2016: Up now

2016 – Up now, an overall of 1567 citations among 74 works (including 3 books) on GOOGLE SCHOLAR (https://scholar.google.com/citations?user=gSyQ-g8AAAAJ&hl=en) [with an Hirsh h-index=19, and an average of 160.2 citations each for any work on my top five] + 900 citations among 57 works on the new RESEARCH GATE site (https://www.researchgate.net/profile/Vitorino_Ramos).

Refs.: Science, Artificial Intelligence, Swarm Intelligence, Data-Mining, Big-Data, Evolutionary Computation, Complex Systems, Image Analysis, Pattern Recognition, Data Analysis.

Problem Solving Foraging Ants, Spiking Neural Networks and Double Pheromones

Figure – Neural circuit controller of the virtual ant (page 3, fig. 2). [URL: http://arxiv.org/abs/1507.08467 ]

Intelligence and decision in foraging ants. Individual or Collective? Internal or External? What is the right balance between the two. Can one have internal intelligence without external intelligence? Can one take examples from nature to build in silico artificial lives that present us with interesting patterns? We explore a model of foraging ants in this paper that will be presented in early September in Exeter, UK, at UKCI 2015. (available on arXiv [PDF] and ResearchGate)

Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson, Vitorino Ramos; “A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones“, UKCI 2015 Computational Intelligence – University of Exeter, UK, September 2015.

Abstract: A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant is trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. In this case the introduction of a double pheromone mechanism (positive and negative reinforcement feedback) yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony. The presented model is an important pedagogical tool as it is also an easy to use library that allows access to the spiking neural network paradigm from inside a Netlogo—a language used mostly in agent based modelling and experimentation with complex systems.

References:

