for a combination of fields like neuroscience, Behavioral psychology, cognitive sciences and the likes and will use it in this meaning in this post ) and also that the name is inspired from the same!

There is more than just a romantic resemblance between the self-organization of “simple” neurons into an intelligent brain like structure, producing behaviors well beyond the capabilities of an individual neuron and the self-organization of simple and un-intelligent insects into complex swarms and producing intelligent and very complex and also aesthetically pleasing behavior! I have written previously on such intelligent mass behavior. Consider another example, neurons are known to transmit neurotransmitters in the same way a social insect colony is marked by pheromone deposition and laying.

[Self Organization in Neurons (Left) and a bird swarm(Below).  Photo Credit >> Here and Here]

First let us try to revisit what swarm intelligence roughly is (yes i still am to write a post on a mathematical definition of the same!), Swarm Intelligence is basically a property of a system where the collective actions of unsophisticated agents, acting locally causes functional and sophisticated global patterns to emerge. Swarm intelligence gives a scheme to explore decentralized problem solving. An example that is also one of my favorites is that of a bird swarm, wherein the collective behaviors of birds each of which is very simple causes very complex global patterns to emerge. Over which I have written previously, don’t forget to look at the beautiful video there if you have not done so already!

Self Organization in the Brain: Over the last two months or so i had been reading Douglas Hofstadter’s magnum opus, Gödel, Escher, Bach: an Eternal Golden Braid (GEB). This great book makes a reference to the self organization in the brain and its comparison with the behavior of the ant colonies and the self organization in them as early as 1979.

[Photo Source: Wikipedia Commons]

A brain is often regarded as one of the most if not the most complex entity. However if we look at a rock it is very complex too, but then what makes a brain so special? What distinguishes the brain from something like a rock is the purposeful arrangement of all the elements in it. The massive parallelism and self organization that is observed in it too amongst other things makes it special. Research in Cybernetics in the 1950s and 1960s lead the “cyberneticians” to try to explain the complex reactions and actions of the brain without any external instruction in terms of self organization. Out of these investigations the idea of neural networks grew out (1943 – ), which are basically very simplified models of how neurons interact in our brains. Unlike the conventional approaches in AI there is no centralized control over a neural network. All the neurons are connected to each other in some way or the other but just like the case in an ant colony none is in control. However together they make possible very complex behaviors. Each neuron works on a simple principle. And combinations of many neurons can lead to complex behavior, an example believed to be due to self-organization. In order to help the animal survive in the environment the brain should be in tune with it too. One way the brain does it is by constantly learning and making predictions on that basis. Which means a constant change and evolution of connections.

Cognitive Maps: The concept of space and how humans perceive it has been a topic that has undergone a lot of discussion in academia and philosophy. A cognitive map is often called a mental map, a mind map, cognitive model etc.

The origin of the term Cognitive Map is largely attributed to Edward Chace Tolman, here cognition refers to mental models that people use to perceive, understand and react to seemingly complex information. To understand what a mental model means it would be favorable to consider an example I came across on wikipedia on the same. A mental model is an inherent explanation in somebody’s thought process on how something works in the spatial or external world in general. It is hypothesized that once a mental model for something or some representation is formed in the brain it can replace careful analysis and careful decision making to reduce the cognitive load. Coming back to the example consider a mental model in a person of perceiving the snake as dangerous. A person who holds this model will likely rapidly retreat as if is like a reflex without initial conscious logical analysis. And somebody who does not hold such a model might not react in the same way.

Extending this idea we can look at cognitive maps as a method to structure, organize and store spatial information in the brain which can reduce the cognitive load using mental models and and enhance quick learning and recall of information.

In a new locality for example, human way-finding involves recognition and appreciation of common representations of information such as maps, signs and images so to say. The human brain tries to integrate and connect this information into a representation which is consistent with the environment and is a sort of a “map”. Such spatial (not necessarily spatial) internal representations formed in the brain can be called a cognitive map. As the familiarity of a person with an area increases then the reliance on these external representations of information gradually reduces. And the common landmarks become a tool to localize within a cognitive map.

Cognitive maps store conscious perceptions of the sense of position and direction and also the subconscious automatic interconnections formed as a result of acquiring spatial information while traveling through the environment. Thus they (cognitive maps) help to determine the position of a person, the positioning of objects and places and the idea of how to get from one place unto another. Thus a cognitive map may also be said to be an internal cognitive collage.

