京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19807号 / 人博第778号 / 新制||人||187(附属図書館) / 27||人博||778(吉田南総合図書館) / 32843 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 立木 秀樹, 准教授 櫻川 貴司, 教授 日置 尋久, 教授 阪上 雅昭 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

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We investigate an approach to apply Genetic Programming for the evolution of optimal escaping strategies of a team of caribou agents in the wolf-caribou predator prey problem (WCPPP) where the WCPPP is comprised of a team of caribou agents attempting to escape from a single yet superior (in terms of sensory abilities, raw speed, and maximum energy) wolf agent in a simulated twodimensional infinite toroidal world. We empirically verify our hypothesis that the incorporation of empathy in caribou agents significantly improves both the evolution efficiency of the escaping behavior and the effectiveness of such a behavior. This finding may be viewed as a verification of the survival value of empathy and the resulting compassionate behavior of the escaping caribou agents. Moreover, considering the fact that a single caribou cannot escape from the superior wolf, the ability of a team of empathic caribou agents to escape may also be viewed as an illustration of the emergent nature of a successful escaping behavior – in that the team-level properties are more than the mere sum of the properties of the individual entities. Within this context, we also present empirical results that verify the complex (nonlinear) nature of the relationship between the size of team of caribou agents and the efficiency of their escaping behavior. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

This research presents both theoretical foundation and numerical simulation work for design and analysis of a multi-agent dynamic system on the collective formation behavior patterns of grouped agents. A mass model with tunable control parameters is proposed. This model can realistically represent the aggregation pattern and the formation shape of multiple agents. Stability analysis is also provided to prove the stability of the second-order dynamic system. Several simulations will also be given according to the proposed model to show the aggregation patterns. The research on self-organizing characteristics of collective agent behaviors has a wide range of applications in nature and engineering. The formation such as a flock of birds, a school of fish, or a swarm of locusts, is the emergence of ordered state in which the moving agents can organize as formation. Design and control of the self-organizing dynamic system has implications on wireless general design of mobile sensor networks, sensor network data fusion, attitude alignment of satellite clusters and congestion control of communication networks. / Electrical and Computer Engineering

Yet, Kam Hon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 128-134). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese; appendixes includes Chinese.