[1] C. G. Langton, “Studying artificial life with cellular automata,” Physica D: Nonlinear Phenomena, vol. 22, no. 1–3, pp. 120 – 149, 1986, proceedings of the Fifth Annual International Conference. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/016727898690237X
[2] A. Abraham and V. Ramos, “Web usage mining using artificial ant colony clustering and linear genetic programming,” in Proceedings of the Congress on Evolutionary Computation. Australia: IEEE Press, 2003, pp. 1384–1391.
[3] V. Ramos, F. Muge, and P. Pina, “Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies,” Hybrid Intelligent Systems, vol. 87, 2002.
[4] V. Ramos and J. J. Merelo, “Self-organized stigmergic document maps: Environment as a mechanism for context learning,” in Proceddings of the AEB, Merida, Spain, February 2002. ´
[5] D. Sousa-Rodrigues and V. Ramos, “Traversing news with ant colony optimisation and negative pheromones,” in European Conference in Complex Systems, Lucca, Italy, Sep 2014.
[6] E. Bonabeau, G. Theraulaz, and M. Dorigo, Swarm Intelligence: From Natural to Artificial Systems, 1st ed., ser. Santa Fe Insitute Studies In The Sciences of Complexity. 198 Madison Avenue, New York: Oxford University Press, USA, Sep. 1999.
[7] M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” Universite Libre de Bruxelles, Tech. Rep. TR/IRIDIA/1996-5, ´ 1996.
[8] M. Dorigo, G. Di Caro, and L. M. Gambardella, “Ant algorithms for discrete optimization,” Artif. Life, vol. 5, no. 2, pp. 137– 172, Apr. 1999. [Online]. Available: http://dx.doi.org/10.1162/ 106454699568728
[9] L. M. Gambardella and M. Dorigo, “Ant-q: A reinforcement learning approach to the travelling salesman problem,” in Proceedings of the ML-95, Twelfth Intern. Conf. on Machine Learning, M. Kaufman, Ed., 1995, pp. 252–260.
[10] A. Gupta, V. Nagarajan, and R. Ravi, “Approximation algorithms for optimal decision trees and adaptive tsp problems,” in Proceedings of the 37th international colloquium conference on Automata, languages and programming, ser. ICALP’10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 690–701. [Online]. Available: http://dl.acm.org/citation.cfm?id=1880918.1880993
[11] V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Second order ˜ swarm intelligence,” in HAIS’13. 8th International Conference on Hybrid Artificial Intelligence Systems, ser. Lecture Notes in Computer Science, J.-S. Pan, M. Polycarpou, M. Wozniak, A. Carvalho, ´ H. Quintian, and E. Corchado, Eds. Salamanca, Spain: Springer ´ Berlin Heidelberg, Sep 2013, vol. 8073, pp. 411–420.
[12] W. Maass and C. M. Bishop, Pulsed Neural Networks. Cambridge, Massachusetts: MIT Press, 1998.
[13] E. M. Izhikevich and E. M. Izhikevich, “Simple model of spiking neurons.” IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 14, no. 6, pp. 1569–72, 2003. [Online]. Available: http://www.ncbi.nlm.nih. gov/pubmed/18244602
[14] C. Liu and J. Shapiro, “Implementing classical conditioning with spiking neurons,” in Artificial Neural Networks ICANN 2007, ser. Lecture Notes in Computer Science, J. de S, L. Alexandre, W. Duch, and D. Mandic, Eds. Springer Berlin Heidelberg, 2007, vol. 4668, pp. 400–410. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-74690-4 41
[15] J. Haenicke, E. Pamir, and M. P. Nawrot, “A spiking neuronal network model of fast associative learning in the honeybee,” Frontiers in Computational Neuroscience, no. 149, 2012. [Online]. Available: http://www.frontiersin.org/computational neuroscience/10.3389/conf.fncom.2012.55.00149/full
[16] L. I. Helgadottir, J. Haenicke, T. Landgraf, R. Rojas, and M. P. Nawrot, “Conditioned behavior in a robot controlled by a spiking neural network,” in International IEEE/EMBS Conference on Neural Engineering, NER, 2013, pp. 891–894.
[17] A. Cyr and M. Boukadoum, “Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks,” pp. 257–272, 2012.
[18] X. Wang, Z. G. Hou, F. Lv, M. Tan, and Y. Wang, “Mobile robots’ modular navigation controller using spiking neural networks,” Neurocomputing, vol. 134, pp. 230–238, 2014.
[19] C. Hausler, M. P. Nawrot, and M. Schmuker, “A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee,” in 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011, 2011, pp. 198–202.
[20] U. Wilensky, “Netlogo,” Evanston IL, USA, 1999. [Online]. Available: http://ccl.northwestern.edu/netlogo/
[21] C. Jimenez-Romero and J. Johnson, “Accepted abstract: Simulation of agents and robots controlled by spiking neural networks using netlogo,” in International Conference on Brain Engineering and Neuro-computing, Mykonos, Greece, Oct 2015.
[22] W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge: Cambridge University Press, 2002.
[23] J. v. H. W Gerstner, R Kempter and H. Wagner, “A neuronal learning rule for sub-millisecond temporal coding,” Nature, vol. 386, pp. 76–78, 1996.
[24] I. P. Pavlov, “Conditioned reflexes: An investigation of the activity of the cerebral cortex,” New York, 1927.
[25] E. J. H. Robinson, D. E. Jackson, M. Holcombe, and F. L. W. Ratnieks, “Insect communication: ‘no entry’ signal in ant foraging,” Nature, vol. 438, no. 7067, pp. 442–442, 11 2005. [Online]. Available: http://dx.doi.org/10.1038/438442a
[26] E. J. Robinson, D. Jackson, M. Holcombe, and F. L. Ratnieks, “No entry signal in ant foraging (hymenoptera: Formicidae): new insights from an agent-based model,” Myrmecological News, vol. 10, no. 120, 2007.
[27] D. Sousa-Rodrigues, J. Louçã, and V. Ramos, “From standard ˜ to second-order swarm intelligence phase-space maps,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, Sep 2011.
[28] V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Spatio-temporal ˜ dynamics on co-evolved stigmergy,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, 9 2011.
[29] S. Tisue and U. Wilensky, “Netlogo: A simple environment for modeling complexity,” in International conference on complex systems. Boston, MA, 2004, pp. 16–21.

