VirtualECare: group support in collaborative networks organizations for digital homecare

Collaborative Work plays an important role in today’s organizations, especially in areas where decisions must be made. However, any decision that involves a collective or group of decision makers is, by itself complex, but is becoming recurrent in recent years. In this work we present the VirtualECare project, an intelligent multi-agent system able to monitor, interact and serve its customers, which are, normally, in need of care services. In last year’s there has been a substantially increase on the number of people needed of intensive care, especially among the elderly, a phenomenon that is related to population ageing. However, this is becoming not exclusive of the elderly, as diseases like obesity, diabetes and blood pressure have been increasing among young adults. This is a new reality that needs to be dealt by the health sector, particularly by the public one. Given this scenarios, the importance of finding new and cost effective ways for health care delivery are of particular importance, especially when we believe they should not to be removed from their natural “habitat”. Following this line of thinking, the VirtualECare project will be presented, like similar ones that preceded it. Recently we have also assisted to a growing interest in combining the advances in information society - computing, telecommunications and presentation – in order to create Group Decision Support Systems (GDSS). Indeed, the new economy, along with increased competition in today’s complex business environments, takes the companies to seek complementarities in order to increase competitiveness and reduce risks. Under these scenarios, planning takes a major role in a company life. However, effective planning depends on the generation and analysis of ideas (innovative or not) and, as a result, the idea generation and management processes are crucial. Our objective is to apply the above presented GDSS to a new area. We believe that the use of GDSS in the healthcare arena will allow professionals to achieve better results in the analysis of one’s Electronically Clinical Profile (ECP). This achievement is vital, regarding the explosion of knowledge and skills, together with the need to use limited resources and get better results.

Aspectos de aprendizagem em optimização

A optimização e a aprendizagem em Sistemas Multi-Agente são consideradas duas áreas promissoras mas relativamente pouco exploradas. A optimização nestes ambientes deve ser capaz de lidar com o dinamismo. Os agentes podem alterar o seu comportamento baseando-se em aprendizagem recente ou em objectivos de optimização. As estratégias de aprendizagem podem melhorar o desempenho do sistema, dotando os agentes da capacidade de aprender, por exemplo, qual a técnica de optimização é mais adequada para a resolução de uma classe particular de problemas, ou qual a parametrização é mais adequada em determinado cenário. Nesta dissertação são estudadas algumas técnicas de resolução de problemas de Optimização Combinatória, sobretudo as Meta-heurísticas, e é efectuada uma revisão do estado da arte de Aprendizagem em Sistemas Multi-Agente. É também proposto um módulo de aprendizagem para a resolução de novos problemas de escalonamento, com base em experiência anterior. O módulo de Auto-Optimização desenvolvido, inspirado na Computação Autónoma, permite ao sistema a selecção automática da Meta-heurística a usar no processo de optimização, assim como a respectiva parametrização. Para tal, recorreu-se à utilização de Raciocínio baseado em Casos de modo que o sistema resultante seja capaz de aprender com a experiência adquirida na resolução de problemas similares. Dos resultados obtidos é possível concluir da vantagem da sua utilização e respectiva capacidade de adaptação a novos e eventuais cenários.

Credibilidade e reputação em agentes inteligentes. Aplicação ao comércio electrónico

Desde o seu aparecimento, a Internet teve um desenvolvimento e uma taxa de crescimento quase exponencial. Os mercados de comércio electrónico têm vindo a acompanhar esta tendência de crescimento, tornando-se cada vez mais comuns e populares entre comerciantes ou compradores/vendedores de ocasião. A par deste crescimento também foi aumentando a complexidade e sofisticação dos sistemas responsáveis por promover os diferentes mercados. No seguimento desta evolução surgiram os Agentes Inteligentes devido à sua capacidade de encontrar e escolher, de uma forma relativamente eficiente, o melhor negócio, tendo por base as propostas e restrições existentes. Desde a primeira aplicação dos Agentes Inteligentes aos mercados de comércio electrónico que os investigadores desta área, têm tentado sempre auto-superar-se arranjando modelos de Agentes Inteligentes melhores e mais eficientes. Uma das técnicas usadas, para a tentativa de obtenção deste objectivo, é a transferência dos comportamentos Humanos, no que toca a negociação e decisão, para estes mesmos Agentes Inteligentes. O objectivo desta dissertação é averiguar se os Modelos de Avaliação de Credibilidade e Reputação são uma adição útil ao processo de negociação dos Agente Inteligentes. O objectivo geral dos modelos deste tipo é minimizar as situações de fraude ou incumprimento sistemático dos acordos realizados aquando do processo de negociação. Neste contexto, foi proposto um Modelo de Avaliação de Credibilidade e Reputação aplicável aos mercados de comércio electrónico actuais e que consigam dar uma resposta adequada o seu elevado nível de exigência. Além deste modelo proposto também foi desenvolvido um simulador Multi-Agente com a capacidade de simular vários cenários e permitir, desta forma, comprovar a aplicabilidade do modelo proposto. Por último, foram realizadas várias experiências sobre o simulador desenvolvido, de forma a ser possível retirar algumas conclusões para o presente trabalho. Sendo a conclusão mais importante a verificação/validação de que a utilização de mecanismos de credibilidade e reputação são uma mais-valia para os mercado de comércio electrónico.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Negotiation platform for personalised advertising

