Document retrieval for e-mail search and discovery using Formal Concept Analysis

This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual. le structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available in e-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.

Mapping the royal road and other hierarchical functions

In this paper we present a technique for visualising hierarchical and symmetric, multimodal fitness functions that have been investigated in the evolutionary computation literature. The focus of this technique is on landscapes in moderate-dimensional, binary spaces (i.e., fitness functions defined over {0, 1}(n), for n less than or equal to 16). The visualisation approach involves an unfolding of the hyperspace into a two-dimensional graph, whose layout represents the topology of the space using a recursive relationship, and whose shading defines the shape of the cost surface defined on the space. Using this technique we present case-study explorations of three fitness functions: royal road, hierarchical-if-and-only-if (H-IFF), and hierarchically decomposable functions (HDF). The visualisation approach provides an insight into the properties of these functions, particularly with respect to the size and shape of the basins of attraction around each of the local optima.

iMap : um mecanismo de inferência para mapas conceituais

Mapas Conceituais são representações gráficas do conhecimento de uma pessoa num dado momento e área de conhecimento. Por sua natureza investigativa, são utilizados como ferramentas de apoio em abordagens pedagógicas que objetivam promover a aprendizagem significativa. No entanto, o processo de avaliação de um mapa tende a ser custoso pois acarreta uma pesada carga de processamento cognitivo por parte do avaliador, já que este precisa mapear os conceitos e relações em busca de nuances de conhecimento alí presentes. Essa pesquisa tem por objetivo aumentar o nível de abstração nas interações entre o avaliador e os mapas conceituais fornecendo uma camada intermediária de inteligência computacional que favoreça a comunicação por meio de perguntas e respostas em linguagem natural, fornecendo ao avaliador ferramentas que lhe permita examinar o conteúdo do mapa conceitual sem exigir deste o mapeamento visual dos conceitos e relações presentes nos mapas avaliados. Uma ferramenta é prototipada e uma prova de conceito apresentada. A análise da arquitetura proposta permitiu definir uma arquitetura final com características que permitem potencializar o uso de mapas conceituais e facilitar diversas operações pedagógicas com estes. Essa pesquisa situa-se na área de investigação de sistemas de perguntas e resposta, aplicando técnicas de processamento de linguagem natural para análise da pergunta e interpretação do mapa conceitual e aplica técnica de inteligência artificial para inferir respostas às perguntas.

A mobile health care rule-based system

The relation between patient and physician in most modern Health Care Sys- tems is sparse, limited in time and very in exible. On the other hand, and in contradiction with several recent studies, most physicians do not rely their patient diagnostics evaluations on intertwined psychological and social nature factors. Facing these problems and trying to improve the patient/physician relation we present a mobile health care solution to im- prove the interaction between the physician and his patients. The solution serves not only as a privileged mean of communication between physicians and patients but also as an evolutionary intelligent platform delivering a mobile rule based system.

The role of coordination analysis in software integration projects

What sort of component coordination strategies emerge in a software integration process? How can such strategies be discovered and further analysed? How close are they to the coordination component of the envisaged architectural model which was supposed to guide the integration process? This paper introduces a framework in which such questions can be discussed and illustrates its use by describing part of a real case-study. The approach is based on a methodology which enables semi-automatic discovery of coordination patterns from source code, combining generalized slicing techniques and graph manipulation

Agentes ou Jogadores? Os monstros estranhamente reais dos jogos de computadores

Pretendemos mostrar como os sistemas de motion capture permitem transpor para avatares movimentos de pessoas reais para, com o auxílio do processamento gráfico e dos incrementos em Inteligência artificial, criar as planícies de Masahiro Mori, o uncanny valley. Acreditamos que, num futuro próximo, será possível emocionar jogadores se o realismo das interacções no jogo apresentar consistência semelhante às experiências no mundo real.

Agent Inferencing Meets the Semantic Web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

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.

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Electricity Markets Simulation: MASCEM Contributions to the Challenging Reality

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in sophisticated tools very helpful under this context. Some simulation tools have already been developed, some of them very interesting. However, at the present state it is important to go a step forward in Electricity Markets simulators as this is crucial for facing changes in Power Systems. This paper explains the context and needs of electricity market simulation, describing the most important characteristics of available simulators. We present our work concerning MASCEM simulator, presenting its features as well as the improvements being made to accomplish the change and challenging reality of Electricity Markets.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.