Nesta dissertação, o autor aborda a dificuldade de prever-se o desempenho dos recursos humanos em um processo de desenvolvimento de software em um ambiente de Programação Extrema (XP) (BECK, 2000) e propõe uma solução com potencial para minimizar esse problema. Especificamente, o problema, a ser tratado neste trabalho, consiste em melhorar as previsões dos gerentes de projeto – no âmbito do ambiente mencionado - com relação ao desempenho dos recursos humanos na geração de valor para o negócio. Tal valor para o negócio é alcançado através da implementação, por parte dos programadores, das diversas funcionalidades de um sistema de software. Para a construção da solução proposta neste trabalho, o autor analisou um sistema XP de desenvolvimento de software (composto por ambiente, pessoas e processo), conforme o processo de modelagem proposto por Streit (2006) e apoiado na revisão da literatura relevante. Em seguida, o autor estruturou esse sistema em um modelo conceitual para, finalmente, desenvolver um modelo computacional do sistema analisado, baseado em múltiplos agentes inteligentes modelados conforme a arquitetura Beliefs-Desires-Intentions (BDI), ou Crenças-Desejos-Intenções. O modelo computacional da simulação multi-agente foi desenvolvido com o apoio da ferramenta SeSAm (KLÜGL, 2006). Testado através da experimentação estatística 2k Fatorial (LAW e KELTON, 2000), o modelo de simulação multi-agente de processos de desenvolvimento de software, para ambientes de Programação Extrema, demonstrou eficácia e aplicabilidade prática sobre o problema em questão. / In this research, the author adresses the difficulty to forecast the performance of the human resources in a software development process in an Extreme Programming (XP) (BECK, 2000) environment and proposes a solution that may be suitable to minimize this problem. Specifically, the main problem consists on how to improve the assumptions of the project managers - in the aforementioned environment - related with the human resources performance in generating value for the business. This value generation is reached through the implementation, by programmers, of the various functionalities of a software system. To build the solution proposed in this research, the author analysed a XP software development system (composed of environment, people and process) considering the modeling process proposed by Streit (2006) and also the relevant related works. This system was later structured in a conceptual model and, in sequence, in a computational model based on the Beliefs-Desires-Intentions (BDI) architecture of intelligent agents. The computational model of the multi-agent simulation was build with the support of the SeSAm (KLÜGL, 2006) tool. The tests of the multi-agent simulation of XP software develoment process model used the 2k Factorial statistical experimentation (LAW e KELTON, 2000) and their results demonstrated the effectiveness and practical applicability of the model for the research problem.

Avec le développement des objets connectés, les agents embarqués déployés dans des environnements physiques et les applications multi-agents qui les impliquent deviennent de plus en plus populaires. Ces systèmes multi-agents sont amenés à partager le même environnement physique. Cette cohabitation d'agents de systèmes différents, qui n'ont pas nécessairement été prévus pour interagir entre eux par les concepteurs, les amène cependant à se solliciter. Un agent peut alors participer à la réalisation d'objectifs incompatibles avec les siens ou ceux de ses collectifs. Pour éviter ces situations, nous proposons un modèle d'agent basé sur les identités pour l'aider à décider de sa participation ou non à des actions collectives. / Embedded agents deployed in physical environments are increasingly interoperable, and are likely to coexist with agents of others systems in a same physical space. So, an agent needs to be able to cooperate with agents from other systems and to form coalitions with unfamiliar teammates. However, before committing to cooperate with others, an agent must take into account that it may get involved in the achievement of objectives that are incompatible with its own, with the global objectives of its system, or with those derived from its previously joined coalitions. To avoid such situations, we propose an identity-based cooperation mechanism. This mechanism involves creating and sustaining the agent identity, and a commitment process to reason about identities when solicited to participate in a collective trans-MAS action.