This paper describes a multi-agent brokerage platform for near real time advertising personalisation organised in three layers: user interface, agency and marketplace. The personalisation is based on the classification of viewer profiles and advertisements (ads). The goal is to provide viewers with a personalised advertising alignment during programme intervals. The enterprise interface agents upload new ads and negotiation profiles to producer agents and new user and negotiation profiles to distributor agents. The agency layer is composed of agents that represent ad producer and media distributor enterprises as well as the market regulator. The enterprise agents offer data upload and download operations as Web Services and register the specification of these interfaces at an UDDI registry for future discovery. The market agent supports the registration and deregistration of enterprise delegate agents at the marketplace. This paper addresses the marketplace layer, an agent-based negotiation platform per se, where delegates of the relevant advertising agencies and programme distributors negotiate to create the advertising alignment that best fits a viewer profile and the advertising campaigns available. The whole brokerage platform is being developed in JADE, a multi-agent development platform. The delegate agents download the negotiation profile and upload the negotiation results from / to the corresponding enterprise agent. In the meanwhile, they negotiate using the Iterated Contract Net protocol. All tools and technologies used are open source.

A proposal for media component brokerage

This paper describes how MPEG-4 object based video (obv) can be used to allow selected objects to be inserted into the play-out stream to a specific user based on a profile derived for that user. The application scenario described here is for personalized product placement, and considers the value of this application in the current and evolving commercial media distribution market given the huge emphasis media distributors are currently placing on targeted advertising. This level of application of video content requires a sophisticated content description and metadata system (e.g., MPEG-7). The scenario considers the requirement for global libraries to provide the objects to be inserted into the streams. The paper then considers the commercial trading of objects between the libraries, video service providers, advertising agencies and other parties involved in the service. Consequently a brokerage of video objects is proposed based on negotiation and trading using intelligent agents representing the various parties. The proposed Media Brokerage Platform is a multi-agent system structured in two layers. In the top layer, there is a collection of coarse grain agents representing the real world players – the providers and deliverers of media contents and the market regulator profiler – and, in the bottom layer, there is a set of finer grain agents constituting the marketplace – the delegate agents and the market agent. For knowledge representation (domain, strategic and negotiation protocols) we propose a Semantic Web approach based on ontologies. The media components contents should be represented in MPEG-7 and the metadata describing the objects to be traded should follow a specific ontology. The top layer content providers and deliverers are modelled by intelligent autonomous agents that express their will to transact – buy or sell – media components by registering at a service registry. The market regulator profiler creates, according to the selected profile, a market agent, which, in turn, checks the service registry for potential trading partners for a given component and invites them for the marketplace. The subsequent negotiation and actual transaction is performed by delegate agents in accordance with their profiles and the predefined rules of the market.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Evaluating the combination of relaxation and argumentation in ontology matching negotiation

