Spelling suggestions: "subject:"[een] MULTIAGENT SYSTEMS"" "subject:"[enn] MULTIAGENT SYSTEMS""
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Simula : ambiente para desenvolvimento de sistemas multiagentes reativos / Simula - a tool for development of reactive multiagent systemsFrozza, Rejane January 1997 (has links)
Sistema multiagente é um tema de estudo em IAD, no qual um conjunto de agentes interage em um ambiente comum. A IAD baseia-se no comportamento social de agentes (humanos e artificiais), enfatizando as ações e as interações dos mesmos. Esses agentes podem ser cognitivos ou reativos. Os sistemas multiagentes reativos têm sido usados em pesquisas e estudos ligados a campos importantes de aplicação, gerando o desenvolvimento de sistemas não apenas para a área acadêmica, mas também para atender às necessidades do mercado industrial. Com o objetivo de abranger um ramo de pesquisas em sistemas multiagentes, este trabalho propôs a definição e a implementação de um protótipo de um ambiente de software que possibilita o desenvolvimento de aplicações em sistemas multiagentes reativos. Este ambiente tem a finalidade de facilitar a criação de tais aplicações com o uso de agentes, atingindo um resultado satisfatório. O ambiente definido é o SIMULA, que possibilita ao usuário criar suas aplicações através de elementos de uma interface gráfica. O usuário, na interação com o ambiente, determina os agentes envolvidos no problema e como eles agirão no processo de resolução do mesmo. O usuário define a sua aplicação criando um modelo para ela. Para definir as características do ambiente SIMULA, foram estudados e analisados três modelos de sistemas multiagentes reativos, encontrados em [STE 90], [DEM 93] e [FER 91], e estabelecido um quadro comparativo dos mesmos, segundo alguns critérios determinados. Esses modelos permitem que se faça a modelagem de aplicações nas quais o processo de resolução dos problemas, representados por tais aplicações, parece se adequar às características dos agentes reativos. A validação do uso do ambiente SIMULA envolveu a modelagem de três aplicações: a atuação de robôs na busca de minerais, definido em [STE 90], o jogo PENGI, definido em [AGR 87] e [FER 91], e a atuação de parasitas no controle de pragas de plantações. Esta última sendo uma novidade para a resolução com agentes reativos. / Multiagent Systems is a subject of study in DAI (Distributed Artificial Intelligence) in which a group of agents interacts with the same tool . DAI is based on social behavior of agents (human and artificial ones) focus on actions and interactions of them . Those agents can be cognitive or reactive . Reactive Multiagent Systems have been used in research and studies linked to important fields of use that generates the development of systems not only for academic areas but also to meet the needs of industrial market. As we have the objective of ranging a research field of multiagent systems, this work comes up with a definition and implementation of a prototype of a software tool which enables the application development in reactive multiagent system . This tool has the purpose to ease the creation of such applications like the use of agents and consequently achieving a satisfactory result . The tool is called SIMULA and it enables the user to create his own applications through elements from a graphic interface . The user who interacts with the tool determines the agents involved in the problem and how they will act in the process of solving this matter . Applications are developed based in models created by the user. In order to characterize the tool SIMULA , three models of reactive multiagent systems found in [STE 90] , [DEM 93] and [FER 91] have been studied and analyzed. A comparative table has been made according to definite criteria. These models allow shaping the applications in which the process of problem solving represented by such applications seems adequate to the characteristics of reactive agents . Validation of use of SIMULA tool involved modeling of three applications : performance of robots in mineral prospection defined in [STE 90] , PENGI game defined in [AGR 87] and [FER 91] and perfomance of parasite in controlling crop plagues that is being a novelty.