Traversing News with Second Order Swarm Intelligence

Figure – Two simplicies a and b connected by the 2-dimensional face, the triangle {1;2;3}. In the analysis of the time-line of The Guardian newspaper (link) the system used feature vectors based on frequency of words and them computed similarity between documents based on those feature vectors. This is a purely statistical approach that requires great computational power and that is difficult for problems that have large feature vectors and many documents. Feature vectors with 100,000 or more items are common and computing similarities between these documents becomes cumbersome. Instead of computing distance (or similarity) matrices between documents from feature vectors, the present approach explores the possibility of inferring the distance between documents from the Q-analysis description. Q-analysis is a very natural notion of connectivity between the simplicies of the structure and in the relation studied, documents are connected to each other through shared sets of tags entered by the journalists. Also in this framework, eccentricity is defined as a measure of the relatedness of one simplex in relation to another [7].

David M.S. Rodrigues and Vitorino Ramos, “Traversing News with Ant Colony Optimisation and Negative Pheromones” [PDF], accepted as preprint for oral presentation at the European Conference on Complex Systems – ECCS‘14 in Lucca, Sept. 22-26, 2014, Italy.

Abstract: The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system. Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora [1]. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data. A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed [2, 3] that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants [4].

In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q-analysis description [5, 6, 7] of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents. The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal improves the quality of the results. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence. This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system.

Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Hamiltonian cycles, Text Mining, Textual Corpora, Information Retrieval, Knowledge Discovery, Sentiment Analysis, Q-Analysis, Data Mining, Journalism, The Guardian.

References:

[1] Alexander Strehl, Joydeep Ghosh, and Raymond Mooney. Impact of similarity measures on web-page clustering.  In Workshop on Artifcial Intelligence for Web Search (AAAI 2000), pages 58-64, 2000.
[2] David M. S. Rodrigues, Jorge Louçã, and Vitorino Ramos. From standard to second-order Swarm Intelligence  phase-space maps. In Stefan Thurner, editor, 8th European Conference on Complex Systems, Vienna, Austria,  9 2011.
[3] Vitorino Ramos, David M. S. Rodrigues, and Jorge Louçã. Second order Swarm Intelligence. In Jeng-Shyang  Pan, Marios M. Polycarpou, Micha l Wozniak, André C.P.L.F. Carvalho, Hector Quintian, and Emilio Corchado,  editors, HAIS’13. 8th International Conference on Hybrid Artificial Intelligence Systems, volume 8073 of Lecture  Notes in Computer Science, pages 411-420. Springer Berlin Heidelberg, Salamanca, Spain, 9 2013.
[4] Elva J.H. Robinson, Duncan Jackson, Mike Holcombe, and Francis L.W. Ratnieks. No entry signal in ant  foraging (hymenoptera: Formicidae): new insights from an agent-based model. Myrmecological News, 10(120), 2007.
[5] Ronald Harry Atkin. Mathematical Structure in Human Affairs. Heinemann Educational Publishers, 48 Charles  Street, London, 1 edition, 1974.
[6] J. H. Johnson. A survey of Q-analysis, part 1: The past and present. In Proceedings of the Seminar on Q-analysis  and the Social Sciences, Universty of Leeds, 9 1983.
[7] David M. S. Rodrigues. Identifying news clusters using Q-analysis and modularity. In Albert Diaz-Guilera,  Alex Arenas, and Alvaro Corral, editors, Proceedings of the European Conference on Complex Systems 2013, Barcelona, 9 2013.

Second Order Swarm Intelligence

In order to solve hard combinatorial optimization problems (e.g. optimally scheduling students and teachers along a week plan on several different classes and classrooms), one way is to computationally mimic how ants forage the vicinity of their habitats searching for food. On a myriad of endless possibilities to find the optimal route (minimizing the travel distance), ants, collectively emerge the solution by using stigmergic signal traces, or pheromones, which also dynamically change under evaporation.

