The Steiner Tree problem is one of the most popular graph problems and has many application areas. The rectilinear version of this problem, introduced by Hanan, has taken special attention since it addresses a fundamental matter in &ldquo / Physical Design&rdquo / phase of the Very Large Scale Integrated (VLSI) Computer Aided Design (CAD) process. The Rectilinear Steiner Tree Problem asks for a minimum length tree that interconnects a given set of points by only horizontal and vertical line segments, enabling the use of extra points. There are various exact algorithms. However the problem is NP-complete hence approximation algorithms have to be used especially for large instances. In this thesis work, first a survey on heuristics for the Rectilinear Steiner Tree Problem is conducted and then two recently developed successful algorithms, BGA by Kahng et. al. and RST by Zhou have been studied and investigated deeply. Both algorithms have subproblems, most of which have individual backgrounds in literature. After an analysis of BGA and RST, the thesis proposes a modification on RST, which leads to a faster and non-recursive version. The efficiency of the modified algorithm has been validated by computational tests using both random and VLSI benchmark instances. A partially parallelized version of the modified algorithm is also proposed for distributed computing environments. It is implemented using MPI (message passing interface) middleware and the results of comparative tests conducted on a cluster of workstations are presented. The proposed distributed algorithm has also proved to be promising especially for large problem instances.

Distributed iterative algorithms are of great importance, as they are known to provide low-complexity and approximate solutions to what are otherwise high-dimensional intractable optimization problems. The theory of message-passing based algorithms is fairly well developed in the coding, machine learning and statistical physics literatures. Even though several applications of message-passing algorithms have already been identified, this work aims at establishing that a plethora of other applications exist where it can be of great importance. In particular, the goal of this work is to develop and demonstrate applications of this class of algorithms in network communications and computational biology. In the domain of communications, message-passing based algorithms provide distributed ways of inferring the optimal solution without the aid of a central agent for various optimization problems that happen in the resource allocation of communication networks. Our main framework is Affinity Propagation (AP), originally developed for clustering problems. We reinterpret this framework to unify the development of distributed algorithms for discrete resource allocation problems. Also, we consider a network-coded communication network, where continuous rate allocation is studied. We formulate an optimization problem with a linear cost function, and then utilize a Belief Propagation (BP) approach to determine a decentralized rate allocation strategy. Next, we move to the domain of computational biology, where graphical representations and computational biology play a major role. First, we consider the motif finding problem with several DNA sequences. In effect, this is a sequence matching problem, which can be modeled using various graphical representations and also solved using low-complexity algorithms based on message-passing techniques. In addition, we address the application of message-passing algorithms for a DNA sequencing problem where the one dimensional structure of a single DNA sequence is identified. We reinterpret the problem as being equivalent to the decoding of a nonlinear code. Based on the iterative decoding framework, we develop an appropriate graphical model which enables us to derive a message-passing algorithm to improve the performance of the DNA sequencing problem. Although this work consists of disparate application domains of communications, networks and computational biology, graphical models and distributed message-passing algorithms form a common underlying theme. / text

The Internet of Things (IoT) is a vision that aims at inter-connecting every physical identifiable object (or, a thing) via a global networking infrastructure (e.g., the legacy Internet). Several architectures are proposed to realize this vision; many of which agree that the IoT shall be considered as a global network of networks. These networks are used to manage wireless sensors, Radio Frequency IDentification (RFID) tags, RFID readers and other types of electronic devices and integrate them into the IoT. A major requirement of the IoT architectures is scalability, which is the capability of delivering high performance even if the input size (e.g., number of the IoT objects) is large. This thesis studies and proposes solutions to meet this requirement, and specifically focuses on the scalability issues found in the networks of the IoT. The thesis proposes several network formation algorithms to achieve these objectives, where a network formation algorithm is an algorithm that, if applied to a certain network, optimizes it to perform its tasks in a more efficient manner by virtually deleting some of its nodes and/or edges. The thesis focuses on three types of networks found in the IoT: 1) RFID readers coverage networks; whose main task is to cover (i.e., identify, monitor, track, sense) IoT objects located in a given area, 2) readers inter-communications networks; whose main task is to guarantee that their nodes are able to inter-communicate with each other and hence use their resources more efficiently (the thesis specifically considers inter-communication networks of readers using Bluetooth for communications), and 3) Object Name Systems (ONS) which are networks of several inter-connected database servers (i.e., distributed database) whose main task is to resolve an object identifier into an Internet address to enable inter-communication via the Internet. These networks are chosen for several reasons. For example, the technologies and concepts found in these networks are among the major enablers of the IoT. Furthermore, these networks solve tasks that are central to any IoT architecture. Particularly, the thesis a) studies the data and readers redundancy problem found in RFID readers coverage networks and introduces decentralized RFID coverage and readers collisions avoidance algorithms to solve it, b) contributes to the problem of forming multihop inter-communications networks of Bluetooth-equipped readers by proposing decentralized time-efficient Bluetooth Scatternet Formation algorithms, and c) introduces a geographic-aware ONS architecture based on Peer-To-Peer (P2P) computing to overcome weaknesses found in existing ONS architectures.