This dissertation studies the application of several event-driven control schemes in multi-agent systems. First, a new cooperative receding horizon (CRH) controller is designed and applied to a class of maximum reward collection problems. Target rewards are time-variant with finite deadlines and the environment contains uncertainties. The new methodology adapts an event-driven approach by optimizing the control for a planning horizon and updating it for a shorter action horizon. The proposed CRH controller addresses several issues including potential instabilities and oscillations. It also improves the estimated reward-to-go which enhances the overall performance of the controller. The other major contribution is that the originally infinite-dimensional feasible control set is reduced to a finite set at each time step which improves the computational cost of the controller. Second, a new event-driven methodology is studied for trajectory planning in multi-agent systems. A rigorous optimal control solution is employed using numerical solutions which turn out to be computationally infeasible in real time applications. The problem is then parameterized using several families of parametric trajectories. The solution to the parametric optimization relies on an unbiased estimate of the objective function's gradient obtained by the "Infinitesimal Perturbation Analysis" method. The premise of event-driven methods is that the events involved are observable so as to "excite" the underlying event-driven controller. However, it is not always obvious that these events actually take place under every feasible control in which case the controller may be useless. This issue of event excitation, which arises specially in multi-agent systems with a finite number of targets, is studied and addressed by introducing a novel performance measure which generates a potential field over the mission space. The effect of the new performance metric is demonstrated through simulation and analytical results.

Cette thèse propose une méthode pour coordonner flexiblement des Systèmes Multi-Agents (SMA). Plus en détails, nous étudions comment influencer des agents artificiels afin que, collectivement, ils atteignent des objectifs complexes et/ou dynamiques dans des environnements eux-aussi complexes et dynamiques (ex: un groupe de robots pour secourir les victimes lors d'un désastre, qui peut s'adapter à une grande variété de dangers, conditions climatiques, état des victimes).Dans ce but, nous avons d'abord étudié pourquoi, dans les sociétés humaines, les humains parviennent à coordonner relativement flexiblement mais pas leurs contreparties artificielles (agents des SMA). Cette opposition peut être grandement expliquée à l'aide d'un facteur clef : la culture. Les humains qui partagent un même bagage culturel se coordonnent flexiblement plus facilement, car ils ont une idée commune de ce que "travailler ensemble" veut dire. A contrario, les agents n'ont pas ce bagage et leurs échecs pour travailler ensemble s'apparente souvent à des chocs culturels.Ainsi, notre objectif consiste à répondre à la question suivante: peut-on utiliser une culture semblable à celle des humains comme un outil coordonner les SMA (et si oui, comment) ? Pour répondre à cette question, il nous faut d'abord expliquer : comment intégrer une culture semblable à celle des humains dans un SMA? Cette seconde question en soulève une troisième à étudier en premier : comment est-ce que la culture influence la manière dont la coordination se passe dans les sociétés humaines ?1- Nous montrons que de manière générale, la culture influence les décisions individuelles prises en situation d'interaction (ex: au travers d'attentes, de manière d'agir et de raisonner). Cette influence mène à l'occurrence de schémas d'interaction abstraits, récurrent et cohérents, qui, généralement, améliorent la performance collective. Ensuite, nous spécifions comment les principaux mécanismes l'influence connue de la culture (ex: importance culturelle accordée au pouvoir, aux règles) appliquent spécifiquement en situation de coordination (ex: la culture influence si les dirigeants donnent des ordres vs. des propositions à leurs subordonnés).2-Nous montrons comment répliquer les mécanismes l'influence de la culture sur la coordination dans les SMA. Tout d'abord, puisque la culture est fondée dans les décisions individuelles, nous mettons en avant un mécanisme de décision humain clef qui, à la fois, est sensible à la culture et influence la coordination. Ce mécanisme se trouve dans les valeurs, ce que les gens considèrent comme "bien" ou "important" (ex: honnêteté, discipline, autonomie). Ensuite, nous intégrons ces valeurs dans une architecture agent capable de prendre des décisions en situation de coordination. Enfin, nous illustrons que notre architecture peut en effet reproduire l'influence de la culture sur la coordination à travers de deux simulations qui répliquent des phénomènes culturels en situation de coordination connus.3-Nous étudions comment ces valeurs, inspirées des valeurs humaines, peuvent être utilisées coordonner des SMA. Tout d'abord, nous étudions pour quels problèmes les valeurs offrent un moyen opérationnel pour soutenir la coordination. A l'instar des sociétés humaines, les valeurs sont particulièrement offrent un haut niveau de flexibilité, quand les agents doivent raisonner eux-même pour établir une coordination. Puis, nous étudions les détails techniques à considérer pour utiliser en pratique des valeurs pour coordonner flexiblement des SMA (ex: quelles valeurs choisir ? Comment les représenter ?).En résumé, cette thèse met en évidence que les principaux mécanismes de l'influence de la culture sur la coordination (en particulier, grâce à l'influence de la culture sur les valeurs) peuvent être répliquées au sein des SMA. De plus, nous montrons que ces mécanismes peuvent être manipulés dans le but de coordonner des SMA. / This thesis proposes a method for supporting flexible coordination in multi-agent systems (MASs). In other words, we aim at influencing societies of artificial agents such that they can handle complex or evolving environments and collective goals (e.g. robots providing an emergency support capable of handling various hazards, climatic conditions, status of victims).Towards achieving this goal, we first investigated why in human societies, for which MASs can be seen as an ``artificial" counterpart, humans manage to coordinate relatively flexibly comparatively with artificial agents in MASs. We discovered that culture is a key factor of this relative success. Briefly, when humans share a cultural background, they manage to coordinate more flexibly because they share a common idea about what ``working together'' means. Conversely, artificial agents miss this aspect, leading in turn to coordination failures that can are similar to cultural clashes.The lack raises our goal: we want to better understand how culture can be integrated within and used for coordinating artificial societies. This goal raises the following research question: (how) can human-like culture be used as a tool for supporting coordination in artificial societies? As a preliminary step for answering this question, we need first to answer this question: (how) can the influence human-like cultures be integrated within artificial societies? In turn, this question raises a third one to be answered first: how does culture influence coordination in human societies?As a first step, we expand general theories of culture for conceptualizing its influence in the context of coordination. From a generic perspective, we explain that culture influences individual decisions that support matching expectations and coherent interaction patterns, leading in turn to (generally) better collective performance. From a more specific perspective, we specify how the core acknowledged patterns of the influence of culture (e.g. cultural importance given to power status, to rules) apply in the context of coordination (e.g. culture influences the likeliness that leaders are (made) responsible for making decisions for subordinates vs. proposing alternatives).As a second step, we study how to replicate human-like influences of culture on coordination within artificial societies. First, since culture is grounded within individual decisions, we investigate the core culturally-sensitive decision aspects that impact the most (flexible) coordination in human societies. We discover that values, what people consider as ``good'' or ``important'' (e.g. honesty, obedience, autonomy), constitute such an aspect by deeply supporting a wide range of (interaction-related) decisions. Then, for illustrating how to replicate influence of culture within artificial societies, we build an value-sensitive agent decision architecture that can make coordination-related decisions. Finally, we illustrate that our architecture can replicate the influence of culture on coordination through two simulations that replicate known coordination-related cultural phenomena.As a third step, we study how human-like values can be used for supporting coordination in artificial societies. First, we investigate the range of coordination problems for which values can offer an operational means for supporting coordination. As in human societies, values are particularly adequate for problems with complex and dynamic environments, requiring agents to make coordination-related decisions. Then, towards concretely implementing values, we study the technical details to consider when using values for supporting flexible coordination (e.g. how to concretely design values and integrating them within decision processes).