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RESOURCE ALLOCATION IN SENSOR NETWORKS USING DISTRIBUTED CONSTRAINT OPTIMIZATIONChachra, Sumit, Elhourani, Theodore 10 1900 (has links)
International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / Several algorithms have been proposed for solving constraint satisfaction and the more general
constraint optimization problem in a distributed manner. In this paper we apply two such algorithms
to the task of dynamic resource allocation in the sensor network domain using appropriate
abstractions. The aim is to effectively track multiple targets by making the sensors coordinate with
each other in a distributed manner, given a probabilistic representation of tasks (targets). We present
simulation results and compare the performance of the DBA and DSA algorithms under varying
experimental settings.
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Decentralized Consensus Control of a Rigid-Body Spacecraft Formation with Communication DelayNazari, Morad, Butcher, Eric A., Yucelen, Tansel, Sanyal, Amit K. 04 1900 (has links)
The decentralized consensus control of a formation of rigid-body spacecraft is studied in the framework of geometric mechanics while accounting for a constant communication time delay between spacecraft. The relative position and attitude (relative pose) are represented on the Lie group SE(3) and the communication topology is modeled as a digraph. The consensus problem is converted into a local stabilization problem of the error dynamics associated with the Lie algebra se(3) in the form of linear time-invariant delay differential equations with a single discrete delay in the case of a circular orbit, whereas it is in the form of linear time-periodic delay differential equations in the case of an elliptic orbit, in which the stability may be assessed using infinite-dimensional Floquet theory. The proposed technique is applied to the consensus control of four spacecraft in the vicinity of a Molniya orbit.
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Autonomous intersection managementDresner, Kurt Mauro 24 August 2010 (has links)
Artificial intelligence research is ushering in an era of sophisticated,
mass-market transportation technology. While computers can fly a
passenger jet better than a human pilot, people still face the dangerous
yet tedious task of driving. Intelligent Transportation Systems (ITS) is
the field focused on integrating information technology with vehicles
and transportation infrastructure. Recent advances in ITS point to a
future in which vehicles handle the vast majority of the driving
task. Once autonomous vehicles become popular, interactions amongst
multiple vehicles will be possible. Current methods of vehicle
coordination will be outdated. The bottleneck for efficiency will no
longer be drivers, but the mechanism by which those drivers' actions are
coordinated.
Current methods for controlling traffic cannot exploit the superior
capabilities of autonomous vehicles. This thesis describes a novel approach
to managing autonomous vehicles at intersections that decreases the
amount of time vehicles spend waiting. Drivers and intersections in this
mechanism are treated as autonomous agents in a multiagent system. In
this system, agents use a new approach built around a detailed
communication protocol, which is also a contribution of the thesis. In
simulation, I demonstrate that this mechanism can significantly
outperform current intersection control technology-traffic signals and
stop signs.
This thesis makes several contributions beyond the mechanism and
protocol. First, it contains a distributed, peer-to-peer version of the
protocol for low-traffic intersections. Without any requirement of
specialized infrastructure at the intersection, such a system would be
inexpensive and easy to deploy at intersections which do not currently
require a traffic signal. Second, it presents an analysis of the
mechanism's safety, including ways to mitigate some failure
modes. Third, it describes a custom simulator, written for this work,
which will be made publicly available following the publication of the
thesis. Fourth, it explains how the mechanism is "backward-compatible"
so that human drivers can use it alongside autonomous vehicles. Fifth,
it explores the implications of using the mechanism at multiple proximal
intersections. The mechanism, along with all available
modes of operation, is implemented and tested in simulation, and I
present experimental results that strongly attest to the efficacy of
this approach. / text
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On the Trade-offs between Modeling Power and Algorithmic ComplexityYe, Chun January 2016 (has links)
Mathematical modeling is a central component of operations research. Most of the academic research in our field focuses on developing algorithmic tools for solving various mathematical problems arising from our models. However, our procedure for selecting the best model to use in any particular application is ad hoc. This dissertation seeks to rigorously quantify the trade-offs between various design criteria in model construction through a series of case studies. The hope is that a better understanding of the pros and cons of different models (for the same application) can guide and improve the model selection process.