In this thesis we look at mobile agent solutions to black hole search and related problems. Mobile agents are computational entities that are autonomous, mobile, and can interact with their environment and each other. The black hole search problem is for a team of these agents to work together to map or explore a graph-like network environment where some elements of the network are dangerous to the agents. Most research into black hole search has focussed on finding a single dangerous node: a black hole. We look at the problem of finding multiple black holes and, in the case of dangerous graph exploration, multiple black links as well. We look at the dangerous graph exploration problem in the network model. The network model is based on a normal static computer network modelled as a simple graph. We give an optimal solution to the dangerous graph exploration problem using agents that start scattered on nodes throughout the network. We then make the problem more difficult by allowing an adversary to delete links during the execution of the algorithm and provide a solution using scattered agents. In the last decade or two, types of networks have emerged, such as ad hoc wireless networks, that are by their nature dynamic. These networks change quickly over time and can make distributed computations difficult. We look at black hole search in one type of dynamic network described by the subway model, which we base on urban subway systems. The model allows us to look at the cost of opportunistic movement by requiring the agents to move using carriers that follow routes among the network's sites, some of which are black holes. We show that there are basic limitations on any solution to black hole search in the subway model and prove lower bounds on any solution's complexity. We then provide two optimal solutions that differ in the agents' starting locations and how they communicate with one another. Our results provide a small window into the cost of deterministic distributed computing in networks that have dynamic elements, but which are not fully random.

We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.

The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected signals from networked sensor nodes. That is to say, the sensors receive local observations and transmit information between each other. Each sensor is capable of combining the collected information with its own observations to improve performance. In this thesis, we propose novel distributed methods for the inference applications using wireless sensor networks. In particular, the efficient algorithms which are not computationally intensive are investigated. Moreover, we present a number of novel algorithms for processing asynchronous network events and robust state estimation. In the first part of the thesis, a distributed adaptive algorithm based on the component-wise EM method for decentralized sensor networks is investigated. The distributed component-wise Expectation-Maximization (EM) algorithm has been designed for application in a Gaussian density estimation. The proposed algorithm operates a component-wise EM procedure for local parameter estimation and exploit an incremental strategy for network updating, which can provide an improved convergence rate. Numerical simulation results have illustrated the advantages of the proposed distributed component-wise EM algorithm for both well-separated and overlapped mixture densities. The distributed component-wise EM algorithm can outperform other EM-based distributed algorithms in estimating overlapping Gaussian mixtures. In the second part of the thesis, a diffusion based EM gradient algorithm for density estimation in asynchronous wireless sensor networks has been proposed. Specifically, based on the asynchronous adapt-then-combine diffusion strategy, a distributed EM gradient algorithm that can deal with asynchronous network events has been considered. The Bernoulli model has been exploited to approximate the asynchronous behaviour of the network. Compared with existing distributed EM based estimation methods using a consensus strategy, the proposed algorithm can provide more accurate estimates in the presence of asynchronous networks uncertainties, such as random link failures, random data arrival times, and turning on or off sensor nodes for energy conservation. Simulation experiments have been demonstrated that the proposed algorithm significantly outperforms the consensus based strategies in terms of Mean-Square- Deviation (MSD) performance in an asynchronous network setting. Finally, the challenge of distributed state estimation in power systems which requires low complexity and high stability in the presence of bad data for a large scale network is addressed. A gossip based quasi-Newton algorithm has been proposed for solving the power system state estimation problem. In particular, we have applied the quasi-Newton method for distributed state estimation under the gossip protocol. The proposed algorithm exploits the Broyden- Fletcher-Goldfarb-Shanno (BFGS) formula to approximate the Hessian matrix, thus avoiding the computation of inverse Hessian matrices for each control area. The simulation results for IEEE 14 bus system and a large scale 4200 bus system have shown that the distributed quasi-Newton scheme outperforms existing algorithms in terms of Mean-Square-Error (MSE) performance with bad data.