Though metaphorically similar the idea of a cognitive map is not really similar to a cartographic map.

Colony Cognitive Maps: With the above general background it would be much easier to think of a colony cognitive map. As it is basically a analogy to the above. As described in my post on adaptive routing, social insects such as ants construct trails and networks of regular traffic via a process of pheromone deposition, positive feedback and amplification by the trail following. These are very similar to cognitive maps. However one obvious difference lies in the fact that cognitive maps lie inside the brain and social insects such as ants write their spatial memories in the external environment.

Let us try to picture this in terms of ants, i HAVE written about how a colony cognitive map is formed in this post without mentioning the term.

A rather indispensable aspect of such mass communication as in insect swarms is Stigmergy. Stigmergy refers to communication indirectly, by using markers such as pheromones in ants. Two distinct types of stigmergy are observed. One is called sematectonic stigmergy, it involves a change in the physical environment characteristics.An example of sematectonic stigmergy is nest building wherein an ant observes a structure developing and adds its ball of mud to the top of it. Another form of stigmergy is sign-based and hence indirect. Here something is deposited in the environment that makes no direct contribution to the task being undertaken but is used to influence the subsequent behavior that is task related. Sign based stigmergy is very highly developed in ants. Ants use chemicals called as pheromones to develop a very sophisticated signaling system. Ants foraging for food lay down some pheromone which marks the path that they follow. An isolated ant moves at random but an ant encountering a previously laid trail will detect it and decide to follow it with a high probability and thereby reinforce it with a further quantity of pheromone. Since the pheromone will evaporate the lesser used paths will gradually vanish. We see that this is a collective behavior.

Now we assume that in an environment the actors (say for example ants) emit pheromone at a set rate. Also there is a constant rate at which the pheromone evaporates. We also assume that the ants themselves have no memory of previous paths taken and act ONLY on the basis of the local interactions with pheromone concentrations in the vicinity. Now if we consider the “field” or “map” that is the overall result and formed in the environment as a result of the movements of the individual ants over a fixed period of time. Then this “pheromonal” field contains information about past movements and decisions of the individual ants.

The pheromonal field (cognitive map) as i just mentioned contains information about past movements and decisions of the organisms, but not arbitrarily far in the past since the field “forgets” its distant history due to evaporation in time. Now this is exactly a parallel to a cognitive map, with the difference that for a colony the spatial information is written in the environment unlike inside the brain in the case of a human cognitive map. Another major similarity is that neurons release a number of neurotransmitters which can be considered to  be a parallel to the pheromones released as described above! The similarities are striking!

Now if i look back at the post on swarm paintings, then we can see that the we can make such paintings, with the help of a swarm of robots. More pheromone concentration on a path means more paint. And hence the painting is NOT random but is EMERGENT. I hope i could make the idea clear.

How Swarms Build Colony Cognitive Maps: Now it would be worthwhile to look at a simple model of how ants construct cognitive maps, that I read about in a wonderful paper by Mark Millonas and Dante Chialvo. Though i have already mentioned, I’ll still sum up the basic assumptions.


1. The individual agent (or ant) is memoryless.

2. There is no direct communication between the organisms.

3. There is no spatial diffusion of the pheromone deposited. It remains fixed at a point where it was deposited.

4. Each agent emits pheromone at a constant rate say \eta.

Stochastic Transition Probabilities:

Now the state of each agent can be described by a phase variable which contains its position r and orientation \theta. Since the response at any given time is dependent solely on the present and not the previous history, it would be sufficient to specify the transition probability from one location (r,\theta) to another place and orientation (r',\theta') an instant later. Thus the movement of each individual agent can be considered roughly to be a continuous markov process whose probabilities at each and every instance of time are decided by the pheromone concentration \sigma(x, t).

By using theoretical considerations, generalizations from observations in ant colonies the response function can be effectively summed up into a two parameter pheromone weight function.

\displaystyle W(\sigma) = (1 + \frac{\sigma}{1 + \delta\varsigma})

This weight function measures the relative probabilities in moving to a site r with the pheromone density \sigma(r).

Another parameter \beta may be considered. This parameter measures the degree of randomness by which an agent can follow a pheromone trail. For low values of \beta the pheromone concentration does not largely impact its choice but higher values do.

At this point we can define another factor \displaystyle\frac{1}{\varsigma}. This signifies the sensory capability. It describes the fact that the ants ability to sense pheromone decreases somewhat at higher concentrations. Something like a saturation scenario.

