Análise funcional comparativa de algoritmos de aprendizagem por reforço

De entre todos os paradigmas de aprendizagem actualmente identificados, a Aprendizagem por Reforço revela-se de especial interesse e aplicabilidade nos inúmeros processos que nos rodeiam: desde a solitária sonda que explora o planeta mais remoto, passando pelo programa especialista que aprende a apoiar a decisão médica pela experiencia adquirida, até ao cão de brincar que faz as delícias da criança interagindo com ela e adaptando-se aos seus gostos, e todo um novo mundo que nos rodeia e apela crescentemente a que façamos mais e melhor nesta área. Desde o aparecimento do conceito de aprendizagem por reforço, diferentes métodos tem sido propostos para a sua concretização, cada um deles abordando aspectos específicos. Duas vertentes distintas, mas complementares entre si, apresentam-se como características chave do processo de aprendizagem por reforço: a obtenção de experiência através da exploração do espaço de estados e o aproveitamento do conhecimento obtido através dessa mesma experiência. Esta dissertação propõe-se seleccionar alguns dos métodos propostos mais promissores de ambas as vertentes de exploração e aproveitamento, efectuar uma implementação de cada um destes sobre uma plataforma modular que permita a simulação do uso de agentes inteligentes e, através da sua aplicação na resolução de diferentes configurações de ambientes padrão, gerar estatísticas funcionais que permitam inferir conclusões que retractem entre outros aspectos a sua eficiência e eficácia comparativas em condições específicas.

Manutenção proactiva de sistemas AVAC com recurso aos sistemas inteligentes multiagente

Nos tempos actuais os equipamentos para Aquecimento Ventilação e Ar Condicionado (AVAC) ocupam um lugar de grande importância na concepção, desenvolvimento e manutenção de qualquer edifício por mais pequeno que este seja. Assim, surge a necessidade premente de racionalizar os consumos energéticos optimizando-os. A alta fiabilidade desejada nestes sistemas obriga-nos cada vez mais a descobrir formas de tornar a sua manutenção mais eficiente, pelo que é necessário prevenir de uma forma proactiva todas as falhas que possam prejudicar o bom desempenho destas instalações. Como tal, torna-se necessário detectar estas falhas/anomalias, sendo imprescíndivel que nos antecipemos a estes eventos prevendo o seu acontecimento num horizonte temporal pré-definido, permitindo actuar o mais cedo possível. É neste domínio que a presente dissertação tenta encontrar soluções para que a manutenção destes equipamentos aconteça de uma forma proactiva e o mais eficazmente possível. A ideia estruturante é a de tentar intervir ainda numa fase incipiente do problema, alterando o comportamento dos equipamentos monitorizados, de uma forma automática, com recursos a agentes inteligentes de diagnóstico de falhas. No caso em estudo tenta-se adaptar de forma automática o funcionamento de uma Unidade de Tratamento de Ar (UTA) aos desvios/anomalias detectadas, promovendo a paragem integral do sistema apenas como último recurso. A arquitectura aplicada baseia-se na utilização de técnicas de inteligência artificial, nomeadamente dos sistemas multiagente. O algoritmo utilizado e testado foi construído em Labview®, utilizando um kit de ferramentas de controlo inteligente para Labview®. O sistema proposto é validado através de um simulador com o qual se conseguem reproduzir as condições reais de funcionamento de uma UTA.

Sobrevivência: jogo interactivo para estudo de arquitecturas de agentes autónomos

Trabalho de projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Integração de reacção e deliberação em agentes inteligentes

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

Interação social entre agentes em ambientes de recursos limitados

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

(Virtual) Agents for running electricity markets

This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units' resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

QSAR modeling of antitubercular activity of diverse organic compounds

Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Commercial agents portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

Nanofiltration of Cork Wastewaters and Their Possible Use in Leather Industry as Tanning Agents

Cork processing wastewater is a very complex mixture of vegetal extracts and has, among other natural compounds, a very high content of phenolic/tannic colloidal matter that is responsible for severe environmental problems. In the present work, the concentration of this wastewater by nanofiltration was investigated with the aim of producing a cork tannin concentrate to be utilized in tanning. Permeation results showed that the permeate fluxes are controlled by both osmotic pressure and fouling/gel layer phenomena, leading to a rapid decrease of permeate fluxes with the concentration factor. The rejection coefficients to organic matter were higher than 95%, indicating that nanofiltration has a very good ability to concentrate the tannins and produce a permeate stream depleted from organic matter. The cork tannin concentrate obtained by nanofiltration and evaporation had total solids concentration of 34.8 g/l. The skins tanned by this concentrate were effectively converted to leather with a shrinking temperature of 7 degrees C.

Molecular imaging agents for detection of β-amyloid plaques in Alzheimer’s disease

The formation of amyloid structures is a neuropathological feature that characterizes several neurodegenerative disorders, such as Alzheimer´s and Parkinson´s disease. Up to now, the definitive diagnosis of these diseases can only be accomplished by immunostaining of post mortem brain tissues with dyes such Thioflavin T and congo red. Aiming at early in vivo diagnosis of Alzheimer´s disease (AD), several amyloid-avid radioprobes have been developed for b-amyloid imaging by positron emission tomography (PET) and single-photon emission computed tomography (SPECT). The aim of this paper is to present a perspective of the available amyloid imaging agents, special those that have been selected for clinical trials and are at the different stages of the US Food and Drugs Administration (FDA) approval.

Genotoxic assessment in different exposure groups working with antineoplastic agents

Antioneoplastic drugs are widely used in treatment of cancer, and several studies suggest acute and long-term effects associated to antineoplastic drug exposures, namely associating workplace exposure with health effects. Cytokinesis blocked micronucleus (CBMN) assay is one promising short-term genotoxicity assays for human risk assessment and their combination is recommended to monitor populations chronically exposed to genotoxic agents. The aim of this investigation is the genotoxicity assessment in different professionals that handle cytostatics drugs. This research is case-control blinded study constituted by 46 non-exposed subjects and 44 workers that handle antineoplastic drugs, such as pharmacists, pharmacy technicians, and nurses. It was found statistically significant increases in the genotoxicity biomarkers in exposed comparising with controls (p<0.05). The findings address the need for regular biomonitoring of personnel occupationally exposed to these drugs, confirming to an enhanced health risk assessment.