Uma importante parte de um sistema multiagente é o seu mecanismo de coordenação que permite que os agentes possam agir de maneira coesa em direção aos seus objetivos, sejam eles individuais ou coletivos. Um agente pode optar por cooperar para atingir um determinado objetivo que seria inalcançável através de ações individuais, para realizar uma tarefa de maneira mais eficiente ou simplesmente porque ele foi projetado para tal. Em todos os casos, a formação de coalizões (grupos de agentes que concordam em coordenar suas ações em torno de um objetivo comum) é uma questão fundamental. O problema de geração de estruturas de coalizão entre agentes (conjunto de todas as combinações de coalizões) é um tópico de pesquisa que recebeu muita atenção principalmente na resolução do problema quando considerado como um jogo de função característica, onde o valor das coalizões independe dos agentes que não estão presentes nela. Essa abordagem, apesar de ser indicada para muitos tipos de problema, não cobre toda a área de pesquisa do assunto, visto que em muitos casos a criação de uma coalizão irá afetar os demais agentes do sistema. Quando o sistema possui agentes com objetivos sobrepostos ou contrários, uma coalizão cujos recursos são destinados a completar tais objetivos irá influenciar as demais coalizões desse sistema. Essa influência se chama externalidade e, nesses casos, o problema de formação de estruturas de coalizão deve ser tratado como um jogo de partição. Apesar das pesquisas na área de jogos de partição serem recentes, elas trazem resultados promissores e há alguns poucos algoritmos já desenvolvidos para buscar soluções a esse problema. A busca pela melhor estrutura de coalizão geralmente demanda que seja calculado o valor de todas possíveis coalizões, a fim de se encontrar aquele conjunto cuja soma dos valores das coalizões forneça o melhor resultado. Esse processo requer um alto número de computações e de memória, devido à natureza exponencial do problema. Assim, ao invés de apenas um agente central realizar todas as operações, é mais eficiente do ponto de vista do uso de recursos computacionais distribuir essas operações entres os diversos agentes presentes no sistema. Além dos benefícios computacionais, distribuir o processo de busca pela melhor estrutura de coalizão permitiria trabalhar com questões como privacidade e tolerância a falhas, tendo em vista que as informações não estão concentradas em um único agente. Apesar disso, não há na literatura qualquer algoritmo capaz de solucionar o problema de geração de estrutura de coalizão em ambientes distribuídos e que sejam modelados como jogos de partição. A proposta desse trabalho é utilizar a fundamentação teórica existente acerca do problema de formação de estruturas de coalizão (modelados tanto como jogos de função característica quanto como jogos de partição) para criar um algoritmo distribuído capaz de encontrar a estrutura de coalizão ótima em ambientes que possuam externalidade. Esse algoritmo utiliza como base a ordenação das coalizões e dos agentes para permitir a distribuição do cálculo dos limites superiores e inferiores de cada coalizão. Após, esses valores são utilizados para se encontrar o subespaço mais provável de conter a estrutura de coalizão ótima. Com base nos experimentos, percebe-se que o algoritmo encontrou a estrutura de coalizão ótima buscando em apenas uma pequena parte do espaço de busca. Para os experimentos com 16 agentes, o algoritmo foi capaz de encontrar a solução ótima procurando em apenas 0,01% do espaço de busca. Também, é demonstrado que em cenários com externalidade negativa os agentes necessitaram investigar um espaço de busca menor para encontrar a estrutura de coalizão ótima que em cenários com externalidade positiva. Experimentos também demonstram que o algoritmo não consegue encontrar a estrutura de coalizão ótima quando há falhas na comunicação entre os agentes. / An important part of a multi-agent system is its coordination mechanism that allows the agents to act cohesively towards their goals, whether individual or collective. An agent can choose to cooperate to achieve a certain goal that would be unattainable through individual actions, to perform a task more efficiently or simply because it was designed to do so. In all cases, the formation of coalitions (group of agents that agree to coordinate their actions around a common goal) is a key issue. The problem of generating coalition structures between agents (set of all combinations of coalitions) is a research topic that has received much attention mostly on solving the problem when considered as a characteristic function game, where the value of coalitions is independent of agents that are not part of it. This approach, although suitable for many types of problem, does not cover the whole area of research on the subject, since in many cases the creation of a coalition will affect the other agents of the system. When the system has agents with overlapping goals or opposing goals, a