Virtual Software Corporations (VSCs) are a novel and important organisational form for large-scale software development. The increased complexity of this development environment requires the use of tools to support human actors in undertaking their tasks, which in turn require modelling solutions able to capture the VSC specific issues. One of the key aspects identified for software development in a VSC setting is the need to support co-ordination. One approach in the development of support for coordination in heterogeneous environments in respect to processes and support tools, such as VSCs, is the use of commitment management. The purpose of this thesis is to define a formalism suitable for capturing and managing commitments, as a means to support co-ordination. This is done by first analysing existing VSCs, and determining the requirements for co-ordination support. Consequently a formalism is defined to address the requirements. The formalism is based on a commitment modelling approach and deontic logic, a modal logic, which is used to manage the commitments. The defined formalism is the basis of a prototype support system, which is used for testing and evaluating. The evaluation has focused on identifying the level of support provided for the initial requirements. To this end three process examples have been used: the initial case study, the study of an independent VSC and the example of a desired process for software configuration management.The results indicate that the formalism, through the use of the prototype system, is able to represent and to manage commitments, as the most important issues in coordinating VSC software development. Thus it has a significant contribution as a modelling approach and it was shown to be applicable to realistic process scenarios.

abstract: This work describes an approach for distance computation between agents in a multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof- Arrival (AoA) data and local trajectory data. Each agent in the swarm is able to discretely determine distance and bearing to every other neighbor agent in the swarm. From this information, I propose a lightweight method for sensor coverage of an unknown area based on the work of Sameera Poduri. I also show that this technique performs well with limited calibration distances. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

Um subconjunto importante da pesquisa em sistemas multiagentes (SMA) baseiase no estudo das organizações. A organização define a estrutura do SMA e as regras que os agentes devem seguir, a fim de aumentar a eficiência do sistema. No entanto, dado um domínio, a escolha da organização que melhor resolve o problema ainda é uma questão sem resposta. Assim, abordagens empíricas para a avaliação de organizações são importantes, pois fornecem indícios valiosos sobre os custos e benefícios de diferentes configurações organizacionais, ajudando desenvolvedores e projetistas na definição da organização a ser adotada. Neste contexto, este trabalho, compara e avalia o impacto da mudança de parâmetros organizacionais no desempenho de um SMA, cujo objetivo é competir no cenário Agents on Mars proposto no Multi-Agent Programming Contest (MAPC). / An important subset of multi-agent systems (MAS) are based on the study of organizations. The organization defines the MAS structure and the rules which the agents must follow, increasing the MAS efficiency. Given an application domain, however, the choice of a particular organization that better solves the problem is still an open problem. Therefore, empirical approaches to the evaluation of organizations are important since they provide valuable evidences about the costs and benefits of different organizational settings, helping developers and designers to define the organization to be adopted. In this context, this work compares and evaluates the impact of organizational changes in the performance of a MAS, whose goal is to evolve in the \"Agents on Mars\" scenario proposed in the Multi-Agent Programming Contest (MAPC).

Um subconjunto importante da pesquisa em sistemas multiagentes (SMA) baseiase no estudo das organizações. A organização define a estrutura do SMA e as regras que os agentes devem seguir, a fim de aumentar a eficiência do sistema. No entanto, dado um domínio, a escolha da organização que melhor resolve o problema ainda é uma questão sem resposta. Assim, abordagens empíricas para a avaliação de organizações são importantes, pois fornecem indícios valiosos sobre os custos e benefícios de diferentes configurações organizacionais, ajudando desenvolvedores e projetistas na definição da organização a ser adotada. Neste contexto, este trabalho, compara e avalia o impacto da mudança de parâmetros organizacionais no desempenho de um SMA, cujo objetivo é competir no cenário Agents on Mars proposto no Multi-Agent Programming Contest (MAPC). / An important subset of multi-agent systems (MAS) are based on the study of organizations. The organization defines the MAS structure and the rules which the agents must follow, increasing the MAS efficiency. Given an application domain, however, the choice of a particular organization that better solves the problem is still an open problem. Therefore, empirical approaches to the evaluation of organizations are important since they provide valuable evidences about the costs and benefits of different organizational settings, helping developers and designers to define the organization to be adopted. In this context, this work compares and evaluates the impact of organizational changes in the performance of a MAS, whose goal is to evolve in the \"Agents on Mars\" scenario proposed in the Multi-Agent Programming Contest (MAPC).

A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake real-time environments, such as rescue missions, it is difficult to predict in advance what the requirements of the mission will be, what resources will be available, and how to optimally employ such resources. Yet, a fast response and speedy execution are critical to the outcome. This thesis proposes distributed optimisation techniques to tackle the following questions: how to maximise the number of assigned tasks in time restricted environments with limited resources; how to reach consensus on an execution plan across many agents, within a reasonable time-frame; and how to maintain robustness and optimality when factors change, e.g. the number of agents changes. Three novel approaches are proposed to address each of these questions. A novel algorithm is proposed to reassign tasks and free resources that allow the completion of more tasks. The introduction of a rank-based system for conflict resolution is shown to reduce the time for the agents to reach consensus while maintaining equal number of allocations. Finally, this thesis proposes an adaptive data-driven algorithm to learn optimal strategies from experience in different scenarios, and to enable individual agents to adapt their strategy during execution. A simulated rescue scenario is used to demonstrate the performance of the proposed methods compared with existing baseline methods.

