Survival of the mutable: architecture of adaptive reactive agents

An agent is defined as any device that perceives a certain environment through stimuli and acts upon it as to achieve a certain goal. There is a plethora of theories, architectures and languages in the literature aiming at how much an agent may be improved at performing a task. However, the majority of them focuses on the internal agent function itself instead of adopting a macroscopic, broader view of what the term “intelligent” means in the long run. In this paper we take a bio-inspired route and describe how the simplest reactive agent can be boosted towards improvements at performing complex tasks by making it mutable. We provide a mathematical framework to support such features. Conceptually, the addition of a mutability layer does not break the existing paradigms and allows hybrid approaches as a means to achieve better results.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images

We segment buildings and trees from aerial photographs by using superpixels, and we estimate the tree’s parameters by using a cost function proposed in this paper. A method based on image complexity is proposed to refine superpixels boundaries. In order to classify buildings from ground and classify trees from grass, the salient feature vectors that include colors, Features from Accelerated Segment Test (FAST) corners, and Gabor edges are extracted from refined superpixels. The vectors are used to train the classifier based on Naive Bayes classifier. The trained classifier is used to classify refined superpixels as object or nonobject. The properties of a tree, including its locations and radius, are estimated by minimizing the cost function. The shadow is used to calculate the tree height using sun angle and the time when the image was taken. Our segmentation algorithm is compared with other two state-of-the-art segmentation algorithms, and the tree parameters obtained in this paper are compared to the ground truth data. Experiments show that the proposed method can segment trees and buildings appropriately, yielding higher precision and better recall rates, and the tree parameters are in good agreement with the ground truth data

Ethics of Coding: A Report on the Algorithmic Condition [EoC]

This project responds to the ICT-35-2016 Enabling responsible ICT-related research and innovation, topic B, and will "reflect and challenge the way ICT-related research and innovation is currently approached." The computerization of society in the late 1970s has now reached a point where the global economy works through an algorithmic networked environment. This situation is addressed in this research as an algorithmic condition. Any form of ICT operates within this condition. The question is, what are the ethical codes and guidelines that guide research within this condition? The Ethics of Coding prepares research that will provide an indexical report on the conceptual and thematic issues of ICT- related research and innovation, which will suggest what an ethics for ICT related issues could be, and how that might be implemented in relation to actualized and possible ICT projects. In addition, the research addresses the extent to which the coding of the social, ethical, and pedagogic, is always already invested in the maintenance of power relations that control the economic conditions for knowledge (which regulate the global markets) with what Wendy Chun (2011) describes as a ""code logos."" Working with the Philosopher of the human condition of the twentieth century; Hannah Arendt (1958; 1978), an inter-disciplinary think-tank research team brings Arendtian ethical philosophy into dialogue with SSH experts from a number of disciplinary fields, including thinkers of technologies and their effects on societies, philosophers of mathematics, gender and humanities experts, educational philosophy specialists, digital media thinkers, to produce a report that reflects the expression of the human algorithmic condition

Improving Electricity Distribution System State Estimation with AMR-Based Load Profiles

The ongoing battle against global warming is rapidly increasing the amount of renewable power generation, and smart solutions are needed to integrate these new generation units into the existing distribution systems. Smart grids answer this call by introducing intelligent ways of controlling the network and active resources connected to it. However, before the network can be controlled, the automation system must know what the node voltages and line currents defining the network state are.Distribution system state estimation (DSSE) is needed to find the most likely state of the network when the number and accuracy of measurements are limited. Typically, two types of measurements are used in DSSE: real-time measurements and pseudomeasurements. In recent years, finding cost-efficient ways to improve the DSSE accuracy has been a popular subject in the literature. While others have focused on optimizing the type, amount and location of real-time measurements, the main hypothesis of this thesis is that it is possible to enhance the DSSE accuracy by using interval measurements collected with automatic meter reading (AMR) to improve the load profiles used as pseudo-measurements.The work done in this thesis can be divided into three stages. In the first stage, methods for creating new AMR-based load profiles are studied. AMR measurements from thousands of customers are used to test and compare the different options for improving the load profiling accuracy. Different clustering algorithms are tested and a novel twostage clustering method for load profiling is developed. In the second stage, a DSSE algorithm suited for smart grid environment is developed. Simulations and real-life demonstrations are conducted to verify the accuracy and applicability of the developed state estimator. In the third and final stage, the AMR-based load profiling and DSSE are combined. Matlab simulations with real AMR data and a real distribution network model are made and the developed load profiles are compared with other commonly used pseudo-measurements.The results indicate that clustering is an efficient way to improve the load profiling accuracy. With the help of clustering, both the customer classification and customer class load profiles can be updated simultaneously. Several of the tested clustering algorithms were suited for clustering electricity customers, but the best results were achieved with a modified k-means algorithm. Results from the third stage simulations supported the main hypothesis that the new AMR-based load profiles improve the DSSE accuracy.The results presented in this thesis should motivate distribution system operators and other actors in the field of electricity distribution to utilize AMR data and clustering algorithms in load profiling. It improves not only the DSSE accuracy but also many other functions that rely on load flow calculation and need accurate load estimates or forecasts

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal