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Pranav Mistry and SixthSense technology – Part 1 of 2
Pranav Mistry and SixthSense technology – Part 2 of 2
[...] … I do not really believe that we shall succeed in creating life artificially; but after having reached the moon and landed a spaceship or two on Mars, I realize that this disbelief of mine means very little. But computers are totally different from brains, whose function is not primarily to compute but to guide and balance an organism and help it to stay alive. It is for this reason that the first step of nature toward an intelligent mind was the creation of life, and I think that should we artificially create an intelligent mind, we would have to follow the same path. [...], Karl Popper – Popper, K. R. and Eccles, J. C. (1983), The Self and its Brain: An Argument for Interactionism, Routledge & Kegan Paul plc, London.
On Bilateral Monopolies: [...] Mary has the world’s only apple, worth fifty cents to her. John is the world’s only customer for the apple, worth a dollar to him. Mary has a monopoly on selling apples, John has a monopoly (technically, a monopsony, a buying monopoly) on buying apples. Economists describe such a situation as bilateral monopoly. What happens? Mary announces that her price is ninety cents, and if John will not pay it, she will eat the apple herself. If John believes her, he pays. Ninety cents for an apple he values at a dollar is not much of a deal but better than no apple. If, however, John announces that his maximum price is sixty cents and Mary believes him, the same logic holds. Mary accepts his price, and he gets most of the benefit from the trade. This is not a fixed-sum game. If John buys the apple from Mary, the sum of their gains is fifty cents, with the division determined by the price. If they fail to reach an agreement, the summed gain is zero. Each is using the threat of the zero outcome to try to force a fifty cent outcome as favorable to himself as possible. How successful each is depends in part on how convincingly he can commit himself, how well he can persuade the other that if he doesn’t get his way the deal will fall through. Every parent is familiar with a different example of the same game. A small child wants to get her way and will throw a tantrum if she doesn’t. The tantrum itself does her no good, since if she throws it you will refuse to do what she wants and send her to bed without dessert. But since the tantrum imposes substantial costs on you as well as on her, especially if it happens in the middle of your dinner party, it may be a sufficiently effective threat to get her at least part of what she wants. Prospective parents resolve never to give in to such threats and think they will succeed. They are wrong. You may have thought out the logic of bilateral monopoly better than your child, but she has hundreds of millions of years of evolution on her side, during which offspring who succeeded in making parents do what they want, and thus getting a larger share of parental resources devoted to them, were more likely to survive to pass on their genes to the next generation of offspring. Her commitment strategy is hardwired into her; if you call her bluff, you will frequently find that it is not a bluff. If you win more than half the games and only rarely end up with a bargaining breakdown and a tantrum, consider yourself lucky.
Herman Kahn, a writer who specialized in thinking and writing about unfashionable topics such as thermonuclear war, came up with yet another variant of the game: the Doomsday Machine. The idea was for the United States to bury lots of very dirty thermonuclear weapons under the Rocky Mountains, enough so that if they went off, their fallout would kill everyone on earth. The bombs would be attached to a fancy Geiger counter rigged to set them off if it sensed the fallout from a Russian nuclear attack. Once the Russians know we have a Doomsday Machine we are safe from attack and can safely scrap the rest of our nuclear arsenal. The idea provided the central plot device for the movie Doctor Strangelove. The Russians build a Doomsday Machine but imprudently postpone the announcement they are waiting for the premier’s birthday until just after an American Air Force officer has launched a unilateral nuclear attack on his own initiative. The mad scientist villain was presumably intended as a parody of Kahn. Kahn described a Doomsday Machine not because he thought we should build one but because he thought we already had. So had the Russians. Our nuclear arsenal and theirs were Doomsday Machines with human triggers. Once the Russians have attacked, retaliating does us no good just as, once you have finally told your daughter that she is going to bed, throwing a tantrum does her no good. But our military, knowing that the enemy has just killed most of their friends and relations, will retaliate anyway, and the knowledge that they will retaliate is a good reason for the Russians not to attack, just as the knowledge that your daughter will throw a tantrum is a good reason to let her stay up until the party is over. Fortunately, the real-world Doomsday Machines worked, with the result that neither was ever used.