coalition whose resources are devoted to completing these objectives will influence the other coalitions of that system. This influence is called externality, and in these cases, the problem of formation of coalition structures should be treated as a partition function game. Although research in the area of partition games is recent, it brings promising results and there are few algorithms already developed to find solutions to this problem. The search for the best coalition structure generally requires computation of the value of all possible coalitions in order to find the set that the sum of the values of the coalitions provides the best result. This process requires a large number of computations and memory due to the exponential nature of the problem. Hence, instead of just one central agent performing all operations, it is more efficient to distribute those operations among several agents. Besides the computational benefits, distributing the search process for the best coalition structure would address issues such as privacy and fault tolerance, given that the information is not concentrated in a single agent. Nevertheless, in the literature there is not algorithm capable of solving the problem of coalition structure generation in decentralized environments and modeled as partition function game. The purpose of this work is to use the existing theoretical foundations for solving the coalition structure generation problem (modeled both as a characteristic function game and as a partition function game) to create a distributed algorithm capable of finding the optimal coalition structure in environments that have externality. This algorithm uses as a base the ordering of coalitions and agents to distribute the calculation of the upper and lower limits for each coalition. Afterwards, these values are used to find the subspace more likely to contain the optimal coalition structure. Based on experiments, the algorithm found the optimal coalition structure searching only a small part of the search space. For the experiments with 16 agents, the algorithm was able to find the solution looking at just 0.0001%of the search space. Also, it is shown that in scenarios with negative externality agents need to investigate a smaller search space to find the optimal coalition structure than in scenarios with positive externality. Experiments also show that the algorithm can not find the optimal coalition structure when there are failures in the communication among the agents.

Uma importante parte de um sistema multiagente é o seu mecanismo de coordenação que permite que os agentes possam agir de maneira coesa em direção aos seus objetivos, sejam eles individuais ou coletivos. Um agente pode optar por cooperar para atingir um determinado objetivo que seria inalcançável através de ações individuais, para realizar uma tarefa de maneira mais eficiente ou simplesmente porque ele foi projetado para tal. Em todos os casos, a formação de coalizões (grupos de agentes que concordam em coordenar suas ações em torno de um objetivo comum) é uma questão fundamental. O problema de geração de estruturas de coalizão entre agentes (conjunto de todas as combinações de coalizões) é um tópico de pesquisa que recebeu muita atenção principalmente na resolução do problema quando considerado como um jogo de função característica, onde o valor das coalizões independe dos agentes que não estão presentes nela. Essa abordagem, apesar de ser indicada para muitos tipos de problema, não cobre toda a área de pesquisa do assunto, visto que em muitos casos a criação de uma coalizão irá afetar os demais agentes do sistema. Quando o sistema possui agentes com objetivos sobrepostos ou contrários, uma coalizão cujos recursos são destinados a completar tais objetivos irá influenciar as demais coalizões desse sistema. Essa influência se chama externalidade e, nesses casos, o problema de formação de estruturas de coalizão deve ser tratado como um jogo de partição. Apesar das pesquisas na área de jogos de partição serem recentes, elas trazem resultados promissores e há alguns poucos algoritmos já desenvolvidos para buscar soluções a esse problema. A busca pela melhor estrutura de coalizão geralmente demanda que seja calculado o valor de todas possíveis coalizões, a fim de se encontrar aquele conjunto cuja soma dos valores das coalizões forneça o melhor resultado. Esse processo requer um alto número de computações e de memória, devido à natureza exponencial do problema. Assim, ao invés de apenas um agente central realizar todas as operações, é mais eficiente do ponto de vista do uso de recursos computacionais distribuir essas operações entres os diversos agentes presentes no sistema. Além dos benefícios computacionais, distribuir o processo de busca pela melhor estrutura de coalizão permitiria trabalhar com questões como privacidade e tolerância a falhas, tendo em vista que as informações não estão concentradas em um único agente. Apesar disso, não há na literatura qualquer algoritmo