Current algorithms, however, make only use of a positive feedback type of pheromone along their search, that is, if they collectively visit a good low-distance route (a minimal pseudo-solution to the problem) they tend to reinforce that signal, for their colleagues. Nothing wrong with that, on the contrary, but no one knows however if a lower-distance alternative route is there also, just at the corner. On his global search endeavour, like a snowballing effect, positive feedbacks tend up to give credit to the exploitation of solutions but not on the – also useful – exploration side. The upcoming potential solutions can thus get crystallized, and freeze, while a small change on some parts of the whole route, could on the other-hand successfully increase the global result.

Figure – Influence of negative pheromone on kroA100.tsp problem (fig.1 – page 6) (values on lines represent 1-ALPHA). A typical standard ACS (Ant Colony System) is represented here by the line with value 0.0, while better results could be found by our approach, when using positive feedbacks (0.95) along with negative feedbacks (0.05). Not only we obtain better results, as we found them earlier.

There is, however, an advantage when a second type of pheromone (a negative feedback one) co-evolves with the first type. And we decided to research for his impact. What we found out, is that by using a second type of global feedback, we can indeed increase a faster search while achieving better results. In a way, it’s like using two different types of evaporative traffic lights, in green and red, co-evolving together. And as a conclusion, we should indeed use a negative no-entry signal pheromone. In small amounts (0.05), but use it. Not only this prevents the whole system to freeze on some solutions, to soon, as it enhances a better compromise on the search space of potential routes. The pre-print article is available here at arXiv. Follows the abstract and keywords:

Vitorino Ramos, David M. S. Rodrigues, Jorge Louçã, “Second Order Swarm Intelligence” [PDF], in Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, Springer-Verlag, Volume 8073, pp. 411-420, 2013.

Abstract: An artificial Ant Colony System (ACS) algorithm to solve general purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem‘s (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP complete combinatorial Optimization Problems, running on symmetrical TSPs. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Grüter [28] where “No entry” signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.

Keywords: Self-Organization, Stigmergy, Co-Evolution, Swarm Intelligence, Dynamic Optimization, Foraging, Cooperative Learning, Combinatorial Optimization problems, Symmetrical Travelling Salesman Problems (TSP).

Not in chaos, at the λ=0.273 vicinity of it

Photo – “O caos é uma ordem por decifrar” (Portuguese), that is… “Chaos is an order yet to be deciphered“, a quote from the Nobel Prize in Literature (1998) José Saramago [Lisbon, V. Ramos, 2013].

In 1990 (*), on one of his now famous works, Christopher Langton (link) decided to ask an important question. In order for computation to emerge spontaneously and become an important factor in the dynamics of a system, the material substrate must support the primitive functions required for computation: the transmission, storage, and modification of information. He then asked: Under what conditions might we expect physical systems to support such computational primitives?

Naturally, the question is difficult to address directly. Instead, he decided to reformulate the question in the context of a class of formal abstractions of physical systems: cellular automata (CAs). First, he introduce cellular automata and a simple scheme for parametrising (lambda parameter, λ) the space of all possible CA rules. Then he applied this parametrisation scheme to the space of possible one-dimensional CAs in a qualitative survey of the different dynamical regimes existing in CA rule space and their relationship to one another.

By presenting a quantitative picture of these structural relationships, using data from an extensive survey of two-dimensional CAs, he finally review the observed relationships among dynamical regimes, discussing their implications for the more general question raised above.  Langton found out that for a 2-state, 1-r neighbourhood, 1D cellular automata the optimal λ value is close to 0.5. For a 2-state, Moore neighbourhood, 2D cellular automata, like Conway’s Life, the λ value is then 0.273.

