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.

Degree: Master of Science in Engineering Department: Engineering / Multi-Agent Systems are becoming a popular paradigm for many engineering applications. However, there is still much research to be performed in this fast growing field. In this thesis, the effect of learning in multi-agent systems on communication and collaboration between agents is investigated. This research focuses on agents learning local cooperative behaviour from a centralised agent, as well as using learning to reduce the amount of communication between agents that use negotiation to achieve their goals. A simple test problem is formulated in MATLAB. The effect of learning is clearly seen to reduce the amount of communication between agents by up to 50%, while still maintaining cooperative behaviour. The agents are also demonstrated to learn to a large degree cooperative local behaviour from a central system.

Recurring traffic congestion is attributable to steadily increasing travel demand coupled with constrained space and financial resources for infrastructure expansion. Another major source of congestion is non-recurrent incidents that disrupt the normal operation of the infrastructure. Aiming to optimize the utilization of the transportation infrastructure, innovative infrastructure management techniques that incorporate on edge technological equipment and information systems need to be adopted to manage recurrent and non-recurrent congestion and reduce their adverse externalities. The framework presented in this thesis lays the foundation for multi-disciplinary semantic web based incident management. During traffic incident response, involved stakeholders will share their knowledge and resources, forming an ad-hoc framework within which each party will focus on its core competencies and cooperate to achieve a coherent incident management process. Negotiation between various response agencies operators is performed using intelligent software agents, alleviating the coordination and synchronization burden of the massive information flow during the incident response. The software agents provide a decision support to human operators based on the reasoning provided from the underlying system knowledge models. Ontological engineering is used to lay the foundation of the knowledge models, which are coded in a web based ontology language, allowing a decentralized access to various elements of the system. The whole system communication infrastructure is based on the Semantic Web technologies. The semantic web facilitates the use of, in an enhanced manner, the already existing web technologies as the communication infrastructure of the proposed system. Its semantic capabilities help to resolve the information and data interoperability issues among various parties. The web services concepts combined with the semantic web allow the direct exploration and access of knowledge models, resources, and data repertories held by various parties. The developed ontology along with the developed software system were tested and evaluated by domain experts and targeted system users. Based on the conducted evaluation, both the ontology and the software system were found to be promising tools in developing pervasive, collaborative and multi-disciplinary traffic incident management systems

Recurring traffic congestion is attributable to steadily increasing travel demand coupled with constrained space and financial resources for infrastructure expansion. Another major source of congestion is non-recurrent incidents that disrupt the normal operation of the infrastructure. Aiming to optimize the utilization of the transportation infrastructure, innovative infrastructure management techniques that incorporate on edge technological equipment and information systems need to be adopted to manage recurrent and non-recurrent congestion and reduce their adverse externalities. The framework presented in this thesis lays the foundation for multi-disciplinary semantic web based incident management. During traffic incident response, involved stakeholders will share their knowledge and resources, forming an ad-hoc framework within which each party will focus on its core competencies and cooperate to achieve a coherent incident management process. Negotiation between various response agencies operators is performed using intelligent software agents, alleviating the coordination and synchronization burden of the massive information flow during the incident response. The software agents provide a decision support to human operators based on the reasoning provided from the underlying system knowledge models. Ontological engineering is used to lay the foundation of the knowledge models, which are coded in a web based ontology language, allowing a decentralized access to various elements of the system. The whole system communication infrastructure is based on the Semantic Web technologies. The semantic web facilitates the use of, in an enhanced manner, the already existing web technologies as the communication infrastructure of the proposed system. Its semantic capabilities help to resolve the information and data interoperability issues among various parties. The web services concepts combined with the semantic web allow the direct exploration and access of knowledge models, resources, and data repertories held by various parties. The developed ontology along with the developed software system were tested and evaluated by domain experts and targeted system users. Based on the conducted evaluation, both the ontology and the software system were found to be promising tools in developing pervasive, collaborative and multi-disciplinary traffic incident management systems