capaz de solucionar o problema de geração de estrutura de coalizão em ambientes distribuídos e que sejam modelados como jogos de partição. A proposta desse trabalho é utilizar a fundamentação teórica existente acerca do problema de formação de estruturas de coalizão (modelados tanto como jogos de função característica quanto como jogos de partição) para criar um algoritmo distribuído capaz de encontrar a estrutura de coalizão ótima em ambientes que possuam externalidade. Esse algoritmo utiliza como base a ordenação das coalizões e dos agentes para permitir a distribuição do cálculo dos limites superiores e inferiores de cada coalizão. Após, esses valores são utilizados para se encontrar o subespaço mais provável de conter a estrutura de coalizão ótima. Com base nos experimentos, percebe-se que o algoritmo encontrou a estrutura de coalizão ótima buscando em apenas uma pequena parte do espaço de busca. Para os experimentos com 16 agentes, o algoritmo foi capaz de encontrar a solução ótima procurando em apenas 0,01% do espaço de busca. Também, é demonstrado que em cenários com externalidade negativa os agentes necessitaram investigar um espaço de busca menor para encontrar a estrutura de coalizão ótima que em cenários com externalidade positiva. Experimentos também demonstram que o algoritmo não consegue encontrar a estrutura de coalizão ótima quando há falhas na comunicação entre os agentes. / An important part of a multi-agent system is its coordination mechanism that allows the agents to act cohesively towards their goals, whether individual or collective. An agent can choose to cooperate to achieve a certain goal that would be unattainable through individual actions, to perform a task more efficiently or simply because it was designed to do so. In all cases, the formation of coalitions (group of agents that agree to coordinate their actions around a common goal) is a key issue. The problem of generating coalition structures between agents (set of all combinations of coalitions) is a research topic that has received much attention mostly on solving the problem when considered as a characteristic function game, where the value of coalitions is independent of agents that are not part of it. This approach, although suitable for many types of problem, does not cover the whole area of research on the subject, since in many cases the creation of a coalition will affect the other agents of the system. When the system has agents with overlapping goals or opposing goals, a coalition whose resources are devoted to completing these objectives will influence the other coalitions of that system. This influence is called externality, and in these cases, the problem of formation of coalition structures should be treated as a partition function game. Although research in the area of partition games is recent, it brings promising results and there are few algorithms already developed to find solutions to this problem. The search for the best coalition structure generally requires computation of the value of all possible coalitions in order to find the set that the sum of the values of the coalitions provides the best result. This process requires a large number of computations and memory due to the exponential nature of the problem. Hence, instead of just one central agent performing all operations, it is more efficient to distribute those operations among several agents. Besides the computational benefits, distributing the search process for the best coalition structure would address issues such as privacy and fault tolerance, given that the information is not concentrated in a single agent. Nevertheless, in the literature there is not algorithm capable of solving the problem of coalition structure generation in decentralized environments and modeled as partition function game. The purpose of this work is to use the existing theoretical foundations for solving the coalition structure generation problem (modeled both as a characteristic function game and as a partition function game) to create a distributed algorithm capable of finding the optimal coalition structure in environments that have externality. This algorithm uses as a base the ordering of coalitions and agents to distribute the calculation of the upper and lower limits for each coalition. Afterwards, these values are used to find the subspace more likely to contain the optimal coalition structure. Based on experiments, the algorithm found the optimal coalition structure searching only a small part of the search space. For the experiments with 16 agents, the algorithm was able to find the solution looking at just 0.0001%of the search space. Also, it is shown that in scenarios with negative externality agents need to investigate a smaller search space to find the optimal coalition structure than in scenarios with positive externality. Experiments also show that the algorithm can not find the optimal coalition structure when there are failures in the communication among the agents.