We then find that by selecting an appropriate parametrisation of the space of CAs, one observes a phase transition between highly ordered and highly disordered dynamics, analogous to the phase transition between the solid and fluid states of matter. Furthermore, Langton observed that CAs exhibiting the most complex behaviour – both qualitatively and quantitatively- are found generically in the vicinity of this phase transition. Most importantly, he observed that CAs in the transition region have the greatest potential for the support of information storage, transmission, and modification, and therefore for the emergence of computation. He concludes:

(…) These observations suggest that  there is  a fundamental connection between phase transitions and computation, leading to the following hypothesis concerning the emergence of computation in  physical systems: Computation may emerge spontaneously and come to dominate the dynamics of physical systems when those systems are at or near a transition between their solid and fluid phases, especially in the vicinity of a second-order or “critical” transition. (…)

Moreover, we observe surprising similarities between the behaviours of computations and systems near phase transitions, finding analogs of computational complexity classes and the halting problem (Turing) within the phenomenology of phase transitions. Langton, concludes that there is a fundamental connection between computation and phase transitions, especially second-order or “critical” transitions, discussing some of the implications for our understanding of nature if such a connection is borne out.

The full paper (*), Christopher G. Langton. “Computation at the edge of chaos”. Physica D, 42, 1990, is available online, here [PDF].

Of higher potentialities than itself

“There is thus this completely decisive property of complexity, that there exists a critical size below which the process of synthesis is degenerative, but above which the phenomenon of synthesis, if properly arranged, can become explosive, in other words, where syntheses of automata can proceed in such a manner that each automaton will produce other automata which are more complex and of higher potentialities than itself“. ~ John von Neumann, in his 1949 University of Illinois lectures on the Theory and Organization of Complicated Automata [J. von Neumann, Theory of self-reproducing automata, 1949 Univ. of Illinois Lectures on the Theory and Organization of Complicated Automata, ed. A.W. Burks (University of Illinois Press, Urbana, IL, 1966).].

Real, unreal, surreal

“I don’t do drugs. I am drugs” ~ Salvador Dalí.

The photo, which dates from 1969, depicts the 65-year-old Catalan surrealist Salvador Dalí emerging from a Paris subway station led by his trusty giant anteater. Surrealism‘s aim was to “resolve the previously contradictory conditions of dream and reality.” Artists painted unnerving, illogical scenes with photographic precision, created strange creatures from everyday objects and developed painting techniques that allowed the unconscious to express itself. [from Wikipedia, link above].

Time, not distance. Or distance as time.

Images – Portugal (1A – top left, original input satellite image below), geodesically stretched by one of my Mathematical Morphology algorithms, in order to represent real travel times from each of the 18 regional districts in Portugal, to the rest of the territory.  From the 18, three capital districts are represented here. As departing from Lisbon (1B – top right), from Faro (1C – South of Portugal, bottom left), and from Bragança (1D – North-East region, bottom right). [World Exposition, Lisbon, Territory pavilion, 1998].

Recently one of my colleagues who knows I love maps, pointed me to an old TV show “Câmara Clara“, a cultural TV show by RTP2, at one of the main public Portuguese TV stations. Main reason for my interest was his current theme: Maps. My second reason was their guests: Joaquim Ferreira do Amaral (an ex-Minister with a passion for maps) and Manuel Lima, which wonderful work on information visualization I know for a long time (on one of my past posts I referred to one of his ongoing working sites: visualcomplexity).

 

For my complete and positive surprise, their interview ended with some new examples, being one of my old works referred (from 57m 12s up to 60m 26s on http://camaraclara.rtp.pt/#/arquivo/131 ). It’s a long story on how I ended doing these kind of maps. Part of it, it’s here. During 1998, the World Exposition was in Portugal, and I got invited to present a set of 18 different maps from the Portuguese territory. So I decided to geodesically stretch the travel distances from any of the 18 different capital districts, to the rest of the territory, in order to represent travel Time not Distance, or Distance as time. For that,  I have coded new algorithms based on Mathematical Morphology (MM), taking in account every road (from main roads to regional, check some images below), from which I applied different MM operators.

Unfortunately, many of those maps are now lost. I did tried hard to find them from my old digital archives, but only found those above, which represent the departure from Lisbon (the Capital), Faro and Bragança. So, if by any reason you happen to have some photos from the 1998’s World Exposition in Lisbon, inside the Territory pavilion, I would love to receive them.