In this dissertation, we focus on two broad types of trade-offs. The first type arises naturally in mechanism or market design, a discipline that focuses on developing optimization models for complex multi-agent systems. Such systems may require satisfying multiple objectives that are potentially in conflict with one another. Hence, finding a solution that simultaneously satisfies several design requirements is challenging. The second type addresses the dynamics between model complexity and computational tractability in the context of approximation algorithms for some discrete optimization problems. The need to study this type of trade-offs is motivated by certain industry problems where the goal is to obtain the best solution within a reasonable time frame. Hence, being able to quantify and compare the degree of sub-optimality of the solution obtained under different models is helpful. Chapters 2-5 of the dissertation focus on trade-offs of the first type and Chapters 6-7 the second type.
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Uma arquitetura para administração automatizada de ativos baseada em agentes competitivos. / An architecture for automated asset management based on competitive agents.Paulo André Lima de Castro 04 August 2009 (has links)
Além da capacidade de negociar, a atuação conjunta de agentes autônomos para resolver problemas complexos requer objetivos ao menos parcialmente compartilhados, de modo a tornar a cooperação atraente para tais agentes. Por outro lado, agentes competindo entre si podem trazer benefícios sociais, à medida que os mais aptos forem identificados e recursos sejam canalizados para aumentar sua relevância social e deste modo contribuir mais fortemente para a eficiência de todo o grupo. Dentro deste conceito, este trabalho apresenta uma arquitetura multiagentes que busca através da competição entre agentes com interesses comuns obter maior ganho social. Esta arquitetura, nomeada COAST, foi desenhada para o complexo ambiente que é a administração automatizada de ativos. Um sistema COAST é capaz de administrar uma carteira de ativos, utilizando um modelo explícito de perfil de investidor também proposto neste trabalho. Busca-se não apenas a maximização dos retornos, mas também um efetivo controle de risco e retorno, seguindo as restrições dadas pelo perfil do investidor. Um sistema de simulação de mercado financeiro, denominado AgEx, foi criado para facilitar o trabalho de desenvolver agentes para a administração de ativos e simular sua atuação através do uso de dados reais de negociações ocorridas em mercados financeiros. Foram realizadas várias simulações onde se pode observar que a arquitetura COAST se mostrou bastante eficaz no domínio escolhido / In order to solve complex problems using several autonomous agents it is necessary that these agents present negotiation skills and share some common goals to make cooperation attractive. On the other hand, competition among agents may bring some social benefits, since the most effective agents can be identified and more resources could be associated to them, thus contributing better to the whole society. According to this idea, this work presents a multiagent architecture that tries to explore competition among agents with commons goals, as a way to achieve better social results. The architecture was named COAST and it was designed to manage assets in an autonomous way. COAST is able to manage assets using an explicit investor profile model that is also proposed in this work. COAST architecture tries not only to maximize profits but also an effective risk and return control, according to an investor profile. A financial market simulation tool, called AgEx, was also implemented. This tool may facilitate the development of agents to automated asset management and simulate their acting in stock markets was also developed. We have performed many simulated experiments to validate COAST and the obtained results show that the use of COAST architecture in this domain is quite effective.
