General Aspects of Constructing an Autonomous Adaptive Agent

There are a great deal of approaches in artificial intelligence, some of them also coming from biology and neirophysiology. In this paper we are making a review, discussing many of them, and arranging our discussion around the autonomous agent research. We highlight three aspect in our classification: type of abstraction applied for representing agent knowledge, the implementation of hypothesis processing mechanism, allowed degree of freedom in behaviour and self-organizing. Using this classification many approaches in artificial intelligence are evaluated. Then we summarize all discussed ideas and propose a series of general principles for building an autonomous adaptive agent.

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

It's good to be different:diversity, heterogeneity, and dynamics in collective systems

We identify two different forms of diversity present in engineered collective systems, namely heterogeneity (genotypic/phenotypic diversity) and dynamics (temporal diversity). Three qualitatively different case studies are analysed, and it is shown that both forms of diversity can be beneficial in very different problem and application domains. Behavioural diversity is shown to be motivated by input diversity and this observation is used to present recommendations for designers of collective systems.

Defining the role of floating periphyton mats in shaping food-web dynamics in the Florida Everglades

Expansive periphyton mats are a striking characteristic of the Florida Everglades. Floating periphyton mats are home to a diverse macroinvertebrate community dominated by chironomid and ceratopogonid larvae and amphipods that use the mat as both a food resource and refuge from predation. While this periphyton complex functions as a self-organizing system, it also serves as a base for trophic interactions with larger organisms. The purpose of my research was to quantify variation in the macroinvertebrate community inhabiting floating periphyton mats, describe the role of mats in shaping food-web dynamics, and describe how these trophic interactions change with eutrophication. ^ I characterized the macroinvertebrate community inhabiting periphyton through a wet-season by describing spatial variation on scales from 0.2 m to 3 km. Floating periphyton mats contained a diverse macroinvertebrate community, with greater taxonomic richness and higher densities of many taxa than adjacent microhabitats. Macroinvertebrate density increased through the wet season as periphyton mats developed. While some variation was noted among sites, spatial patterns were not observed on smaller scales. I also sampled ten sites representing gradients of hydroperiod and nutrient (P) levels. The density of macroinvertebrates inhabiting periphyton mats increased with increasing P availability; however, short-hydroperiod P-enriched sites had the highest macroinvertebrate density. This pattern suggests a synergistic interaction of top-down and bottom-up effects. In contrast, macroinvertebrate density was lower in benthic floc, where it was negatively correlated with hydroperiod. ^ I used two types of mesocosms (field cages and tanks) to manipulate large consumers (fish and grass shrimp) with inclusion/exclusion cages over an experimental P gradient. In most cases, periphyton mats served as an effective predation refuge. Macroinvertebrates were consumed more frequently in P-enriched treatments, where mats were also heavily grazed. Macroinvertebrate densities decreased with increasing P in benthic floc, but increased with enrichment in periphyton mats until levels were reached that caused disassociation of the mat. ^ This research documents several indirect trophic interactions that can occur in complex habitats, and emphasizes the need to characterize dynamics of all microhabitats to fully describe the dynamics of an ecosystem. ^

Space division multiple access for wireless sensor networks

This dissertation proposed a self-organizing medium access control protocol (MAC) for wireless sensor networks (WSNs). The proposed MAC protocol, space division multiple access (SDMA), relies on sensor node position information and provides sensor nodes access to the wireless channel based on their spatial locations. SDMA divides a geographical area into space divisions, where there is one-to-one map between the space divisions and the time slots. Therefore, the MAC protocol requirement is the sensor node information of its position and a prior knowledge of the one-to-one mapping function. The scheme is scalable, self-maintaining, and self-starting. It provides collision-free access to the wireless channel for the sensor nodes thereby, guarantees delay-bounded communication in real time for delay sensitive applications. This work was divided into two parts: the first part involved the design of the mapping function to map the space divisions to the time slots. The mapping function is based on a uniform Latin square. A Uniform Latin square of order k = m 2 is an k x k square matrix that consists of k symbols from 0 to k-1 such that no symbol appears more than once in any row, in any column, or in any m x in area of main subsquares. The uniqueness of each symbol in the main subsquares presents very attractive characteristic in applying a uniform Latin square to time slot allocation problem in WSNs. The second part of this research involved designing a GPS free positioning system for position information. The system is called time and power based localization scheme (TPLS). TPLS is based on time difference of arrival (TDoA) and received signal strength (RSS) using radio frequency and ultrasonic signals to measure and detect the range differences from a sensor node to three anchor nodes. TPLS requires low computation overhead and no time synchronization, as the location estimation algorithm involved only a simple algebraic operation.