Video (LINK) – “Câmara Clara” TV show by journalist Paula Moura Pinheiro dedicated to maps (nº 131), at one of the main public Portuguese TV stations (RTP2), broadcasted on May 3 2009, in Portuguese.

A sketchy summary of this TV program went on something like this (the poor translation is mine): At the year Google promises to launch his first and exhaustive world-wide open-access digital cartography of the African continent, Joaquim Ferreira do Amaral, passioned by the Portuguese World Discover History and collector of historical maps, joins as guest with Manuel Lima, the Portuguese information designer that recently Creativity magazine has considered one of the top bright minds along with Google and Amazon founders, debating the importance of “navigating” reality with a map. From the Portuguese cartographic history, know to be the best in the XV and XVI centuries, up to the actual state-of-the-art in this area, from which Manuel Lima is considered to be one of the top researchers at global scale.

Signs, Traces and Cues

Photo – Signal traces, September 2013, Vitorino Ramos.

[…] While pheromone reinforcement plays a role as system’s memory, evaporation allows the system to adapt and dynamically decide, without any type of centralized or hierarchical control […], below.

“[…] whereas signals tends to be conspicuous, since natural selection has shaped signals to be strong and effective displays, information transfer via cues is often more subtle and based on incidental stimuli in an organism’s social environment […]”, Seeley, T.D., “The Honey Bee Colony as a Super-Organism”, American Scientist, 77, pp.546-553, 1989.

[…] If an ant colony on his cyclic way from the nest to a food source (and back again), has only two possible branches around an obstacle, one bigger and the other smaller (the bridge experiment [7,52]), pheromone will accumulate – as times passes – on the shorter path, simple because any ant that sets out on that path will return sooner, passing the same points more frequently, and via that way, reinforcing the signal of that precise branch. Even if as we know, the pheromone evaporation rate is the same in both branches, the longer branch will faster vanish his pheromone, since there is not enough critical mass of individuals to keep it. On the other hand – in what appears to be a vastly pedagogic trick of Mother Nature – evaporation plays a critical role on the society. Without it, the final global decision or the phase transition will never happen. Moreover, without it, the whole colony can never adapt if the environment suddenly changes (e.g., the appearance of a third even shorter branch). While pheromone reinforcement plays a role as system’s memory, evaporation allows the system to adapt and dynamically decide, without any type of centralized or hierarchical control. […], in “Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes“, V. Ramos et al., available as pre-print on arXiV, 2005.

[…] There is some degree of communication among the ants, just enough to keep them from wandering of completely at random. By this minimal communication they can remind each other that they are not alone but are cooperating with team-mates. It takes a large number of ants, all reinforcing each other this way, to sustain any activity – such as trail building – for any length of time. Now my very hazy understanding of the operation of brain leads me to believe that something similar pertains to the firing of neurons… […] in, p. 316, Hofstadter, D.R., “Gödel, Escher, Bach: An Eternal Golden Braid“, New York: Basic Books, 1979).

[…] Since in Self-Organized (SO) systems their organization arises entirely from multiple interactions, it is of critical importance to question how organisms acquire and act upon information [9]. Basically through two forms: a) information gathered from one’s neighbours, and b) information gathered from work in progress, that is, stigmergy. In the case of animal groups, these internal interactions typically involve information transfers between individuals. Biologists have recently recognized that information can flow within groups via two distinct pathways – signals and cues. Signals are stimuli shaped by natural selection specifically to convey information, whereas cues are stimuli that convey information only incidentally [9]. The distinction between signals and cues is illustrated by the difference on ant and deer trails. The chemical trail deposited by ants as they return from a desirable food source is a signal. Over evolutionary time such trails have been moulded by natural selection for the purpose of sharing with nest mates information about the location of rich food sources. In contrast, the rutted trails made by deer walking through the woods is a cue, not shaped by natural selection for communication among deer but are a simple by-product of animals walking along the same path. SO systems are based on both, but whereas signals tends to be conspicuous, since natural selection has shaped signals to be strong and effective displays, information transfer via cues is often more subtle and based on incidental stimuli in an organism’s social environment [45] […], in “Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes“, V. Ramos et al., available as pre-print on arXiV, 2005.