Pheromone Evolution: It is essential to describe how the pheromone evolves. According to an assumption already made, each agent emits pheromone at a constant rate \eta with no spatial diffusion. If the pheromone at a location is not replenished then it will gradually evaporate. The pheromonal field so formed does contain a memory of the past movements of the agents in space, however because of the evaporation process it does not have a very distant memory.

Analysis: Another important parameter is the regarding the number of ants present, the density of ants \rho_0. Thus using all these parameters we can define a single parameter, the average pheromonal field \displaystyle\sigma_0 = \frac{\rho_0 \eta}{\kappa}. Where \displaystyle \kappa is what i mentioned above, the rate of scent decay.

Further detailed analysis can be studied out here. With the above background it is just a matter of understanding.

[Evolution of distribution of ants : Source]

Click to Enlarge

Now after continuing with the mathematical analysis in the hyperlink above, we fix the values of the parameters.

Then a large number of ants are placed at random positions, the movement of each ant is determined by the probability P_{ik}.

Another assumption is that the pheromone density at each point at t=0 is zero. Each ant deposits pheromone at a decided rate \eta and also the pheromone evaporates at a fixed rate \kappa.

In the above beautiful picture we the evolution of a distribution of ants on a 32×32 lattice. A pattern begins to emerge as early as the 100th time step. Weak pheromonal paths are completely evaporated and we finally get a emergent ant distribution pattern as shown in the final image.

The Conclusion that Chialvo and Millonas note is that scent following of the very fundamental type described above (assumptions) is sufficient to produce an evolution (emergence) of complex pattern of organized flow of social insect traffic all by itself. Detailed conclusion can be read in this wonderful paper!

References and Suggested for Further Reading:

1. Cognitive Maps, click here >>

2. Remembrance of places past: A History of Theories of Space. click here >>

3. The Science of Self Organization and Adaptivity, Francis Heylighen, Free University of Brussels, Belgium. Click here >>

4.   The Hippocampus as a Cognitive Map, John O’ Keefe and Lynn Nadel, Clarendon Press, Oxford. To access the pdf version of this book click here >>

5. The Self-Organization in the Brain, Christoph von der Malsburg, Depts for Computer Science, Biology and Physics, University of Southern California.

5. How Swarms Build Cognitive Maps, Dante R. Chialvo and Mark M. Millonas, The Santa Fe Institute of Complexity. Click here >>

6. Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes, Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa.

Related Posts:

1. Swarm Paintings: Non-Human Art

2. The Working of a Bird Swarm

3. Adaptive Routing taking Cues from Stigmergy in Ants

Possibly Related:

Gödel, Escher, Bach: A Mental Space Odyssey

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General Background: Since childhood i have enjoyed sketching and painting, and very much at that! Sometimes i found myself copying an existing image or painting, making small changes here and there in it. Yes, the paintings came out beautiful (or so i think!), but one thing always made me unhappy, i thought that the creativity needed to make original stuff was missing at times (not always). It was not there all the time. It came in bursts and went away.

I agree with Leonel Moura (from his article) that creativity is basically produced due to different experiences and interactions. Absence or lack of which could make art lose novelty.