Uma importante parte de um sistema multiagente é o seu mecanismo de coordenação que permite que os agentes possam agir de maneira coesa em direção aos seus objetivos, sejam eles individuais ou coletivos. Um agente pode optar por cooperar para atingir um determinado objetivo que seria inalcançável através de ações individuais, para realizar uma tarefa de maneira mais eficiente ou simplesmente porque ele foi projetado para tal. Em todos os casos, a formação de coalizões (grupos de agentes que concordam em coordenar suas ações em torno de um objetivo comum) é uma questão fundamental. O problema de geração de estruturas de coalizão entre agentes (conjunto de todas as combinações de coalizões) é um tópico de pesquisa que recebeu muita atenção principalmente na resolução do problema quando considerado como um jogo de função característica, onde o valor das coalizões independe dos agentes que não estão presentes nela. Essa abordagem, apesar de ser indicada para muitos tipos de problema, não cobre toda a área de pesquisa do assunto, visto que em muitos casos a criação de uma coalizão irá afetar os demais agentes do sistema. Quando o sistema possui agentes com objetivos sobrepostos ou contrários, uma coalizão cujos recursos são destinados a completar tais objetivos irá influenciar as demais coalizões desse sistema. Essa influência se chama externalidade e, nesses casos, o problema de formação de estruturas de coalizão deve ser tratado como um jogo de partição. Apesar das pesquisas na área de jogos de partição serem recentes, elas trazem resultados promissores e há alguns poucos algoritmos já desenvolvidos para buscar soluções a esse problema. A busca pela melhor estrutura de coalizão geralmente demanda que seja calculado o valor de todas possíveis coalizões, a fim de se encontrar aquele conjunto cuja soma dos valores das coalizões forneça o melhor resultado. Esse processo requer um alto número de computações e de memória, devido à natureza exponencial do problema. Assim, ao invés de apenas um agente central realizar todas as operações, é mais eficiente do ponto de vista do uso de recursos computacionais distribuir essas operações entres os diversos agentes presentes no sistema. Além dos benefícios computacionais, distribuir o processo de busca pela melhor estrutura de coalizão permitiria trabalhar com questões como privacidade e tolerância a falhas, tendo em vista que as informações não estão concentradas em um único agente. Apesar disso, não há na literatura qualquer algoritmo capaz de solucionar o problema de geração de estrutura de coalizão em ambientes distribuídos e que sejam modelados como jogos de partição. A proposta desse trabalho é utilizar a fundamentação teórica existente acerca do problema de formação de estruturas de coalizão (modelados tanto como jogos de função característica quanto como jogos de partição) para criar um algoritmo distribuído capaz de encontrar a estrutura de coalizão ótima em ambientes que possuam externalidade. Esse algoritmo utiliza como base a ordenação das coalizões e dos agentes para permitir a distribuição do cálculo dos limites superiores e inferiores de cada coalizão. Após, esses valores são utilizados para se encontrar o subespaço mais provável de conter a estrutura de coalizão ótima. Com base nos experimentos, percebe-se que o algoritmo encontrou a estrutura de coalizão ótima buscando em apenas uma pequena parte do espaço de busca. Para os experimentos com 16 agentes, o algoritmo foi capaz de encontrar a solução ótima procurando em apenas 0,01% do espaço de busca. Também, é demonstrado que em cenários com externalidade negativa os agentes necessitaram investigar um espaço de busca menor para encontrar a estrutura de coalizão ótima que em cenários com externalidade positiva. Experimentos também demonstram que o algoritmo não consegue encontrar a estrutura de coalizão ótima quando há falhas na comunicação entre os agentes. / An important part of a multi-agent system is its coordination mechanism that allows the agents to act cohesively towards their goals, whether individual or collective. An agent can choose to cooperate to achieve a certain goal that would be unattainable through individual actions, to perform a task more efficiently or simply because it was designed to do so. In all cases, the formation of coalitions (group of agents that agree to coordinate their actions around a common goal) is a key issue. The problem of generating coalition structures between agents (set of all combinations of coalitions) is a research topic that has received much attention mostly on solving the problem when considered as a characteristic function game, where the value of coalitions is independent of agents that are not part of it. This approach, although suitable for many types of problem, does not cover the whole area of research on the subject, since in many cases the creation of a coalition will affect the other agents of the system. When the system has agents with overlapping goals or opposing