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Figure – Book cover of Toby Segaran’s, “Programming Collective Intelligence – Building Smart Web 2.0 Applications“, O’Reilly Media, 368 pp., August 2007.
{scopus online description} Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting data-sets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application.
{even if I don’t totally agree, here’s a “over-rated” description – specially on the scientific side, by someone “dwa” – link above} Programming Collective Intelligence is a new book from O’Reilly, which was written by Toby Segaran. The author graduated from MIT and is currently working at Metaweb Technologies. He develops ways to put large public data-sets into Freebase, a free online semantic database. You can find more information about him on his blog: http://blog.kiwitobes.com/. Web 2.0 cannot exist without Collective Intelligence. The “giants” use it everywhere, YouTube recommends similar movies, Last.fm knows what would you like to listen and Flickr which photos are your favorites etc. This technology empowers intelligent search, clustering, building price models and ranking on the web. I cannot imagine modern service without data analysis. That is the reason why it is worth to start read about it. There are many titles about collective intelligence but recently I have read two, this one and “Collective Intelligence in Action“. Both are very pragmatic, but the O’Reilly’s one is more focused on the merit of the CI. The code listings are much shorter (but examples are written in Python, so that was easy). In general these books comparison is like Java vs. Python. If you would like to build recommendation engine “in Action”/Java way, you would have to read whole book, attach extra jar-s and design dozens of classes. The rapid Python way requires reading only 15 pages and voila, you have got the first recommendations. It is awesome!
So how about rest of the book, there are still 319 pages! Further chapters say about: discovering groups, searching, ranking, optimization, document filtering, decision trees, price models or genetic algorithms. The book explains how to implement Simulated Annealing, k-Nearest Neighbors, Bayesian Classifier and many more. Take a look at the table of contents (here: http://oreilly.com/catalog/9780596529321/preview.html), it does not list all the algorithms but you can find more information there. Each chapter has about 20-30 pages. You do not have to read them all, you can choose the most important and still know what is going on. Every chapter contains minimum amount of theoretical introduction, for total beginners it might be not enough. I recommend this book for students who had statistics course (not only IT or computing science), this book will show you how to use your knowledge in practice _ there are many inspiring examples. For those who do not know Python - do not be afraid _ at the beginning you will find short introduction to language syntax. All listings are very short and well described by the author _ sometimes line by line. The book also contains necessary information about basic standard libraries responsible for xml processing or web pages downloading. If you would like to start learn about collective intelligence I would strongly recommend reading “Programming Collective Intelligence” first, then “Collective Intelligence in Action”. The first one shows how easy it is to implement basic algorithms, the second one would show you how to use existing open source projects related to machine learning.
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.
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)
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)
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)
Figure – Evolutionary lego cranes and bridges. Check here for a small animation as well as a long mpeg video.
These, I believe, will be the solution for the first floating Europe-Africa bridge preliminary project (linking the Portuguese-Spanish border between Faro and Huelva, ending near Tanger – Morocco, spanning the entire strait of Gibraltar), or for many of them coming in the near future, in case they need to robustly and adaptively join further long distances:
[...] Creating artificial life forms through evolutionary robotics faces a “chicken and egg” problem: Learning to control a complex body is dominated by inductive biases specific to its sensors and effectors, while building a body which is controllable is conditioned on the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has been constrained by the “reality gap” which implies that resultant objects are not buildable. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We demonstrate its functionality in several different evolved organisms [...]. in, Computer Evolution of Buildable Objects, Pablo Funes and Jordan Pollack, 1997.
[...] The type of rationality we assume in economics – perfect, logical, deductive rationality–is extremely useful in generating solutions to theoretical problems. But it demands much of human behavior – much more in fact than it can usually deliver. If we were to imagine the vast collection of decision problems economic agents might conceivably deal with as a sea or an ocean, with the easier problems on top and more complicated ones at increasing depth, then deductive rationality would describe human behavior accurately only within a few feet of the surface. For example, the game Tic-Tac-Toe is simple, and we can readily find a perfectly rational, minimax solution to it. But we do not find rational “solutions” at the depth of Checkers; and certainly not at the still modest depths of Chess and Go.
There are two reasons for perfect or deductive rationality to break down under complication. The obvious one is that beyond a certain complicatedness, our logical apparatus ceases to cope – our rationality is bounded. The other is that in interactive situations of complication, agents can not rely upon the other agents they are dealing with to behave under perfect rationality, and so they are forced to guess their behavior. This lands them in a world of subjective beliefs, and subjective beliefs about subjective beliefs. Objective, well-defined, shared assumptions then cease to apply. In turn, rational, deductive reasoning–deriving a conclusion by perfect logical processes from well-defined premises – itself cannot apply. The problem becomes ill-defined.
As economists, of course, we are well aware of this. The question is not whether perfect rationality works, but rather what to put in its place. How do we model bounded rationality in economics? Many ideas have been suggested in the small but growing literature on bounded rationality; but there is not yet much convergence among them. In the behavioral sciences this is not the case. Modern psychologists are in reasonable agreement that in situations that are complicated or ill-defined, humans use characteristic and predictable methods of reasoning. These methods are not deductive, but inductive. [...] The system that emerges under inductive reasoning will have connections both with evolution and complexity. [...]
in, Inductive Reasoning and Bounded Rationality (The El Farol Problem), by W. Brian Arthur, 1994.
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