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Uma arquitetura para administração automatizada de ativos baseada em agentes competitivos. / An architecture for automated asset management based on competitive agents.Castro, Paulo André Lima de 04 August 2009 (has links)
Além da capacidade de negociar, a atuação conjunta de agentes autônomos para resolver problemas complexos requer objetivos ao menos parcialmente compartilhados, de modo a tornar a cooperação atraente para tais agentes. Por outro lado, agentes competindo entre si podem trazer benefícios sociais, à medida que os mais aptos forem identificados e recursos sejam canalizados para aumentar sua relevância social e deste modo contribuir mais fortemente para a eficiência de todo o grupo. Dentro deste conceito, este trabalho apresenta uma arquitetura multiagentes que busca através da competição entre agentes com interesses comuns obter maior ganho social. Esta arquitetura, nomeada COAST, foi desenhada para o complexo ambiente que é a administração automatizada de ativos. Um sistema COAST é capaz de administrar uma carteira de ativos, utilizando um modelo explícito de perfil de investidor também proposto neste trabalho. Busca-se não apenas a maximização dos retornos, mas também um efetivo controle de risco e retorno, seguindo as restrições dadas pelo perfil do investidor. Um sistema de simulação de mercado financeiro, denominado AgEx, foi criado para facilitar o trabalho de desenvolver agentes para a administração de ativos e simular sua atuação através do uso de dados reais de negociações ocorridas em mercados financeiros. Foram realizadas várias simulações onde se pode observar que a arquitetura COAST se mostrou bastante eficaz no domínio escolhido / In order to solve complex problems using several autonomous agents it is necessary that these agents present negotiation skills and share some common goals to make cooperation attractive. On the other hand, competition among agents may bring some social benefits, since the most effective agents can be identified and more resources could be associated to them, thus contributing better to the whole society. According to this idea, this work presents a multiagent architecture that tries to explore competition among agents with commons goals, as a way to achieve better social results. The architecture was named COAST and it was designed to manage assets in an autonomous way. COAST is able to manage assets using an explicit investor profile model that is also proposed in this work. COAST architecture tries not only to maximize profits but also an effective risk and return control, according to an investor profile. A financial market simulation tool, called AgEx, was also implemented. This tool may facilitate the development of agents to automated asset management and simulate their acting in stock markets was also developed. We have performed many simulated experiments to validate COAST and the obtained results show that the use of COAST architecture in this domain is quite effective.
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Avaliação de escalabilidade e desempenho da camada de transporte de mensagens em plataformas multiagente / Scalability and performance comparison between message transport systems of multiagent platformsRodrigues, Henrique Donâncio Nunes 12 August 2019 (has links)
Este trabalho reside no campo de sistemas multiagente (MAS) compostos por agentes inteligentes que são capazes de usar protocolos de comunicação da Internet. Uma plataforma multiagente é um software ou framework capaz de gerenciar múltiplos aspectos da execução de agentes e suas interações. Muitas plataformas MAS foram desenvolvidas nos últimos anos, todas elas compatíveis com padrões de desenvolvimento de sistemas interoperáveis em diferentes níveis. Nos últimos anos,novas linguagens de programação foram definidas e novos protocolos foram adotados para comunicação em sistemas distribuídos. Esses fatos também influenciaram a comunidade multiagente,com a proposição de novas plataformas para apoiar o desenvolvimento de sistemas multiagente. Além disso, a adoção de agentes como paradigma para o desenvolvimento de sistemas distribuídos complexos em larga escala é vista como uma solução interessante na era do grande volume de dados. Portanto, uma comparação entre as plataformas existentes e seu suporte para desenvolver e implantar com eficiência sistemas multiagente de grande escala pode beneficiar a comunidade de desenvolvedores interessada em escolher qual plataforma melhor se adapta a seus projetos. O objetivo deste trabalho é avaliar plataformas multiagente em relação à escalabilidade, desempenho e compatibilidade com outras tecnologias com o objetivo de facilitar a escolha do desenvolvedor que queira projetar Sistemas Multiagente de grande porte. A fim de escolher as plataformas MAS para a comparação proposta, são consideradas plataformas de código aberto que são ativamente utilizadas pela comunidade multiagente. Além disso, tais plataformas MAS devem ser capazes de oferecer uma implantação de forma distribuída, característica essencial de sistemas escaláveis. Depois de restringir a lista de plataformas MAS de acordo com esses critérios, são analisados os sistemas de transporte de mensagens utilizando benchmarks para análise de escalabilidade e desempenho, considerando diferentes cenários de comunicação. Por fim, é apresentado um cenário realístico onde um