A concepção de trabalho escolar a partir de Maurício Tragtenberg: fios e tramas de debate na (des)politização dos sujeitos sociais

This thesis, part of the research line "Work, Society and Education" analyzes, in a dialectical perspective, in the light of Tragtenberg´s studies, the conception of subject of schoolwork, considering the organization and the complexity of this context and inviting the investigative gaze. Our hypothesis is that the workers of education have, in the schoolwork, a fragile politicization field and overcoming the installed model, accepting themselves as executers with self-organizing difficulties, distanced from the effective participation, autonomy and self-management. We consider studies on the logic of the work, believing that the understanding of schoolwork is constitute into the current societal model, in whom its bureaucratic and hierarchical matrix, linked to articulated / articulators of the dominant socio-economic class interests. Scholars such as Braverman, Frigotto and Tragtenberg, among others, are important contributions to this reflection. We have analyzed the logic of the work also in dialogue with pedagogies differentiated, among them, the Libertarian defended by Tragtenberg, aiming to understand the conceptions and practices of the subjects involved. We accomplished a bibliographical critical analysis, in a dialectical character, focusing on the categories: work, schoolwork, control, autonomy and self-management, in order to understand the complexity of the praxis in study, dialoguing with the pillars categories historicity, contradiction and totality, which transversalize the developed analysis. The consideration of these concepts allows us to uncover the dominant bureaucratic structure and its possible overcoming fields. We believe contribute to problematizing and proposers studies, reflections and discussions, strengthening debates, deconstructing naturalizations and contributing to the political process of the subjects.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Interface-mediated ferroelectric patterning and Mn valency in nano-structured PbTiO3/La0.7Sr0.3MnO3

We employed a multitechnique approach using piezo-force response microscopy and photoemission microscopy to investigate a self-organizing polarization domain pattern in PbTiO3/La0.7Sr0.3MnO3 (PTO/LSMO) nanostructures. The polarization is correlated with the nanostructure morphology as well as with the thickness and Mn valence of the LSMO template layer. On the LSMO dots, the PTO is upwards polarized, whereas outside the nanodots, the polarization appears both strain and interface roughness dependent. The results suggest that the electronic structure and strain of the PTO/LSMO interface contribute to determining the internal bias of the ferroelectric layer.

Impact of Thresholds and Load Patterns when Executing HPC Applications with Cloud Elasticity

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed reactively using thresholds. In this way, maximum and minimum limits are used to drive resource allocation and deallocation actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application’s load pattern in the elasticity? This article tries to answer these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the maximum threshold influences the application time more than the minimum one. We concluded that threshold values close to 100% of CPU load are directly related to a weaker reactivity, postponing resource reconfiguration when its activation in advance could be pertinent for reducing the application runtime.

Implementación eficiente de redes neuronales autoorganizadas en CUDA C/C++.