No decorrer dos últimos anos, os agentes (inteligentes) de software foram empregues como um método para colmatar as dificuldades associadas com a gestão, partilha e reutilização de um crescente volume de informação, enquanto as ontologias foram utilizadas para modelar essa mesma informação num formato semanticamente explícito e rico. À medida que a popularidade da Web Semântica aumenta e cada vez informação é partilhada sob a forma de ontologias, o problema de integração desta informação amplifica-se. Em semelhante contexto, não é expectável que dois agentes que pretendam cooperar utilizem a mesma ontologia para descrever a sua conceptualização do mundo. Inclusive pode revelar-se necessário que agentes interajam sem terem conhecimento prévio das ontologias utilizadas pelos restantes, sendo necessário que as conciliem em tempo de execução num processo comummente designado por Mapeamento de Ontologias [1]. O processo de mapeamento de ontologias é normalmente oferecido como um serviço aos agentes de negócio, podendo ser requisitado sempre que seja necessário produzir um alinhamento. No entanto, tendo em conta que cada agente tem as suas próprias necessidades e objetivos, assim como a própria natureza subjetiva das ontologias que utilizam, é possível que tenham diferentes interesses relativamente ao processo de alinhamento e que, inclusive, recorram aos serviços de mapeamento que considerem mais convenientes [1]. Diferentes matchers podem produzir resultados distintos e até mesmo contraditórios, criando-se assim conflitos entre os agentes. É necessário que se proceda então a uma tentativa de resolução dos conflitos existentes através de um processo de negociação, de tal forma que os agentes possam chegar a um consenso relativamente às correspondências que devem ser utilizadas na tradução de mensagens a trocar. A resolução de conflitos é considerada uma métrica de grande importância no que diz respeito ao processo de negociação [2]: considera-se que existe uma maior confiança associada a um alinhamento quanto menor o número de conflitos por resolver no processo de negociação que o gerou. Desta forma, um alinhamento com um número elevado de conflitos por resolver apresenta uma confiança menor que o mesmo alinhamento associado a um número elevado de conflitos resolvidos. O processo de negociação para que dois ou mais agentes gerem e concordem com um alinhamento é denominado de Negociação de Mapeamentos de Ontologias. À data existem duas abordagens propostas na literatura: (i) baseadas em Argumentação (e.g. [3] [4]) e (ii) baseadas em Relaxamento [5] [6]. Cada uma das propostas expostas apresenta um número de vantagens e limitações. Foram propostas várias formas de combinação das duas técnicas [2], com o objetivo de beneficiar das vantagens oferecidas e colmatar as suas limitações. No entanto, à data, não são conhecidas experiências documentadas que possam provar tal afirmação e, como tal, não é possível atestar que tais combinações tragam, de facto, o benefício que pretendem. O trabalho aqui apresentado pretende providenciar tais experiências e verificar se a afirmação de melhorias em relação aos resultados das técnicas individuais se mantém. Com o objetivo de permitir a combinação e de colmatar as falhas identificadas, foi proposta uma nova abordagem baseada em Relaxamento, que é posteriormente combinada com as abordagens baseadas em Argumentação. Os seus resultados, juntamente com os da combinação, são aqui apresentados e discutidos, sendo possível identificar diferenças nos resultados gerados por combinações diferentes e possíveis contextos de utilização.

Plataforma de simulação em Repast Simphony para análise da divulgação de novas ideias em mercado

Modelação e simulação baseadas em agentes estão a ganhar cada vez mais importância e adeptos devido à sua flexibilidade e potencialidade em reproduzir comportamentos e estudar um sistema na perspetiva global ou das interações individuais. Neste trabalho, criou-se um sistema baseado em agentes e desenvolvido em Repast Simphony com o objectivo de analisar a difusão de um novo produto ou serviço através de uma rede de potenciais clientes, tentando compreender, assim, como ocorre e quanto tempo demora esta passagem de informação (inovação) com diversas topologias de rede, no contato direto entre pessoas. A simulação baseia-se no conceito da existencia de iniciadores, que são os primeiros consumidores a adotar um produto quando este chega ao mercado e os seguidores, que são os potenciais consumidores que, apesar de terem alguma predisposição para adotar um novo produto, normalmente só o fazem depois de terem sido sujeitos a algum tipo de influência. Com a aplicação criada, simularam-se diversas situações com a finalidade de obter e observar os resultados gerados a partir de definições iniciais diferentes. Com os resultados gerados pelas simulações foram criados gráficos representativos dos diversos cenários. A finalidade prática desta aplicação, poderá ser o seu uso em sala de aula para simulação de casos de estudo e utilização, em casos reais, como ferramenta de apoio à tomada de decisão, das empresas.

Personalised placement in networked video

Personalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.

Solving Conflicting Beliefs with a Distributed Belief Revision Approach

The ability to solve conflicting beliefs is crucial for multi- agent systems where the information is dynamic, incomplete and dis- tributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance sy- stem and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are au- tomatically detected by system’s reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodolo- gies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Con- flicts is, basically, a selection process based on the assessment of the cre- dibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a “next best” relaxation strategy.

Improving Assumption based Distributed Belief Revision

Belief revision is a critical issue in real world DAI applications. A Multi-Agent System not only has to cope with the intrinsic incompleteness and the constant change of the available knowledge (as in the case of its stand alone counterparts), but also has to deal with possible conflicts between the agents’ perspectives. Each semi-autonomous agent, designed as a combination of a problem solver – assumption based truth maintenance system (ATMS), was enriched with improved capabilities: a distributed context management facility allowing the user to dynamically focus on the more pertinent contexts, and a distributed belief revision algorithm with two levels of consistency. This work contributions include: (i) a concise representation of the shared external facts; (ii) a simple and innovative methodology to achieve distributed context management; and (iii) a reduced inter-agent data exchange format. The different levels of consistency adopted were based on the relevance of the data under consideration: higher relevance data (detected inconsistencies) was granted global consistency while less relevant data (system facts) was assigned local consistency. These abilities are fully supported by the ATMS standard functionalities.

Distributed Belief Revision and Environmental Decision Support

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed - Distributed Belief Revision Test-bed - DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Distributed Belief Revision and Environmental Decision Support

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed ; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Intelligent Distributed Environmental Decision Support System

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed Distributed Belief Revision Test-bed — DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.