Simulacra and Simulation

According to Baudrillard, Simulacra are copies that depict things that either had no reality to begin with, or that no longer have an original. While, Simulation is the imitation of the operation of a real-world process or system over time. “Simulacres et Simulation” is a 1981 philosophical treatise by Jean Baudrillard seeking to interrogate the relationship among reality, symbols, and society:

[…] Simulacra and Simulation is most known for its discussion of symbols, signs, and how they relate to contemporaneity (simultaneous existences). Baudrillard claims that our current society has replaced all reality and meaning with symbols and signs, and that human experience is of a simulation of reality. Moreover, these simulacra are not merely mediations of reality, nor even deceptive mediations of reality; they are not based in a reality nor do they hide a reality, they simply hide that anything like reality is relevant to our current understanding of our lives. The simulacra that Baudrillard refers to are the significations and symbolism of culture and media that construct perceived reality, the acquired understanding by which our lives and shared existence is and are rendered legible; Baudrillard believed that society has become so saturated with these simulacra and our lives so saturated with the constructs of society that all meaning was being rendered meaningless by being infinitely mutable. Baudrillard called this phenomenon the “precession of simulacra”. […] (from Wikipedia)

“Simulacra and Simulation” is definitely one of my best summer holiday readings I had this year. There are several connections to areas like Collective Intelligence and Perception, even Self-Organization as the dynamic and entangled use of symbols and signals, are recurrent on all these areas. Questions like the territory (cultural habitats) and metamorphose are also aborded. The book is an interesting source of new questions and thinking about our digital society, for people working on related areas such as Digital Media, Computer Simulation, Information Theory, Information and Entropy, Augmented Reality, Social Computation and related paradigms. I have read it in English for free [PDF] from a Georgetown Univ. link, here.

Hybrid Artificial Intelligent Systems

Figure – Hybrid Artificial Intelligent Systems new LNAI (Lecture Notes on Artificial Intelligence) series volume 8073, Springer-Verlag Book [original photo by my colleague David M.S. Rodrigues].

New work, new book. Last week one of our latest works come out published on Springer. Edited by Jeng-Shyang Pan, Marios M. Polycarpou, Emilio Corchado et al. “Hybrid Artificial Intelligent Systems” comprises a full set of new papers on this hybrid area on Intelligent Computing (check the full articles list at Springer). Our new paper “Second Order Swarm Intelligence” (pp. 411-420, Springer books link) was published on the Bio-inspired Models and Evolutionary Computation section.

The Hacker and the Ants

The Hacker and the Ants is a work of science fiction by Rudy Rucker published in 1994 by Avon Books. It was written while Rucker was working as a programmer at Autodesk, Inc., of Sausalito, California from 1988 to 1992. The main character is a transrealist interpretation of Rucker’s life in the 1970s (Rucker taught mathematics at the State University College at Geneseo, New York from 1972 to 1978. from Wikipedia). The plot follows:

(…) Jerzy Rugby is trying to create truly intelligent robots. While his actual life crumbles, Rugby toils in his virtual office, testing the robots online. Then, something goes wrong and zillions of computer virus ants invade the net. Rugby is the man wanted for the crime. He’s been set up to take a fall for a giant cyberconspiracy and he needs to figure out who — or what — is sabotaging the system in order to clear his name. Plunging deep into the virtual worlds of Antland of Fnoor to find some answers, Rugby confronts both electronic and all-too-real perils, facing death itself in a battle for his freedom. (…)

Samuel was an aLife(r)

Interesting how this Samuel Beckett (1906–1989) quote to his work is so close to the research on Artificial Life (aLife), as well as how Christopher Langton (link) approached the field, on his initial stages, fighting back and fourth with his Lambda parameter (“Life emerges at the Edge of Chaos“) back in the 80’s. According to Langton‘s findings, at the edge of several ordered states and the chaotic regime (lambda=0,273) the information passing on the system is maximal, thus ensuring life. Will not wait for Godot. Here:

