General detection model in cooperative multirobot localization

The cooperative multirobot localization problem consists in localizing each robot in a group within the same environment, when robots share information in order to improve localization accuracy. It can be achieved when a robot detects and identifies another one, and measures their relative distance. At this moment, both robots can use detection information to update their own poses beliefs. However some other useful information besides single detection between a pair of robots can be used to update robots poses beliefs as: propagation of a single detection for non participants robots, absence of detections and detection involving more than a pair of robots. A general detection model is proposed in order to aggregate all detection information, addressing the problem of updating poses beliefs in all situations depicted. Experimental results in simulated environment with groups of robots show that the proposed model improves localization accuracy when compared to conventional single detection multirobot localization.FAPESPCNP

Transferring knowledge as heuristics in reinforcement learning: A case-based approach

The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another (simpler) domain (the source domain). This meta-algorithm works in three stages: first, it uses a Reinforcement Learning step to learn a task on the source domain, storing the knowledge thus obtained in a case base; second, it does an unsupervised mapping of the source-domain actions to the target-domain actions; and, third, the case base obtained in the first stage is used as heuristics to speed up the learning process in the target domain. A set of empirical evaluations were conducted in two target domains: the 3D mountain car (using a learned case base from a 2D simulation) and stability learning for a humanoid robot in the Robocup 3D Soccer Simulator (that uses knowledge learned from the Acrobot domain). The results attest that our transfer learning algorithm outperforms recent heuristically-accelerated reinforcement learning and transfer learning algorithms. © 2015 Elsevier B.V.Luiz Celiberto Jr. and Reinaldo Bianchi acknowledge the support of FAPESP (grants 2012/14010-5 and 2011/19280-8). Paulo E. Santos acknowledges support from FAPESP (grant 2012/04089-3) and CNPq (grant PQ2 -303331/2011-9).Peer Reviewe

A tecnologia nos negócios: análise da influência do celular na produtividade organizacional

This article aims to analyze the influence of cell phone on the productivity and organizational behavior of professionals in Belo Horizonte, and proposes a discussion about the benefits and harms of cell phone use in the daily life of individuals and institutions. Through an exploratory study of the perceptions of employees and managers and the orientation of the organizations in relation to technologies, the work is intended to understand the real influence of the tool. Based on the research carried out, the end result is that the tools are essential for professionals and companies in the scenario of constant evolution in which we live. However, the application of mobile phones in the business sphere still requires maturity and discipline to represent gains and competitiveness rather than dispersion.El presente artículo objetiva el análisis de la influencia del celular en la productividad y comportamiento organizacional de profesionales de Belo Horizonte, y propone discusión sobre los beneficios y maleficios del empleo del celular en el cotidiano de los individuos e instituciones. A través de un estudio exploratorio de las percepciones de colaboradores y gestores y, direccionamiento de las organizaciones en relación a las tecnologías, el trabajo tiene como objetivo el entendimiento del real influjo de la herramienta. Con base en la investigación realizada, el desenlace es que las herramientas son esenciales para los profesionales y empresas en el escenario de constante evolución en el que vivimos. Sin embargo, la aplicación del celular en el ámbito empresarial todavía demanda madurez y disciplina para representar ganancias y competitividad en lugar de dispersión.O presente artigo objetiva a análise da influência do celular na produtividade e comportamento organizacional de profissionais de Belo Horizonte, e propõe discussão sobre os benefícios e malefícios do emprego do celular no cotidiano dos indivíduos e instituições. Através de um estudo exploratório das percepções de colaboradores e gestores e, direcionamento das organizações em relação às tecnologias, o trabalho tem como intuito o entendimento do real influxo da ferramenta. Com base na pesquisa realizada, o desfecho é de que as ferramentas são essenciais para os profissionais e empresas no cenário de constante evolução no qual vivenciamos. Contudo, a aplicação do celular no âmbito empresarial ainda demanda maturidade e disciplina para representar ganhos e competitividade ao invés de dispersão

Answer Set Programming for Non-Stationary Markov Decision Processes

Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combines Markov Decision Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming (ASP) in a method we call ASP(RL). In this method, Answer Set Programming is used to find the possible trajectories of an MDP, from where Reinforcement Learning is applied to learn the optimal policy of the problem. Results show that ASP(RL) is capable of efficiently finding the optimal solution of an MDP representing non-stationary domains