For a final example, consider someone who is big, strong, and likes to get his own way. He adopts a policy of beating up anyone who does things he doesn’t like, such as paying attention to a girl he is dating or expressing insufficient deference to his views on baseball. He commits himself to that policy by persuading himself that only sissies let themselves get pushed around and that not doing what he wants counts as pushing him around. Beating someone up is costly; he might get hurt and he might end up in jail. But as long as everyone knows he is committed to that strategy, other people don’t cross him and he doesn’t have to beat them up. Think of the bully as a Doomsday Machine on an individual level. His strategy works as long as only one person is playing it. One day he sits down at a bar and starts discussing baseball with a stranger also big, strong, and committed to the same strategy. The stranger fails to show adequate deference to his opinions. When it is over, one of the two is lying dead on the floor, and the other is standing there with a broken beer bottle in his hand and a dazed expression on his face, wondering what happens next. The Doomsday Machine just went off. With only one bully the strategy is profitable: Other people do what you want and you never have to carry through on your commitment. With lots of bullies it is unprofitable: You frequently get into fights and soon end up either dead or in jail. As long as the number of bullies is low enough so that the gain of usually getting what you want is larger than the cost of occasionally having to pay for it, the strategy is profitable and the number of people adopting it increases. Equilibrium is reached when gain and loss just balance, making each of the alternative strategies, bully or pushover, equally attractive. The analysis becomes more complicated if we add additional strategies, but the logic of the situation remains the same.
This particular example of bilateral monopoly is relevant to one of the central disputes over criminal law in general and the death penalty in particular: Do penalties deter? One reason to think they might not is that the sort of crime I have just described, a barroom brawl ending in a killing more generally, a crime of passion seems to be an irrational act, one the perpetrator regrets as soon as it happens. How then can it be deterred by punishment? The economist’s answer is that the brawl was not chosen rationally but the strategy that led to it was. The higher the penalty for such acts, the less profitable the bully strategy. The result will be fewer bullies, fewer barroom brawls, and fewer “irrational” killings. How much deterrence that implies is an empirical question, but thinking through the logic of bilateral monopoly shows us why crimes of passion are not necessarily undeterrable. [...]
in Chapter 8, David D. Friedman, “Law’s Order: What Economics Has to Do With Law and Why it Matters“, Princeton University Press, Princeton, New Jersey, 2000.
Note – Further reading should include David D. Friedman’s “Price Theory and Hidden Order“. Also, a more extensive treatment could be found on “Game Theory and the Law“, by Douglas G. Baird, Robert H. Gertner and Randal C. Picker, Cambridge, Mass: Harvard University Press, 1994.
Video – ABB FlexPicker Robots (Source: http://www.botjunkie.com/ + http://www.abb.com/)
As well as, something at the lower pre-processing engineering level involving also Pattern Recognition, Image Analysis and Classification. Not for brownies, cookies or sausages. Since this is summer time, it relates with clams and bivalve in general. From the video, everything appears to be rather easy. But, they are not.
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.
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.
a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)
b) Swarm adaptive response over time, under severe dynamics, over the dynamic environment on the left (a).
Figs. – Check animated pictures in here. (a) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (b) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (left), describing a Dynamic Optimization (DO) Control Problem. Over each foraging step, the swarm self-regulates his population and keeps tracking the extrema (44 frames in total).
[] Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, in Artificial Life X – Proc. of the Tenth Int. Conf. on the Simulation and Synthesis of Living Systems, L.M. Rocha, L.S. Yaeger, M.A. Bedau, D. Floreano, R.L. Goldstone and A. Vespignani (Eds.), MIT Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June 3-7, 2006.
PDF paper.
Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. Termite colonies – for instance – build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS’s were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different sub-populations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.
Abraham, Ajith; Grosan, Crina; Ramos, Vitorino (Eds.), Stigmergic Optimization, Studies in Computational Intelligence (series), Vol. 31, Springer-Verlag, ISBN: 3-540-34689-9, 295 p., Hardcover, 2006.
TABLE OF CONTENTS (short /full) / CHAPTERS:
[1] Stigmergic Optimization: Foundations, Perspectives and Applications.
[2] Stigmergic Autonomous Navigation in Collective Robotics.
[3] A general Approach to Swarm Coordination using Circle Formation.
[4] Cooperative Particle Swarm Optimizers: a powerful and promising approach.
[5] Parallel Particle Swarm Optimization Algorithms with Adaptive
Simulated Annealing.
[6] Termite: a Swarm Intelligent Routing algorithm for Mobile
Wireless ad-hoc Networks.
[7] Linear Multiobjective Particle Swarm Optimization.
[8] Physically realistic Self-Assembly Simulation system.
[9] Gliders and Riders: A Particle Swarm selects for coherent Space-time Structures in Evolving Cellular Automata.
[10] Stigmergic Navigation for Multi-agent Teams in Complex Environments.
[11] Swarm Intelligence: Theoretical proof that Empirical techniques are Optimal.
[12] Stochastic Diffusion search: Partial function evaluation in Swarm Intelligence Dynamic Optimization.
With the current ongoing dramatic need of Africa to have contemporary maps (currently, Google promises to launch his first and exhaustive world-wide open-access digital cartography of the African continent very soon), back in 1999-2000 we envisioned a very simple idea into a research project (over my previous lab. – CVRM IST). Instead of producing new maps in the regular standard way, which are costly (specially for African continent countries) as well as time consuming (imagine the amount of money and time needed to cover the whole continent with high resolution aerial photos) the idea then was to hybridize trough an automatic procedure (with the help of Artificial Intelligence) new current data coming from satellites with old data coming from the computational analysis of images of old colonial maps. For instance, old roads segmented in old maps will help us finding the new ones coming from the current satellite images, as well as those that were lost. The same goes on for bridges, buildings, numbers, letters at the map, etc. However in order to do this, several preparatory steps were needed. One of those crucial steps was to obtain (segment – know to be one of the hardest procedures in image processing) the old roads, buildings, airports, at the old maps. Back in 1999-2000 while dealing with several tasks at this research project (AUTOCARTIS - Automatic Methods for Updating Cartographic Maps) I started to think of using evolutionary computation in order to tackle and surpass this precise problem, in what then later become one of the first usages of Genetic Algorithms in image analysis. The result could be checked below. Meanwhile, the experience gained with AUTOCARTIS was then later useful not only for digital old books (Visão Magazine, March 2002), as well as for helping us finding water in Mars (at the MARS EXPRESS European project – Expresso newspaper, May 2003) from which CVRM lab. was one of the European partners. Much often in life simple ideas (I owe it to Prof. Fernando Muge and Prof. Pedro Pina) are the best ones. This is particularly true in science.
Figure – One original image (left – Luanda, Angola map) and two segmentation examples, rivers and roads respectively obtained through the Genetic Algorithm proposed (low resolution images). [at the same time this precise Map of Luanda, was used by me along with the face of Einstein to benchmark several dynamic image adaptive perception versus memory experiments via ant-like artificial life systems over what I then entitled Digital Image Habitats]
[] Vitorino Ramos, Fernando Muge, Map Segmentation by Colour Cube Genetic K-Mean Clustering, Proc. of ECDL´2000 – 4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.
Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or non-stationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation. Some examples in Colour Maps are presented and overall results discussed.
(to obtain the respective PDF file follow link above or visit chemoton.org)
[] Crina Grosan, Ajith Abraham, Sang Yong Han, Vitorino Ramos, Stock Market Prediction using Multi Expression Programming, in ALEA´05, Workshop on Artificial Life and Evolutionary Algorithms at EPIA´05 – Proc. of the 12th Portuguese Conference on Artificial Intelligence, C. Bento, A. Cardoso and G. Dias (Eds.), IEEE Press, pp. 73-78, 2005.
The use of intelligent systems for stock market predictions has been widely established. In this paper we introduce a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artifcial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model, a difference boosting neural network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index as test data.
(to obtain the respective PDF file follow link above or visit chemoton.org)
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)
Springer book “Swarm Intelligence in Data Mining” (Studies in Computational Intelligence Series, Vol. 34) published in late 2006, is receiving a fair amount of attention, so much so, that early this year, Tokyo Denki University press (TDU) decided to negotiate with Springer the translation rights and copyrights in order to released it over their country in Japanese language. The Japanese version will now become shortly available, and I do hope – being one of the scientific editors – it will receive increasing attention as well in Japan, being it one of the most difficult and extraordinary real-world areas we could work nowadays among computer science. Multiple Sequence Alignment (MSA) within Bio-informatics is just one recent example, Financial Markets another. The amount of data – 100000 DVD’s every year -, CERN’s Large Hadron Collider (LHC) will collect is yet another. In order to transform data into information, and information into useful and critical knowledge, reliable and robust Data Mining is more than ever needed, on our daily life.
