Ant Paintings Based on the Seed Foraging Behavior of P. barbatus

Abstract We describe our conversion of a simulation of the seed foraging behavior of the ant species P. barbatus to a generative art technique for creating ant paintings. We also show how the key parameters involved influence the results

Swarms on Continuous Data

While being it extremely important, many Exploratory Data Analysis (EDA) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD - Knowledge Discovery, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past sucesses in recently proposed Stigmergic Ant Systems a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstraded by other authors in different areas. KEYWORDS: Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Exploratory Data Analysis, Image Retrieval, Continuous Classification.Comment: 6 pages, 3 figures, at http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_45.htm

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Antspicbreeder: arte evolutiva produzida por formigas artificiais

Tese de mestrado em Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013Os insetos sociais são conhecidos pelas suas extraordinárias capacidades auto-organizativas e cooperativas, que se traduzem em comportamentos coletivos complexos, mesmo quando a nível individual existe um acesso limitado a informação. As formigas da espécie Temnothorax albipennis constroem um dos ninhos mais simples encontrados entre os insetos sociais. Os seus ninhos têm propriedades específicas no que respeita à sua dinâmica, dado que se baseiam num template que adapta o tamanho do ninho de acordo com o número de indivíduos pertencentes à colónia. O template não é implementado fisicamente, mas é uma força que orienta a construção. No presente modelo, é concretizada uma representação de um sistema computacional evolucionário interativo, semelhante ao Picbreeder original, mas que contempla um nível de opacidades sobre as imagens originais. Estes pontos têm como principal intuito ocultar sua visibilidade das imagens e promover a emergência de designs compostos pelos trilhos de cores das formigas. Este parâmetro é fixo ao longo da evolução de cada geração. Cada espécie de formigas é definida por uma cor central e um intervalo de tolerância centrado nessa cor. O efeito de template para cada uma das espécies é dado pelas zonas de cores da imagem que coincidam com os respetivos intervalos de tolerância cor. Estes templates podem ser parcialmente visíveis logo desde o início da evolução, no momento em que as formigas partem para a sua atividade transformadora. Neste sistema as colónias de formigas têm capacidades comportamentais dinâmicas que lhes permite adaptar o seu trabalho de acordo com as alterações morfológicas de um template.Social insects are known for their extraordinary capabilities of self-organizing and cooperative behaviors, which are reflected through complex collective behaviors, even when individuals are limited in accessing local information. Ants of Temnothorax albipennis specie build one of the simplest nests found among social insects. The nests have specific properties regarding to its dynamic, since they are based on a template the size of the nest is adjusted in accordance with the number of individuals of the colony. The template is not implemented physically, but it is a driving force behind the construction. In this model, we achieved a representation of an interactive evolutionary system, similar to the original Picbreeder but which includes a level of opacities above the original images with the purpose to hide its visibility and promote the emergence of designs composed by the color rails made by the involved artificial ants. This parameter is fixed throughout the evolution of each generation. A center color and a range of tolerance define each species of ants. The areas of the image colors that match the respective color tolerance give the effect of template. These templates can be partially visible from the very beginning of evolution, when ants start their transforming activity. In this system, ant’s colonies have a dynamic behavioral capability that allows them to adapt their work according to the morphological changes of the template

Simulators: evolutionary multi-agent system for object recognition in satellite image.