Engineering complex systems and New Product Development (NPD) are major challenges for contemporary engineering design and must be studied at three levels of: Products, Processes and Organizations (PPO). The science of complexity indicates that complex systems share a common characteristic: they are robust yet fragile. Complex and large scale systems are robust in the face of many uncertainties and variations; however, they can collapse, when facing certain conditions. This is so since complex systems embody many subtle, intricate and nonlinear interactions. If formal modelling exercises with available computational approaches are not able to assist designers to arrive at accurate predictions, then how can we immunize our large scale and complex systems against sudden catastrophic collapse? This thesis is an investigation into complex product design. We tackle the issue first by introducing a template and/or design methodology for complex product design. This template is an integrated product design scheme which embodies and combines elements of both design theory and organization theory; in particular distributed (spatial and temporal) problem solving and adaptive team formation are brought together. This design methodology harnesses emergence and innovation through the incorporation of massive amount of numerical simulations which determines the problem structure as well as the solution space characteristics. Within the context of this design methodology three design methods based on measures of complexity are presented. Complexity measures generally reflect holistic structural characteristics of systems. At the levels of PPO, correspondingly, the Immunity Index (global modal robustness) as an objective function for solutions, the real complexity of decompositions, and the cognitive complexity of a design system are introduced These three measures are helpful in immunizing the complex PPO from chaos and catastrophic failure. In the end, a conceptual decision support system (DSS) for complex NPD based on the presented design template and the complexity measures is introduced. This support system (IMMUNE) is represented by a Multi Agent Blackboard System, and has the dual characteristic of the distributed problem solving environments and yet reflecting the centralized viewpoint to process monitoring. In other words IMMUNE advocates autonomous problem solving (design) agents that is the necessary attribute of innovative design organizations and/or innovation networks; and at the same time it promotes coherence in the design system that is usually seen in centralized systems.