Meanwhile, I wonder how the Japanese cover design will be?! Starting with it’s own title, which appears to be pretty hard to translate. According to Yahoo BabelFish the Japanese characters (群れの知性) – derived among other language scripts from Kanji – correspond to the English sentence “Swarm Intelligence“. I wonder if this translation is correct or not, since “swarm” in itself, is kind of difficult to translate. Some meanings of it point out to a spaghetti dish, as well, which kind of makes some logic too. Moreover, the technical translation of it is also difficult. I guess the best person to handle the translation (at least from the list of colleagues around the world I know) is Claus Aranha. (IBA Lab., University of Tokyo). Not only he works in Japan for several years now, as well as some of his works focus this precise area.
SIDM book (Swarm Int. in Data Mining) focus on the hybridization of these two areas. As you may probably now, Data Mining (see also; Knowledge Extraction) refers to a collection of techniques – many of them classical – that envisions to tackle large amounts of data, in order to perform classification, clustering, sorting, feature selection, search, forecasting, decision, meaningful extraction, association rule discovery, sequential pattern discovery, etc. In recent years however (1985-2000), state of the art Artificial Intelligence such as Evolutionary Computation was also used, since some of his problems could be seen as – or properly translated to – optimization problems (namely, combinatorial). The same now happens with Swarm Intelligence, since some of it’s unique self-organizing distributed features (allowing direct applications over Grid Computing) seems ideal to tackle some of the most complex data mining problems we may face today.
For those willing for more, I will leave you with it’s contents (chapters), a foreword to this book by James Kennedy (one of the founding fathers of PSO - Particle Swarm Optimization, along with Russell C. Eberhart, and Yuhui Shi) which I vividly recommend (starting with the sentence “Science is a Swarm“!), as well as a more detailed description to it:
Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behavior of real ants, which are used to solve discrete optimization problems. Historically the notion of finding useful patterns in data has been given a variety of names including data mining, knowledge discovery, information extraction, etc. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. In order to achieve this, data mining uses computational techniques from statistics, machine learning and pattern recognition. Data mining and Swarm intelligence may seem that they do not have many properties in common. However, recent studies suggests that they can be used together for several real world data mining problems especially when other methods would be too expensive or difficult to implement. This book deals with the application of swarm intelligence methodologies in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapters giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
The eleven chapters are organized as follows. In Chapter 1, Grosan et al. present the biological motivation and some of the theoretical concepts of swarm intelligence with an emphasis on particle swarm optimization and ant colony optimization algorithms. The basic data mining terminologies are explained and linked with some of the past and ongoing works using swarm intelligence techniques. Martens et al. in Chapter 2 introduce a new algorithm for classification, named AntMiner+, based on an artificial ant system with inherent selforganizing capabilities. AntMiner+ differs from the previously proposed AntMiner classification technique in three aspects. Firstly, AntMiner+ uses a MAX-MIN ant system which is an improved version of the originally proposed ant system, yielding better performing classifiers. Secondly, the complexity of the environment in which the ants operate has substantially decreased. Finally, AntMiner+ leads to fewer and better performing rules. In Chapter 3, Jensen presents a feature selection mechanism based on ant colony optimization algorithm to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. The proposed method is applied to two very different challenging tasks, namely web classification and complex systems monitoring. Galea and Shen in the fourth chapter present an ant colony optimization approach for the induction of fuzzy rules. Several ant colony optimization algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific class. The final outcome is a fuzzy rulebase that has been evolved so that individual rules complement each other during the classification process. In the fifth chapter Tsang and Kwong present an ant colony based clustering model for intrusion detection. The proposed model improves existing ant-based clustering algorithms by incorporating some meta-heuristic principles. To further improve the clustering solution and alleviate the curse of dimensionality in network connection data, four unsupervised feature extraction algorithms are also studied and evaluated. Omran et al. in the sixth chapter present particle swarm optimization algorithms for pattern recognition and image processing problems. First a clustering method that is based on PSO is discussed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. Then PSO-based approaches that tackle the color image quantization and spectral unmixing problems are discussed.