“Beckett was intrigued by chess because of the way it combined the free play of imagination with a rigid set of rules, presenting what the editors of the Faber Companion to Samuel Beckett call a “paradox of freedom and restriction”. That is a very Beckettian notion: the idea that we are simultaneously free and unfree, capable of beauty yet doomed. Chess, especially in the endgame when the board’s opening symmetry has been wrecked and the courtiers eliminated, represents life reduced to essentials – to a struggle to survive.”(*)

(*) on Stephen Moss, “Samuel Beckett’s obsession with chess: how the game influenced his work“, The Guardian, 29 August 2013. [link]

Midsummer night’s swarm

Nocturnal moth trails – Fluttering wings leave lacy trails as moths beat their way to a floodlight on a rural Ontario lawn. The midsummer night’s exposure, held for 20 seconds, captured some of the hundreds of insects engaged in a nocturnal swarm. [Photo: Steve Irvine, National Geographic, 2013, link]

Surfaces and essences

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

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

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

Blindfold patterns

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

David and Goliath

During several years, Octavio Aburto thought of one photo. Now, he finally got it. The recently published photograph by Aburto, titled “David and Goliath” (it his in fact David Castro, one of his research science colleagues at the center of this stunning image) has been widely shared over the last few weeks. It was taken at Cabo Pulmo National Park (Mexico) and submitted to the National Geographic photo contest 2012. Here, he captures the sheer size of fish aggregations in perspective with a single human surrounded by abundant marine life. On a recent interview, he explains:

[…] … this “David and Goliath” image is speaking to the courtship behavior of one particular species of Jack fish. […] Many people say that a single image is worth a thousand words, but a single image can also represent thousands of data points and countless statistical analyses. One image, or a small series of images can tell a complicated story in a very simple way. […] The picture you see was taken November 1^st, 2012. But this picture has been in my mind for three years — I have been trying to capture this image ever since I saw the behavior of these fish and witnessed the incredible tornado that they form during courtship. So, I guess you could say this image took almost three years. […], in mission-blue.org , Dec. 2012.

Video – Behind the scenes of David and Goliath image. This photo was taken at Cabo Pulmo National Park and submitted to the National Geographic photo contest 2012. You can see more of his images from this place and about Mexican seas on Octavio‘s web link.

Evolution Emerging

Image – The frontispiece of William King Gregory’s two-volume Evolution Emerging. Gregory, 1951, Evolution Emerging: A Survey of Changing Patterns from Primeval Life to Man, vol. 2, p. 757; fig. 20.33; [courtesy of Mary DeJong, Mai Qaraman, and the American Museum of Natural History].

Real people (VII)

Photo – Oscar Niemeyer (1907-2012) photographed by Ludovic Lent for L’Express, France.

“First were the thick stone walls, the arches, then the domes and vaults – of the architect, searching out for wider spaces. Now it is concrete-reinforced that gives our imagination flight with its soaring spans and uncommon cantilevers. Concrete, to which architecture is integrated, through which it is able to discard the foregone conclusions of rationalism, with its monotony and repetitious solutions. A concern for beauty, a zest for fantasy, and an ever-present element of surprise bear witness that today’s architecture is not a minor craft bound to straight-edge rules, but an architecture imbued with technology: light, creative and unfettered, seeking out its architectural scene.” ~ Oscar Niemeyer, acceptance speech, Pritzker Architecture Prize (1988).

Build a tangled bank

Fig. – (book cover) “Emergence: The Connected Lives of Ants, Brains, Cities, and Software” (additional link) is a book written by media theorist Steven Berlin Johnson, published in 2001,  Scribner. New York, NY.

“The patterns are simple, but followed together, they make for a whole that is wiser than the sum of its parts. Go for a walk; cultivate hunches; write everything down, but keep your folders messy; embrace serendipity; make generative mistakes; take on multiple hobbies; frequent coffee houses and other liquid networks; follow the links; let others build on your ideas; borrow, recycle; reinvent. Build a tangled bank.” — Steven Johnson.