A Large Scale Robot Colony (LSRC) is a complex artifact comprising of a significant population of both mobile and static robots. LSRC research is in its literary infancy and it is therefore necessary to rely upon external fields for the appropriate framework, Multi Agent Systems (MAS) and Large Scale Systems (LSS). At the intersection of MAS, LSS and LSRC exist near identical issues, problems and solutions. If attention is paid to coherence then solution portability is possible. The issue of Self-Reliability is poorly addressed by the MAS research field. Disparity between the real world and simulation is another area of concern. Despite these deficiencies, MAS and LSS are perceived as the most appropriate frameworks. MAS research focuses on three prime areas, cognitive science, management and interaction. LSRC is focused on Self-Sustainability, Self-Management and Self-Organization. While LSS research was not primarily intended for populations of mobile robots, it does address key issues of LSRC, such as effective sustainability and management. Implementation of LSRC that is based upon the optimal solution for any one or two of the three aspects will be inferior to a coherent solution based upon all three. LSRC’s are complex organizations with significant populations of both static and mobile robots. The increase in population size and the requirement to address the issue of Self-Reliance give rise to new issues. It is no longer sufficient to speak only in terms of robot intelligence, architecture, interaction or team behaviour, even though these are still valid topics. Issues such as population sustainability and management have greater significance within LSRC. As the size of a robot populations increases, minor uneconomical decisions and actions inhibit the performance of the population. Interaction must be made economical within the context of the LSRC. Sustainability of the population becomes significant as it enables stable performance and extended operational lifespan. Management becomes significant as a mechanism to direct the population so as to achieve near optimal performance. The Self-Sustainability, Self-Management and Self-Organization of LSRC are vastly more complex than in team robotics. Performance of the overall population becomes more significant than individual or team achievement. This thesis is a presentation of the Cooperative Autonomous Robot Colony (CARC) architecture. The CARC architecture is novel in that it offers a coherent baseline solution to the issue of mobile robot Self-Reliance. This research uses decomposition as a mechanism to reduce problem complexity. Self-Reliance is decomposed into Self-Sustainability, Self-Management, and Self-Organization. A solution to the issue of Self-Reliance will comprise of conflicting sub-solutions. A product of this research is a set of guidelines that manages the conflict of sub-solutions and maintains a coherent solution. In addressing the issue of Self-Reliance, it became apparent that Economies of Scale, played an important role. The effects of Economies of Scale directed the research towards LSRC’s. LSRC’s demonstrated improved efficiency and greater capability to achieve the requirements of Self-Reliance. LSRC’s implemented with the CARC architecture would extend human capability, enabling large scale operations to be performed in an economical manner, within real world and real time environments, including those of a remote and hostile nature. The theory and architecture are supported using published literature, experiments, observations and mathematical projections. Contributions of this work are focused upon the three pillars of Self-Reliance addressed by CARC: Self-Sustainability, Self-Management and Self-Organization. The chapter on Self-Sustainability explains and justifies the relevance of this issue, what it is, why it is important and how it can be achieved. Self-Sustainability enables robots to continue to operate beyond disabling events by addressing failure and routine maintenance. Mathematical projections are used to compare populations of non-sustained and sustained robots. Computer modeling experiments are used to demonstrate the feasibility of Self-Sustainability, including extended operational life, the maintenance of optimal work flow and graceful physical degradation (GPD). A detailed explanation is presented of Sustainability Functions, Colony Sites, Static Robot Roles, Static Robot Failure Options, and Polymorphism. The chapter on Self-Management explores LSS research as a mechanism to exert influence over a LSRC. An experimental reactive management strategy is demonstrated. This strategy while limited does indicate promising potential directions for future research including the Man in the Loop (MITL) strategy highly desired by NASA JPL for off world command and control of a significant robot colony (Huntsberger, et. al., 2000). Experiments on Communication evaluate both Broadcast Conveyance (BC) and Message Passing Conveyance (MPC). These experiments demonstrate the potential of Message Passing as a low cost system for LSRC communication. Analysis of Metrics indicates that a Performance Based Feedback Method (PBFM) and a Task Achievement Method (TAM) are both necessary and sufficient to monitor a LSRC. The chapter on Self-Organization describes a number of experiments, algorithms and protocols on Reasoning Robotics, a minor variant of Reactive Robotics. Reasoning Robotics utilizes an Event Driven Architecture (EDA) rather than a Stimulus Driven Architecture (SDA) common to Reactive Robotics. Enhanced robot performance is demonstrated by a combination of EDA and environmental modification enabling stigmergy. These experiments cover Intersection Navigation with contingency for Multilane Intersections, a Radio Packet Controller (RPC) algorithm, Active and Passive Beacons including a communication protocol, mobile robot navigation using Migration Decision Functions (MDF’s), including MDF positional errors. The central issue addressed by this thesis is the production of Self-Reliance guidelines for LSRC’s. Self-Reliance is perceived as a critical issue in advancing the useful and productive applications for LSRC’s. LSRC’s are complex with many issues in related fields of MAS and LSS. Decomposition of Self-Reliance into Self-Sustainability, Self-Management and Self-Organization were used to aid in problem understanding. It was found that Self-Sustainability extends the operational life of individual robots and the LSRC. Self-Management enables the exertion of human influence over the LSRC, such that the ratio of humans to robots is reduced but not eliminated. Self-Organization achieves and enhances performance through a routine and reliable LSRC environment. The product of this research was the novel CARC architecture, which consists of a set of Self-Reliance guidelines and algorithms. The Self-Reliance guidelines manage conflict between optimal solutions and provide a framework for LSRC design. This research was supported by literature, experiments, observations and mathematical projections.