En este documento se expondrá una implementación del problema del viajante de comercio usando una implementación personalizada de un mapa auto-organizado basándose en soluciones anteriores y adaptándolas a la arquitectura CUDA, haciendo a la vez una comparativa de la implementación eficiente en CUDA C/C++ con la implementación de las funciones de GPU incluidas en el Parallel Computing Toolbox de Matlab. La solución que se da reduce en casi un cuarto las iteraciones necesarias para llegar a una solución buena del problema mencionado, además de la mejora inminente del uso de las arquitecturas paralelas. En esta solución se estudia la mejora en tiempo que se consigue con el uso específico de la memoria compartida, siendo esta una de las herramientas más potentes para mejorar el rendimiento. En lo referente a los tiempos de ejecución, se llega a concluir que la mejor solución es el lanzamiento de un kernel de CUDA desde Matlab a través de la funcionalidad incluida en el Parallel Computing Toolbox.

The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

Location analysis of city sections: socio-demographic segmentation and restaurant potentiality estimation : a case study of Lisbon

One of the objectives of this study is to perform classification of socio-demographic components for the level of city section in City of Lisbon. In order to accomplish suitable platform for the restaurant potentiality map, the socio-demographic components were selected to produce a map of spatial clusters in accordance to restaurant suitability. Consequently, the second objective is to obtain potentiality map in terms of underestimation and overestimation in number of restaurants. To the best of our knowledge there has not been found identical methodology for the estimation of restaurant potentiality. The results were achieved with combination of SOM (Self-Organized Map) which provides a segmentation map and GAM (Generalized Additive Model) with spatial component for restaurant potentiality. Final results indicate that the highest influence in restaurant potentiality is given to tourist sites, spatial autocorrelation in terms of neighboring restaurants (spatial component), and tax value, where lower importance is given to household with 1 or 2 members and employed population, respectively. In addition, an important conclusion is that the most attractive market sites have shown no change or moderate underestimation in terms of restaurants potentiality.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

Arquitecturas Flexibles, Crecientes y Jerárquicas para Sistemas Neuronales Autoorganizados

La autoorganización es un proceso de aprendizaje no supervisado mediante el cual se descubren características, relaciones, patrones significativos o prototipos en los datos. Entre los sistemas neuronales autoorganizados más usados destaca el el mapa autoorganizado o SOM (Self-Organizing Map), el cual ha sido aplicado en multitud de campos distintos. Sin embargo, este modelo autoorganizado tiene varias limitaciones relacionadas con su tamaño, topología, falta de representación de relaciones jerárquicas, etc. La red neuronal llamada gas neuronal creciente o GNG (Growing Neural Gas), es un ejemplo de modelo neuronal autoorganizado con mayor flexibilidad que el SOM ya que está basado en un grafo de unidades de proceso en vez de en una topología fija. A pesar de su éxito, se ha prestado poca atención a su extensión jerárquica, a diferencia de muchos otros modelos que tienen varias versiones jerárquicas. El gas neuronal jerárquico creciente o GHNG (Growing Hierarchical Neural Gas) es una extensión jerárquica del GNG en el que se aprende un árbol de grafos, donde el algoritmo original del GNG se ha mejorado distinguiendo entre una fase de crecimiento y una fase de convergencia. Los resultados experimentales demuestran las capacidades de autoorganización y aprendizaje jerárquico de esta red.

Holacracy as an alternative to organisations governance

Holacracy as an alternative to organisations governance A *TRS event, Room P1.2 (Fernando Pessoa Hall), University Fernando Pessoa 16th, November, 2016, Luis Borges Gouveia This presentation introduce holacracy and discuss its use as an alternative to organize people within a corporate environment. A holacracy is a governance structure characterized by a distribution of power among self-organizing groups, rather than the top-down authority in the typical hierarchical corporate culture model. A holacracy provides a flat management structure that distributes authority. The goal of a holacracy is to ensure that those responsible for completing the work have the authority to decide how that work should be carried out. Holacracy benefits are the promises to harness agility, transparency, accountability, employee engagement and innovation. It also potentiates greater efficiency. Main critics are that the model do not allow for sufficient lateral communication. Also, it use is still emerging and we do not have sufficient evidence on holacracy advantages to rely on its promises