In the seventh chapter Azzag et al. present a new model for data clustering, which is inspired from the self-assembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Authors have also introduced an incremental version of the artificial ants algorithm. Kazemian et al. in the eighth chapter presents a new swarm data clustering method based on Flowers Pollination by Artificial Bees (FPAB). FPAB does not require any parameter settings and any initial information such as the number of classes and the number of partitions on input data. Initially, in FPAB, bees move the pollens and pollinate them. Each pollen will grow in proportion to its garden flowers. Better growing will occur in better conditions. After some iterations, natural selection reduces the pollens and flowers and the gardens of the same type of flowers will be formed. The prototypes of each gardens are taken as the initial cluster centers for Fuzzy C Means algorithm which is used to reduce obvious misclassification errors. In the next stage, the prototypes of gardens are assumed as a single flower and FPAB is applied to them again. Palotai et al. in the ninth chapter propose an Alife architecture for news foraging. News foragers in the Internet were evolved by a simple internal selective algorithm: selection concerned the memory components, being finite in size and containing the list of most promising supplies. Foragers received reward for locating not yet found news and crawled by using value estimation. Foragers were allowed to multiply if they passed a given productivity threshold. A particular property of this community is that there is no direct interaction (here, communication) amongst foragers that allowed us to study compartmentalization, assumed to be important for scalability, in a very clear form. Veenhuis and Koppen in the tenth chapter introduce a data clustering algorithm based on species clustering. It combines methods of particle swarm optimization and flock algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, i.e., clusters. The data to be clustered are assigned to datoids which form a swarm on a two-dimensional plane. A datoid can be imagined as a bird carrying a piece of data on its back. While swarming, this swarm divides into sub-swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters. In the last chapter Yang et al. present a clustering ensemble model using ant colony algorithm with validity index and ART neural network. Clusterings are visually formed on the plane by ants walking, picking up or dropping down projected data objects with different probabilities. Adaptive Resonance Theory (ART) is employed to combine the clusterings produced by ant colonies with different moving speeds. We are very much grateful to the authors of this volume and to the reviewers for their tremendous service by critically reviewing the chapters. The editors would like to thank Dr. Thomas Ditzinger (Springer Engineering Inhouse Editor, Studies in Computational Intelligence Series), Professor Janusz Kacprzyk (Editor-in-Chief, Springer Studies in Computational Intelligence Series) and Ms. Heather King (Editorial Assistant, Springer Verlag, Heidelberg) for the editorial assistance and excellent cooperative collaboration to produce this important scientific work. We hope that the reader will share our excitement to present this volume on ‘Swarm Intelligence in Data Mining’ and will find it useful.
April, 2006
Ajith Abraham, Chung-Ang University, Seoul, Korea
Crina Grosan, Cluj-Napoca, Babes-Bolyai University, Romania
Vitorino Ramos, IST Technical University of Lisbon, Portugal
Fig. – (Above) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (Bellow) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (above), 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). [extra details + PDF]
[] Vitorino Ramos, Fernandes, C., Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria over Dynamic Environments, in CEC´07 – Congress on Evolutionary Computation, IEEE Press, USA, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 2007.
Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know De Jong test suite. Later, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.
(to obtain the respective PDF file follow link above or visit chemoton.org)
Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behavior occurs (check for more).
Possible laboratories around the world that follow this line of research include (Europe) Marco Dorigo’s Swarm-Bots Project in Brussels plus Swarm-Intelligent Systems Group, EPFL, in Lausanne, and CORO over Caltech, USA, among many others.
Video – Thousands of starlings birds gathering in flocks, flying in formations while emerging complex patterns on S.W. Scotland (more photos & video by/at Fresh Pics, 2007). Here for an artificial version with different purposes. They are not birds, instead an entirely different new animal.
[...] In contrast to negative feedback, positive feedback (PF) generally promotes changes in the system (the majority of self-organizing SO systems use them). The explosive growth of the human population provides a familiar example of the effect of positive feedback. The snowballing autocatalytic effect of PF 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 autocatalysis are all terms used to describe positive feedback [9]. 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 (1): 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 (check for further references on [7,9]). On social insect societies, PF 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 [49] 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 (2), or even just thinking of yawning, can trigger a yawn [9]. There is however one associated risk, generally if PF 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 PF 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 [9], the minimal behavioral rule could be “I nest where others nest, unless the area is overcrowded” (HEY !! here we go again to the El Farol Bar problem!). In this case both 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. [...]
in, V. Ramos et al., “Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes“.
(1) See also on this subject the seminal sociological work of Gabriel Tarde; Tarde, G., Les Lois de l’Imitation, Eds. du Seuil (2001), 1st Edition, Eds. Alcan, Paris, 1890.
(2) Similarly, Milgram et al (Milgram, Bickerman and Berkowitz, “Note on the Drawing Power of Crowds of Different Size”, Journal of Personality and Social Psychology, 13, 1969) found that if one person stood in a Manhattan street gazing at a sixth floor window, 20% of pedestrians looked up; if five people stood gazing, then 80% of people looked up.
(to obtain the respective PDF file follow this link or visit chemoton.org)
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