A forma como as formigas se dispersam pelo espaço está relacionada tanto à processos coletivos de formação de trilhas preferenciais de forrageamento por meio de marcação feromonal quanto à fatores individuais como orientação por ângulos, distância e direção da colônia, o comportamento em bifurcações e memória individual. Entretanto a compreensão de como esses mecanismos se articulam na produção de uma resposta da colônia frente à uma tarefa espacial poucas vezes foi focado. Além disso, assim como no forrageamento, a forma como se dá a dispersão pelo espaço na exploração também é crítica. Sendo assim, a dispersão das formigas pelo espaço representa um ponto de balanço entre a exploração e o forrageamento, em que os fatores individuais e coletivos compõem conjuntamente uma resposta ao desafio espacial. No entanto, na medida em que o uso do espaço é frequentemente observado pelo ponto de vista da otimização do forrageamento, esse balanço é pouco explorado. O objetivo do trabalho foi avaliar como a dispersão espaço-temporal das formigas de uma colônia, produzida por mecanismos coletivos e individuais, reflete tanto o esforço exploratório quanto o de forrageamento. Para isso, foi criado um labirinto hexagonal duplo concêntrico e foram comparadas as dispersões das formigas em duas condições: com alimento numa posição específica, e em uma condição controle, em que o alimento era oferecido junto à colônia de forma que a dispersão fosse uma exploração desvinculada do forrageamento. Para a observação das formigas no labirinto, foram dispostas câmeras de vídeo sobre os corredores e foi desenvolvido um software de análise de vídeo para a contagem de formigas. Embora a dispersão decorrente do forrageamento e da exploração sejam indissociáveis, através da simulação computacional com modelos de referência em que predominam um dos casos foi possível avaliar o grau de contraste com dispersões observadas experimentalmente. Observamos que, de fato, os controles são mais próximos do modelo nulo de exploração e os experimentos com folhas do modelo de forrageamento. Embora o caminho preferencial observado tenha divergido do modelo, são mais próximos do modelo de forrageamento, mostrando a relação entre a forma da dispersão e os comportamentos subjacentes. Além disso, houve uma baixa convergência nos controles, ao passo que nos experimentos com folhas a formação de trilhas preferenciais apresentou graus variados de convergência. Na condição experimental a formação de trilhas preferenciais decorrente da marcação feromonal coletiva, em conjunto com o comportamento individual nas bifurcações assimétricas e o próprio desenho do labirinto favoreceram a separação entre as trilhas de ida e volta do alimento. Enquanto o caminho de ida permaneceu o mais provável dado o labirinto, o caminho de retorno foi otimizado, estabelencendo um caminho mínimo contralateral ao caminho de ida. Assim, a relação entre os mecanismos coletivos e individuais fornece um cenário apropriado para a análise do balanço entre o forrageamento e a exploração / The means by which ants disperse is given by collective processes of preferential foraging trail formation by means of pheromone marking as well as by individual factors such as orientation by features\' angles, colony distance and direction, choices at bifurcations and individual memory. However, how these mechanisms interact in producing a colony response given a spatial task has rarely been the focus of studies. In addition, as in the case of foraging, the spatial dispersal pattern for exploration is also critical. So, the ants dispersal in space represents a balance point between exploration and foraging, in which the individual and collective factors together compose a response to the spatial challenges. However, as the space occupation is frequently studied regarding foraging optimization, this balance is disregarded. The objective of this study is to understand how the spatio-temporal dispersal of ants in a maze, as produced by such collective and individual mechanisms, reflects both the exploratory and foraging efforts. With that aim, I created a double concentric hexagonal maze and compared the ants dispersal in two conditions: in the offering of food in a specific place, and in a control condition, food was given in the same place as the colony such that its dispersal is unrelated to foraging. In order to observe the ants behaviour, video cameras were set on top of each maze connection and a software was developed to count the passage of the ants. Although the dispersal due to foraging and from exploration are inseparable, using computer simulations of reference theoretical models of each case it was possible to contrast and measure the differences to the experimentally observed dispersals. We observed that indeed, the experimental group was closer to the foraging model while the control was closer to the null model of exploration. Although the observed preferential path was different from that of the foraging model, it was still closer, showing the relationship between the observed dispersal and the underlying behaviours. Moreover, there was a low convergence in the control group, while various degrees of convergence were observed in the experimental group. In the experimental group, the formation of preferential paths by means of collective pheremonal markings, together with individual preferences at asymmetric bifurcations and the design of the maze, favoured the separation between outbound and inbound paths. Whilst the outbound path remained the most probable given the geometry of the maze, the inbound path was optimised, establishing a minimal path on the opposite side of the outbound path. Thus, the relationship between the collective and individual mechanisms provide a more than appropriate scenario for the analysis of the balance between foraging and exploration

O aprendizado de máquina consiste em conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou em outras palavras, aprender com dados. Duas de suas principais categorias são o aprendizado não-supervisionado e o semissupervisionado, que respectivamente consistem em inferir padrões em bases cujos dados não têm rótulo (classe) e classificar dados em bases parcialmente rotuladas. Embora muito estudado, trata-se de um campo repleto de desafios e com muitos tópicos abertos. Sistemas dinâmicos coletivos, por sua vez, são sistemas constituídos por muitos indivíduos, cada qual um sistema dinâmico por si só, de modo que todos eles agem coletivamente, ou seja, a ação de cada indivíduo é influenciada pela ação dos vizinhos. Uma característica notável desses sistemas é que padrões globais podem surgir espontaneamente das interações locais entre os indivíduos, fenômeno conhecido como emergência. Os desafios intrínsecos e a relevância do tema vêm motivando sua pesquisa em diversos ramos da ciência e da engenharia. Este trabalho de doutorado consiste no desenvolvimento e análise de modelos dinâmicos coletivos para o aprendizado de máquina, especificamente suas categorias não-supervisionada e semissupervisionada. As tarefas de segmentação de imagens e de detecção de comunidades em redes, que de certo modo podem ser entendidas como tarefas do aprendizado de máquina, são também abordadas. Em especial, desenvolvem-se modelos nos quais a movimentação dos objetos é determinada pela localização e velocidade de seus vizinhos. O sistema dinâmico assim modelado é então conduzido a um estado cujo padrão formado por seus indivíduos realça padrões subjacentes do conjunto de dados. Devido ao seu caráter auto-organizável, os modelos aqui desenvolvidos são robustos e as informações geradas durante o processo (valores das variáveis do sistema) são ricas e podem, por exemplo, revelar características para realizar soft labeling e determinar classes sobrepostas. / Machine learning consists of concepts and techniques that enable computers to improve their performance with experience, i.e., learn from data. Unsupervised and semi-supervised learning are important categories of machine learning, which respectively consists of inferring patterns in datasets whose data have no label (class) and classifying data in partially-labeled datasets. Although intensively studied, machine learning is still a field full of challenges and with many open topics. Collective dynamical systems, in turn, are systems made of a large group of individuals, each one a dynamical system by itself, such that all of them behave collectively, i.e., the action of each individual is influenced by the action of its neighbors. A remarkable feature of those systems is that global patterns may spontaneously emerge from the local interactions among individuals, a phenomenon known as emergence. Their relevance and intrinsic challenges motivate research in various branches of science and engineering. In this doctorate research, we develop and analyze collective dynamical models for their usage in machine-learning tasks, specifically unsupervised and semi-supervised ones. Image segmentation and network community detection are also addressed, as they are related to machine learning as well. In particular, we propose to work on models in which the objects motion is determined by the location and velocity of their neighbors. By doing so, the dynamical system reaches a configuration in which the patterns developed by the set of individuals highlight underlying patterns of the dataset. Due to their self-organizing nature, it is also expected that the models can be robust and the information generated during the process (values of the system variables) can be rich and reveal, for example, features to perform soft labeling and determine overlapping classes.