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Reinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train intelligent agents when very little is known about the agent’s environment, and consequently the agent’s designer is unable to hand-craft an appropriate policy. Using reinforcement learning, the agent’s designer can merely give reward to the agent when it does something right, and the algorithm will craft an appropriate policy automatically. In many situations it is desirable to use this technique to train systems of agents (for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately, several significant computational issues occur when using this technique to train systems of agents. This dissertation introduces a suite of techniques that overcome many of these difficulties in various common situations. First, we show how multi-agent reinforcement learning can be made more tractable by forming coalitions out of the agents, and training each coalition separately. Coalitions are formed by using information-theoretic techniques, and we find that by using a coalition-based approach, the computational complexity of reinforcement-learning can be made linear in the total system agent count. Next we look at ways to integrate domain knowledge into the reinforcement learning process, and how this can signifi-cantly improve the policy quality in multi-agent situations. Specifically, we find that integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions when lack of training data would have prevented such convergence without domain knowledge. We then show how to train policies over continuous action spaces, which can reduce problem complexity for domains that require continuous action spaces (analog controllers) by eliminating the need to finely discretize the action space. Finally, we look at ways to perform reinforcement learning on modern GPUs and show how by doing this we can tackle significantly larger problems. We find that by offloading some of the RL computation to the GPU, we can achieve almost a 4.5 speedup factor in the total training process.

Little attention has been paid, in depth, to the relationship between fitness evaluation in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if these could be related it opens the way for implementation of distributed evolutionary systems via multi-agent architectures. Our investigation concentrates on the effectiveness with which social selection, in the form of reputation, can replace direct fitness observation as the selection bias in an evolutionary multi-agent system. We do this in two stages: In the first, we implement a peer-to-peer, adaptive Genetic Algorithm (GA), in which agents act as individual GAs that, in turn, evolve dynamically themselves in real-time, using the traditional evolutionary operators of fitness-based selection, crossover and mutation. In the second stage, we replace the fitness-based selection operator with a reputation-based one, in which agents choose their mates based on the collective past experiences of themselves and their peers. Our investigation shows that this simple model of distributed reputation can be successful as the evolutionary drive in such a system, exhibiting practically identical performance and scalability to direct fitness observation. Further, we discuss the effect of noise (in the form of “defective” agents) in both models. We show that the reputation-based model is significantly better at identifying the defective agents, thus showing an increased level of resistance to noise.

In this research, a multi-agent approach was applied to the design of a large axial flow compressor in order to optimise performance and to greatly enlarge the useful operating range of the machine. In this design a number of distributed software/hardware agents co-operate to control the internal geometry of the machine and thereby optimise the compressor characteristics in response to changes in flow conditions. The resulting machine is termed an ‘Intelligent Geometry Compressor’ (IGC). The design of a multi-agent system for the IGC was carried out in three main phases, each supported by computer simulation. In the first phase a steady-state model of the IGC was developed in which global control of the variable geometry is achieved by a single agent. This was used to help identify specific requirements for performance and the underlying parametric relationships. The subsequent phases incorporated additional agents into the machine design to meet these requirements. Initially, agents were deployed to optimise the settings of individual rows of stator vanes. In the final phase, the MAS was extended to incorporate agents into the machine design for the control of individual stator vanes. Simulation results were obtained which demonstrate the effectiveness of the intelligent geometry compressor in achieving delivery pressure regulation over a wide range of steady-state operating conditions whilst optimising overall machine efficiency and avoiding the occurrence of stall. Some of the implications for the physical design of an IGC arising from the MAS concept were briefly considered. The experience of the research supported by the specific results and observations from many simulation trials, led to the conclusion that multi-agent systems can provide an effective and novel alternative approach to the design of an intelligent geometry compressor. By implication, this conclusion may be extended to other intelligent machine applications where similar opportunity to apply a distributed control solution exists.

Natural disasters result in loss of lives, damage to built facilities, and interruption of businesses. The losses are not instantaneous rather they continue to occur until the community is restored to a functional socio-economic entity. Hence, it is essential that policy makers recognize this dynamic aspect of the incurring losses and make realistic plans to enhance the recovery. However, this cannot take place without understanding how homeowners react to recovery signals. These signals can come in different ways: from policy makers showing their strong commitment to restore the community by providing financial support and/or restoration of lifeline infrastructure; or from the neighbors showing their willingness to reconstruct. The goal of this research is to develop a model that can account for homeowners’ dynamic interactions in both organizational and spatial domains. Spatial domain of interactions focuses on how homeowners process signals from the environment such as neighbors reconstructing and local agencies restoring infrastructure, while organizational domain of interactions focuses on how agents process signals from other stakeholders that do not directly affect the environment like insurers. The hypothesis of this study is that these interactions significantly influence decisions to reconstruct and stay, or sell and leave. A multi-agent framework is used to capture emergent behavior such as spatial patterns and formation of clusters. The developed framework is illustrated and validated using experimental data sets.