Talking of novelty, how about looking at art in nature? Richard Dawkins states that the difference between human art or design and the amazingly “ingenious” forms that we encounter in nature, is due tho the fact that Human art originates in the mind , while the natural designs result from natural selection. Which is very true. However it is another matter that natural selection and cultural selection, that will ultimately decide on the “popularity” of an art don’t function in the same way. Anyhow How can we remove the cultural bias or the human bias that we have in our art forms?
Answers in Artificial Life: Artificial life may be defined as “A field of study devoted to understanding life by attempting to derive general theories underlying biological phenomena, and recreating these dynamics in other physical media – such as computers – making them accessible to new kinds of experimental manipulation and testing. This scientific research links biology and computer science.”
Most of the A-Life simulations today can not be considered truly alive, as they still can not show some properties of truly alive systems and also that they have considerable human bias in design. However there are two views that have existed on the whole idea of Artificial Life and the extent it can go.
Weak A-Life is the idea that the “living process” can not be achieved beyond a chemical domain. Weak A-life researchers concentrate on simulating life processes with an underlying aim to understand the biological processes.
Strong A-Life is exactly the reverse. John Von Neumann once remarked life is a process which can be abstracted away from any particular medium. In recent times Ecologist Tom Ray declared that his computer simulation Tierra was not a simulation of life but a synthesis of life. In Tierra, computer programmes compete for CPU time and access to the main memory. These programs are also evolvable, can replicate, mutate and recombine.
Relating A-Life to Art: While researching on these ideas and the fact that these could be used to generate the art forms that i talked about in the first paragraph i came across a few papers by Swarm Intelligence Guru Vitorino Ramos and a couple of articles by Leonel Moura who had worked in collaboration with Dr Ramos on precisly this theme.
Swarm Paintings: Thus the idea as i had mentioned in my very first paragraph is to create an organism ideally with minimum pre-commitment to any representational art scheme or human style or taste. Sounds simple but is not so simple to implement!
There are a number of projects that have dealt with creating art, but these mostly have been evolutionary algorithms that learn from human behavior, and learn about human mannerisms and try to create art according to that. The idea here is to create art with a minimum of human intervention.
I came across a project by Dr Vitorino Ramos to which i had pointed out implicitly in the last paragraph. This project called ARTSBOT (ARTistic Swarm roBOTs) project. This project tries to address this issue of minimizing the human intervention in aesthetics , ethnicity, taste,style etc. In short their idea was to remove or to minimize the anthropocentric bias that pervades all our art forms. Obviously all this can have massive implications in our understanding of the biological processes also, however here we’ll talk of only art.
Two of the first paintings that emerged were:
(Source: Here)
(Source: Here)
These paintings were among the first swarm paintings by Leonel Moura and Vitorino Ramos. Now we see that these seem detached from a functional human pre-commitment. They don’t seem to represent any emotion or style or taste. However they still look very pleasant!
However the point to be understood and to be noted is that these are NOT random pictures created either by a programme or by a swarm of robots moving “randomly”. These pictures were generated by a horde of artificial ants and also by robots. They are not random, but they EMERGE from a process of pheromone deposition and evaporation as was simulated in this system from ants. Thus the result that we have above is a Colony Cognitive Map. The colony cognitive map is analogous to a cognitive map in the brain. I will cover the idea of a colony cognitive map in the next post.
A couple of more beautiful paintings can be seen below!
(Source for both images : Here>>)
Though i have already mentioned how these art forms emerge, i would still like to quote a paragraph from here:

The painting robots are artificial ‘organisms’ able to create their own art forms. They are equipped with environmental awareness and a small brain that runs algorithms based on simple rules. The resulting paintings are not predetermined, emerging rather from the combined effects of randomness and stigmergy, that is, indirect communication trough the environment.
Although the robots are autonomous they depend on a symbiotic relationship with human partners Not only in terms of starting and ending the procedure, but also and more deeply in the fact that the final configuration of each painting is the result of a certain gestalt fired in the brain of the human viewer. Therefore what we can consider ‘art’ here, is the result of multiple agents, some human, some artificial, immerged in a chaotic process where no one is in control and whose output is impossible to determine.
Hence, a ‘new kind of art’ represents the introduction of the complexity paradigm in the cultural and artistic realm.’

A Painting bot is something like in the picture shown below:

A swarm of robots at work:

The final art generated by the swarm of these robots is beautiful!

(Photo Credit for the three pictures above: Here>>



The work of Dr Ramos and Leonel Maura can be summed up as:
1. The human is only the “art-architect”, the “swarm” is the artist.
2. The “life” of Artificial Life shows characteristics like natural life itself namely Morphogenesis, ability to adapt to changing environments, evolution etc.
Leonel Moura’s wonderful article states that the final aim is to create an “Artificial Autopoietic System”, intriguing indeed and eagerly awaited!!
Such simulations could change the way we understand the biological processes and life.
Also i am now thinking how could music be produced based on the same or similar ideas. I wonder if Swarm music could be available. It would be most interesting and i can’t wait to listen to it already!
Have a look at this video by Leonel Moura, having some time lapse footage of robots painting.
1. Ant- Swarm Morphogenese By Leonel Moura
2. On the Implicit and on the Artificial – Morphogenesis and Emergent Aesthetics in Autonomous Collective Systems, in ARCHITOPIA Book, Art, Architecture and Science, INSTITUT D’ART CONTEMPORAIN, J.L. Maubant et al. (Eds.), pp. 25-57, Chapter 2, Vitorino Ramos.
3. A Strange Metamorphosis [From Kafka to Red Ant], Vitorino Ramos
Follow the following links to follow on more exciting papers and paintings.

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