Despite extensive research in conflict, relatively little is known about how psychological processes evolve over time in response to a dispute. The present research examines how cognitive and affective processes react to cooperative, competitive, or mixed cooperative-competitive interactions. Experimental predictions were derived from a model of two-actor interaction (Liebovitch, Naudot, Vallacher, Nowak, Bu--Wrzosinksa & Coleman, 2008). Specifically, it was expected that attitudes and emotional valence would exhibit stable dynamics when people encountered a neutral, continually cooperative, or continually competitive interaction. However, attitudes and emotional valence were expected to exhibit perturbation in response to transitions from cooperation to competition and vice-versa. These predictions were tested in four experiments. The first study verified most predictions, finding that people have little attitude or valence reaction to interactions that are neutral, continually coo perative or continually competitive. This study also established that people's attitudes are significantly unstable when faced with an interaction shifting from cooperation to competition, and this is experienced with negative emotions. However, interactions shifting from competition to cooperation resulted in stable attitudes and emotional valence. The remaining three experiments sought to explain the lack of psychological reaction to the development of cooperation in a previously competitive interaction. In Study 2, interaction expectancy was ruled out as a factor. Study 3 demonstrated that the reactivity to sudden competition and lack of reactivity to sudden cooperation developed regardless of interaction history. Finally, Study 4 offers evidence indicating that the lack of reaction to sudden cooperation results from factors other than the duration of cooperative feedback. The research has several important implications. First, the results provide evidence that competition is / not necessarily a key factor in promoting heightened psychological reaction in conflict. Rather, transitions between peace and conflict likely hold greater influence on psychological processes in disputes. Furthermore, the experimental evidence provides the first empirical test of the model predictions and offers insight into how the model may be improved. By combining experimental results with the model, the research provides much needed information about how mental dynamics unfold and differ in response to cooperation versus competition. / by Jay L. Michaels. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