goals, a coalition whose resources are devoted to completing these objectives will influence the other coalitions of that system. This influence is called externality, and in these cases, the problem of formation of coalition structures should be treated as a partition function game. Although research in the area of partition games is recent, it brings promising results and there are few algorithms already developed to find solutions to this problem. The search for the best coalition structure generally requires computation of the value of all possible coalitions in order to find the set that the sum of the values of the coalitions provides the best result. This process requires a large number of computations and memory due to the exponential nature of the problem. Hence, instead of just one central agent performing all operations, it is more efficient to distribute those operations among several agents. Besides the computational benefits, distributing the search process for the best coalition structure would address issues such as privacy and fault tolerance, given that the information is not concentrated in a single agent. Nevertheless, in the literature there is not algorithm capable of solving the problem of coalition structure generation in decentralized environments and modeled as partition function game. The purpose of this work is to use the existing theoretical foundations for solving the coalition structure generation problem (modeled both as a characteristic function game and as a partition function game) to create a distributed algorithm capable of finding the optimal coalition structure in environments that have externality. This algorithm uses as a base the ordering of coalitions and agents to distribute the calculation of the upper and lower limits for each coalition. Afterwards, these values are used to find the subspace more likely to contain the optimal coalition structure. Based on experiments, the algorithm found the optimal coalition structure searching only a small part of the search space. For the experiments with 16 agents, the algorithm was able to find the solution looking at just 0.0001%of the search space. Also, it is shown that in scenarios with negative externality agents need to investigate a smaller search space to find the optimal coalition structure than in scenarios with positive externality. Experiments also show that the algorithm can not find the optimal coalition structure when there are failures in the communication among the agents.

Orientador: Luiz Eduardo Buzato / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-17T02:18:31Z (GMT). No. of bitstreams: 1 Vieira_GustavoMacielDias_D.pdf: 1911190 bytes, checksum: fa6fc7e0d376225fabc7bb406c4d5aa1 (MD5) Previous issue date: 2010 / Resumo: Esta tese explora uma das ferramentas fundamentais para construção de sistemas distribuídos: a replicação de componentes de software. Especificamente, procuramos resolver o problema de como simplificar a construção de aplicações replicadas que combinem alto grau de disponibilidade e desempenho. Como ferramenta principal para alcançar o objetivo deste trabalho de pesquisa desenvolvemos Treplica, uma biblioteca de replicação voltada para construção de aplicações distribuídas, porém com semântica de aplicações centralizadas. Treplica apresenta ao programador uma interface simples baseada em uma especificação orientada a objetos de replicação ativa. A conclusão que defendemos nesta tese é que é possível desenvolver um suporte modular e de uso simples para replicação que exibe alto desempenho, baixa latência e que permite recuperação eficiente em caso de falhas. Acreditamos que a arquitetura de software proposta tem aplicabilidade em qualquer sistema distribuído, mas é de especial interesse para sistemas que não são distribuídos pela ausência de uma forma simples, eficiente e confiável de replicá-los / Abstract: This thesis explores one of the fundamental tools for the construction of distributed systems: the replication of software components. Specifically, we attempted to solve the problem of simplifying the construction of high-performance and high-availability replicated applications. We have developed Treplica, a replication library, as the main tool to reach this research objective. Treplica allows the construction of distributed applications that behave as centralized applications, presenting the programmer a simple interface based on an object-oriented specification for active replication. The conclusion we reach in this thesis is that it is possible to create a modular and simple to use support for replication, providing high performance, low latency and fast recovery in the presence of failures. We believe our proposed software architecture is applicable to any distributed system, but it is particularly interesting to systems that remain centralized due to the lack of a simple, efficient and reliable replication mechanism / Doutorado / Sistemas de Computação / Doutor em Ciência da Computação