Miu, Hoi Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 170-182).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.4Chapter 1.2 --- Contributions --- p.5Chapter 1.3 --- Thesis Organization --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Multi-agent Systems --- p.8Chapter 2.1.1 --- Agent Architectures --- p.9Chapter 2.1.2 --- Multi-agent system frameworks --- p.12Chapter 2.1.3 --- The Advantages and Disadvantages of Multi-agent Systems --- p.15Chapter 2.2 --- Evolutionary Computation --- p.16Chapter 2.2.1 --- Genetic Algorithms --- p.17Chapter 2.2.2 --- Genetic Programming --- p.18Chapter 2.2.3 --- Evolutionary Strategies --- p.19Chapter 2.2.4 --- Evolutionary Programming --- p.19Chapter 2.3 --- Object Recognition --- p.19Chapter 2.3.1 --- Knowledge Representation --- p.20Chapter 2.3.2 --- Object Recognition Methods --- p.21Chapter 2.4 --- Evolutionary Multi-agent Systems --- p.25Chapter 2.4.1 --- Competitive Coevolutionary Agents --- p.26Chapter 2.4.2 --- Cooperative Coevolutionary Agents --- p.26Chapter 2.4.3 --- Cellular Automata --- p.27Chapter 2.4.4 --- Emergent Behavior --- p.28Chapter 2.4.5 --- Evolutionary Agents for Image processing and Pattern Recog- nition --- p.29Chapter 3 --- System Architecture and Agent Behaviors in SIMULATORS --- p.33Chapter 3.1 --- Organization of the System --- p.34Chapter 3.1.1 --- General Architecture of Object Recognition System --- p.34Chapter 3.1.2 --- Introduction to SIMULATORS --- p.35Chapter 3.1.3 --- System Flow of SIMULATORS --- p.37Chapter 3.1.4 --- Layered Digital Image Environment --- p.39Chapter 3.2 --- Architecture of Autonomous Agents --- p.41Chapter 3.2.1 --- Internal Object Model in an Agent --- p.41Chapter 3.2.2 --- Current State of an Agent --- p.46Chapter 3.2.3 --- Local Information Sensor --- p.46Chapter 3.2.4 --- Direction Density Vector --- p.47Chapter 3.3 --- Agent Behaviors --- p.48Chapter 3.3.1 --- Feature Target Marking --- p.49Chapter 3.3.2 --- Reproduction --- p.49Chapter 3.3.3 --- Diffusion --- p.52Chapter 3.3.4 --- Vanishing --- p.54Chapter 3.4 --- Clustering for Autonomous Agent Training --- p.56Chapter 3.4.1 --- Introduction --- p.56Chapter 3.4.2 --- Creating the Internal Object Model --- p.58Chapter 3.5 --- Summary --- p.63Chapter 4 --- Evolutionary Algorithms for Multi Agent System --- p.64Chapter 4.1 --- Evolutionary Agent Behaviors in SIMULATORS --- p.65Chapter 4.1.1 --- Overview --- p.65Chapter 4.1.2 --- Evolutionary Autonomous Agents --- p.66Chapter 4.1.3 --- Reproduction --- p.68Chapter 4.1.4 --- Fitness Function --- p.68Chapter 4.1.5 --- Direction Density Vector Propagation --- p.73Chapter 4.1.6 --- Mutation --- p.73Chapter 4.2 --- Agents Voting Mechanism --- p.74Chapter 4.2.1 --- Overview --- p.74Chapter 4.2.2 --- Voting for Cooperative Agents --- p.75Chapter 4.3 --- Evolutionary Multi Agent Object Recognition --- p.79Chapter 4.4 --- Summary --- p.81Chapter 5 --- Experimental Results and Applications --- p.82Chapter 5.1 --- Experiment Methodology --- p.82Chapter 5.1.1 --- Introduction to Fung Shui Woodland --- p.83Chapter 5.1.2 --- Testing Images --- p.83Chapter 5.1.3 --- Creating Internal Object Model --- p.85Chapter 5.1.4 --- Experiment Parameters --- p.86Chapter 5.2 --- Experimental Results of Fung Shui Woodland Recognition --- p.92Chapter 5.2.1 --- Experiment 1: artificial0l --- p.92Chapter 5.2.2 --- Experiment 2: artificial0l´ؤnoise --- p.92Chapter 5.2.3 --- Experiment 3: artificial02 --- p.93Chapter 5.2.4 --- Experiment 4: FungShui0l --- p.93Chapter 5.2.5 --- Experiment 5: FungShui0l´ؤnoise --- p.94Chapter 5.2.6 --- Experiments 6 to 11: FungShui02 to FungShui07 --- p.94Chapter 5.3 --- Discussion --- p.119Chapter 5.4 --- An Example of Eyes Detection --- p.124Chapter 5.4.1 --- Result of the Eyes Detection --- p.128Chapter 5.5 --- Summary --- p.132Chapter 6 --- Conclusion --- p.133Chapter 6.1 --- Summary --- p.133Chapter 6.2 --- Future Work --- p.136Chapter A --- The Figures in the Experiments --- p.13

Towards a swarm robotic approach for cooperative object recognition

Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios

Redes de troca de informação aplicadas a tarefas de optimização

Tese de Doutoramento em Gestão na especialidade em Gestão de Informação apresentada à Universidade AbertaEsta tese estuda a aplicação da teoria do equilíbrio estrutural das redes sociais a tarefas práticas. O estudo do equilíbrio estrutural é uma linha de investigação da sociologia matemática dedicada à análise dos processos de estabilização das relações dos indivíduos quando ocorre uma divergência na avaliação de uma mesma fonte de informação. Existe uma longa discussão sobre qual o modelo dinâmico que adequadamente descreve essa estabilização. É demonstrado que as regras originalmente observadas por Newcomb correspondem a uma maior simplicidade e coerência da dinâmica do equilíbrio estrutural. São depois desenvolvidas algumas aplicações deste modelo. Num primeiro exemplo é desenvolvido um algoritmo híbrido para optimização sem restrições. É depois transformado um algoritmo de partição de redes com sinais de acordo com a dinâmica de rede descrita. Ambos os algoritmos obtém resultados competitivos na resolução de problemas padrão, por comparação com algoritmos conhecidos e com propriedades semelhantes. Estes resultados mostram a utilidade da aplicação do modelo supracitado.This dissertation addresses the application of the theory of structural balance of social networks in solving practical problems. Structural Balance is a line of research in mathematical sociology dedicated to the analysis of stabilization of an individual‟s relationships with others when evaluations of a single piece of information diverge. There has been a long discussion on how to describe the stabilization dynamics. It is shown that the set of rules observed by Newcomb is a manifestation of a simple and coherent dynamic model of structural balance. Some applications of this model are then developed to problems represented as signed networks. First example is a hybrid algorithm for unrestricted optimization. Secondly, a signed networks partitioning algorithm is transformed to accommodate the defended dynamical model. Both algorithms are demonstrated to be perform competetively against related well-known algorithms in standard problems. These results show the utility of the application of the model