A análise de comportamento coletivo e rastreamento de pedestres apresentam diversas aplicações, especialmente em sistemas de vigilância inteligente. Neste trabalho é proposta uma solução compreensiva com objetivo de atingir rastreamento de pedestre e reconhecimento de atividade coletiva de maneira robusta baseada na utilização de câmeras calibradas. Primeiramente, com o objetivo de remover a necessidade de calibração manual, nós apresentamos um método de calibração automática que explora detectores de pedestres e remoção de fundo para calibragem baseada em otimização não-linear. Adicionalmente, nós propomos a utilização da matriz de calibração para gerar candidatos coerentes com a geometria de cena em detectores de pedestres. Nossa abordagem tem como objetivo diminuir o intervalo de escalas comumente utilizado em detectores baseados em janelas deslizantes, gerando um número menor de extrações de atributos e reduzindo o número de falsos positivos na detecção. Em seguida, nós propomos um método de rastreamento de múltiplos pedestres utilizando câmeras calibradas. Nossa abordagem explora histogramas de cor para rastrear os pequenas regiões (patches) de cada alvo. Os vetores de deslocamento obtidos através do pareamento de atributos de aparência são combinados com um vetor obtido através de um preditor de movimento em coordenadas de mundo. Adicionalmente, nós incluímos informações originárias de detectores de pedestres para aumentar a acurácia do sistema e sua habilidade de recuperação a falhas. Por fim, nós propomos uma abordagem hierárquica de duas camadas para o problema de reconhecimento de atividade coletiva baseada no uso de classificadores Random Forests. No primeiro nível da técnica proposta, nós utilizamos distâncias entre pares de pessoas e suas respectivas velocidades relativas para classificar interações de pares. Estas interações são combinadas com a dinâmica do formato do grupo observado (e sua respectiva velocidade) para o reconhecimento de atividades coletivas. Os experimentos realizados neste trabalho demonstram a qualidade de nossas abordagens em sequências de vídeos disponíveis publicamente. Nossos resultados mostram serem competitivos quando comparados com técnicas do estado da arte e, particularmente, apresentam uma boa generalização entre diferentes cenários de captura de vídeo. / Collective behavior detection and pedestrian tracking present many applications, specially in surveillance systems. In this dissertation, we proposed a complete pipeline for achieving robust tracking and collective behavior recognition based on calibrated static cameras. To remove the necessity of manual calibration, we first present a fully automatic self-calibration system that explores pedestrian detection results and background removal at non-consecutive frames in order to calibrate a static camera using a non-linear cost function. We also propose the use of camera calibration to generate geometrically coherent candidates for pedestrian detection. Our approach aims to reduce the scale range typically used in sliding-window techniques, which leads to less feature extractions and decreased number of false positives. Then, we propose a multi-target pedestrian tracking algorithm using a calibrated static camera. The tracking approach explores color histograms to track patches of each target. Obtained displacement vectors are combined with the expected motion of pedestrians in the world coordinate system. The proposed tracker also incorporates pedestrian detector results to improve the system’s accuracy and its ability to recover from failure. Finally, we propose a two-layered approach for collective behavior recognition based on Random Forests classifiers. In the first level, we use inter-personal distances and relative speeds computed in the world coordinate system to classify asymmetrical pair interactions. Those interactions are combined with group shape dynamics and mean velocity to recognize the collective behavior. We devise a set of experiments to attest the quality of our approaches using publicly available datasets. Results have shown to be competitive against state-of-the-art techniques, and particularly of good generalization across different databases.

Fire ants link their legs and jaws together to form functional structures called self- assemblages. Examples include floating rafts, towers, bridges, and bivouacs. We investigate these self-assemblages of fire ants. Our studies are motivated in part by the vision of providing guidance for programmable robot swarms. The goal for such systems is to develop a simple programmable element from which complex patterns or behaviors emerge on the collective level. Intelligence is decentralized, as is the case with social insects such as fire ants. In this combined experimental and theoretical study, we investigate the construction of two fire ant self-assemblages that are critical to the colony’s survival: the raft and the tower. Using time-lapse photography, we record the construction processes of rafts and towers in the laboratory. We identify and characterize individual ant behaviors that we consistently observe during assembly, and incorporate these behaviors into mathematical models of the assembly process. Our models accurately predict both the assemblages’ shapes and growth patterns, thus providing evidence that we have identified and analyzed the key mechanisms for these fire ant self-assemblages. We also develop novel techniques using scanning electron microscopy and micro-computed tomography scans to visualize and quantify the internal structure and packing properties of live linked fire ants. We compare our findings to packings of dead ants and similarly shaped granular material packings to understand how active arranging affects ant spacing and orientation. We find that ants use their legs to increase neighbor spacing and hence reduce their packing density by one-third compared to packings of dead ants. Also, we find that live ants do not align themselves in parallel with nearest neighbors as much as dead ants passively do. Our main contribution is the development of parsimonious mathematical models of how the behaviors of individuals result in the collective construction of fire ant assemblages. The models posit only simple observed behaviors based on local information, yet their mathe- matical analysis yields accurate predictions of assemblage shapes and construction rates for a wide range of ant colony sizes.