MAS escalável pode ser adotado como solução. / This work resides in the field of multiagent systems (MAS) composed of intelligent agents that are able to use Internet communication protocols. A multiagent platform is a software or framework capable of managing multiple aspects of the agent execution and their interactions. In the recent years, many MAS platforms have been developed, all of them compliant with interoperable system development standards at different levels. Also, new programming languages have been defined and new protocols have been adopted for communication in distributed systems. These facts also influenced the multiagent community with the proposition of new platforms to support the development of multiagent systems. In addition, the adoption of agents as a paradigm for the development of large scale complex distributed systems is seen as an interesting solution in the era of big data. Therefore, a comparison between existing platforms and their support for efficiently developing and deploying large scale multiagent systems can benefit the developer community interested in choosing which platform best fits their projects. The purpose of this work is evaluate multiagent platforms for scalability, performance and compatibility with other technologies in order to facilitate the choice of the developer that wants design large scale multiagent systems. In order to choose MAS platforms for the proposed comparison, are considered open source platforms that are actively used by the multiagent community. Moreover, these MAS platforms should be able to provide a deployment in a distributed manner, essential characteristic of scalable systems. After narrowing the list of MAS platforms according to these criteria, message transport systems are analyzed using benchmarks for scalability and performance comparison, considering different communication scenarios. Finally, a realistic scenario is presented where a scalable MAS can be adopted as a solution.
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Combining coordination mechanisms to improve performance in multi-robot teamsNasroullahi, Ehsan 09 March 2012 (has links)
Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can significantly improve coordination and system performance over either approach individually. First, we use difference evaluations (which aim to compute an agent's contribution to the team) and stigmergy to promote implicit coordination. Second, we introduce an explicit coordination mechanism dubbed Intended Destination Enhanced Artificial State (IDEAS), where an agent incorporates other agents' intended destinations directly into its state. The IDEAS approach does not require any formal negotiation between agents, and is based on passive information sharing. Finally, we combine these two approaches on a variant of a team-based multi-robot exploration domain, and show that agents using a both explicit and implicit coordination outperform other learning agents up to 25%. / Graduation date: 2012
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Addressing the Issues of Coalitions and Collusion in Multiagent SystemsKerr, Reid C. January 2013 (has links)
In the field of multiagent systems, trust and reputation systems are intended to assist agents in finding trustworthy partners with whom to interact. Earlier work of ours identified in theory a number of security vulnerabilities in trust and reputation systems, weaknesses that might be exploited by malicious agents to bypass the protections offered by such systems. In this work, we begin by developing the TREET testbed, a simulation platform that allows for extensive evaluation and flexible experimentation with trust and reputation technologies. We use this testbed to experimentally validate the practicality and gravity of attacks against vulnerabilities. Of particular interest are attacks that are collusive in nature: groups of agents (coalitions) working together to improve their expected rewards. But the issue of coalitions is not unique to trust and reputation; rather, it cuts across a range of fields in multiagent systems and beyond. In some scenarios, coalitions may be unwanted or forbidden; in others they may be benign or even desirable. In this document, we propose a method for detecting coalitions and identifying coalition members, a capability that is likely to be valuable in many of the diverse fields where coalitions may be of interest. Our method makes use of clustering in benefit space (a high-dimensional space reflecting how agents benefit others in the system) in order to identify groups of agents who benefit similar sets of agents. A statistical technique is then used to identify which clusters contain coalitions. Experimentation using the TREET platform verifies the effectiveness of this approach. A series of enhancements to our method are also introduced, which improve the accuracy and robustness of the algorithm. To demonstrate how this broadly-applicable tool can be used to address domain-specific problems, we focus again on trust and reputation systems. We show how, by incorporating our work into one such system (the existing Beta Reputation System), we can provide resistance to collusion. We conclude with a detailed discussion of the value of our work for a wide range of environments, including a variety of multiagent systems and real-world settings.
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