This thesis presents an approach to modeling Multi Agent Systems (MAS). A framework and its implementation are presented as an extension to .NET. A number of definitions of agents are evaluated for the purpose of a broad understanding of the term software agent. Software agent has been defined in MAS context and its characteristics are identified and implemented. Motivation factors for building framework for MAS have been discussed. A number of existing technologies are discussed and evaluated. A number of agent systems previously developed are also being discussed in the middle part of the thesis. A model software agent has been defined and its characteristics are divided in two basic categories essential and optional. Its implementation has been distributed into different components throughout the MAS framework. Some of these characteristics are jointly implemented by a number of components and others responsibility rest on the individual components. Detail working of the MAS framework (i.e. what to do, when to do) is explained as guide to develop MAS using MAS framework. The protocols followed by the framework components to make communication possible between them are discussed at components level. The required information for developing MAS using MAS framework are also discussed. It answers the why, when and how questions in regards to using MAS framework A case study on Dynamic Truck Scheduling (DTS) system is discussed, designed and implemented using the MAS framework. DTS System has been used as a prototype application to test and evaluate the framework. DTS also represents a model problem that can be answered by using MAS; complete in-depth details about the problem statement are discussed. It also discusses the design and implementation of the solution along with the test results of the framework. Possible future expansion is presented in light of a number of limitations known of the MAS framework. The code working behind the different components of the MAS framework is given in appendices. Some important standards of XML that are used to pass information between agents and MAS framework components are also given in the format of tables.

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

Psychological studies have shown that information exchange is a key component of effective teamwork. In addition to requesting information that they need for their tasks, members of effective teams often proactively forward information that they believe other teammates require to complete their tasks. We refer to this type of communication as proactive information exchange and the formalization and implementation of this is the subject of this thesis. The important question that we are trying to answer is: under normative conditions, what types of information needs can agent teammates extract from shared plans and how can they use these information needs to proactively forward information to teammates? In the following, we make two key claims about proactive information exchange: first, agents need to be aware of the information needs of their teammates and that these information needs can be inferred from shared plans; second, agents need to be able to model the beliefs of others in order to deliver this information efficiently. To demonstrate this, we have developed an algorithm named PIEX, which, for each agent on a team, reasonably approximates the information-needs of other team members, based on analysis of a shared team plan. This algorithm transforms a team plan into an individual plan by inserting coomunicative tasks in agents' individual plans to deliver information to those agents who need it. We will incorporate a previously developed architecture for multi-agent belief reasoning. In addition to this algorithm for proactive information exchange, we have developed a formal framework to both describe scenarios in which proactive information exchange takes place and to evaluate the quality of the communication events that agents running the PIEX algorithm generate. The contributions of this work are a formal and implemented algorithm for information exchange for maintaining a shared mental model and a framework for evaluating domains in which this type of information exchange is useful.

In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.