The research described in this thesis is an attempt to utilize the Model Driven Architecture for semi-automatically developing a prototype Multi-Agent System to support the management of a real container terminal. Agent technology has been increasingly applied in Transport Logistics and seems to be a viable solution to support the container terminal management. Thus, from the user point of view, the focus of this research is to investigate the applicability of Multi-Agent Systems to assist the container terminal's decision makers in improving the container terminal productivity, which is often measured in terms of the productivity of cranes. A prototype Multi-Agent System has been developed to evaluate and compare a set of proposed vehicle dispatching strategies, which are a collection of rules that a vehicle (e.g. straddle carrier) uses to decide the priority of serving the working cranes. Employing an appropriate dispatching strategy may greatly improve the efficiency of vehicle allocation to the working cranes, so as to increase the utilization of cranes which directly enhance the container terminal productivity. In order to investigate the applicability of the Multi-Agent System for supporting the container terminal management, experiments have been conducted in a variety of real-world scenarios. The experiment results have revealed that Multi-Agent Systems are applicable to assist container terminal decision makers in evaluating operating strategies. On the other hand, from the developer point of view, the author investigates how to apply the Model Driven Architecture to agent technologies, providing a partially automated support for the derivation of Multi-Agent System implementation from the agent-oriented design, independently from the target implementation platforms. The Model Driven Architecture approach studied in this research is a model-driven software development process that explicitly separates models at three different levels of abstraction: platform independent models, platform specific models, and implementation models. In contrast to the conventional code-centric software development, the Model Driven Architecture based software development uses models as the primary engineering artifacts. The adopted development approach is to take a high-level abstraction model of a system and transform it into a set of platform specific models, each of which is in turn transformed into the corresponding implementation. Transformations between models are automatically carried out by a set of transformation tools. The experience of using the Model Driven Architecture for the development of the prototype Multi-Agent System has revealed the following benefits: (a) automated transformations between models increase software productivity; (b) separating the high-level specification of the system from the underlying implementation technology improves the portability of the system's high-level abstraction model; (c) strong separation of concerns, guaranteed consistency between models, and automatic generation of source code minimize future software maintenance effort.

The research described in this thesis is an attempt to utilize the Model Driven Architecture for semi-automatically developing a prototype Multi-Agent System to support the management of a real container terminal. Agent technology has been increasingly applied in Transport Logistics and seems to be a viable solution to support the container terminal management. Thus, from the user point of view, the focus of this research is to investigate the applicability of Multi-Agent Systems to assist the container terminal's decision makers in improving the container terminal productivity, which is often measured in terms of the productivity of cranes. A prototype Multi-Agent System has been developed to evaluate and compare a set of proposed vehicle dispatching strategies, which are a collection of rules that a vehicle (e.g. straddle carrier) uses to decide the priority of serving the working cranes. Employing an appropriate dispatching strategy may greatly improve the efficiency of vehicle allocation to the working cranes, so as to increase the utilization of cranes which directly enhance the container terminal productivity. In order to investigate the applicability of the Multi-Agent System for supporting the container terminal management, experiments have been conducted in a variety of real-world scenarios. The experiment results have revealed that Multi-Agent Systems are applicable to assist container terminal decision makers in evaluating operating strategies. On the other hand, from the developer point of view, the author investigates how to apply the Model Driven Architecture to agent technologies, providing a partially automated support for the derivation of Multi-Agent System implementation from the agent-oriented design, independently from the target implementation platforms. The Model Driven Architecture approach studied in this research is a model-driven software development process that explicitly separates models at three different levels of abstraction: platform independent models, platform specific models, and implementation models. In contrast to the conventional code-centric software development, the Model Driven Architecture based software development uses models as the primary engineering artifacts. The adopted development approach is to take a high-level abstraction model of a system and transform it into a set of platform specific models, each of which is in turn transformed into the corresponding implementation. Transformations between models are automatically carried out by a set of transformation tools. The experience of using the Model Driven Architecture for the development of the prototype Multi-Agent System has revealed the following benefits: (a) automated transformations between models increase software productivity; (b) separating the high-level specification of the system from the underlying implementation technology improves the portability of the system's high-level abstraction model; (c) strong separation of concerns, guaranteed consistency between models, and automatic generation of source code minimize future software maintenance effort.

Cette thèse sur travaux, composée de quatre articles, s’intéresse à la dynamique de la diffusion des connaissances entre les acteurs internes d’un pôle de compétitivité français au travers de modélisations multi-agents individu-centrées. Les trois premiers articles constituent un ensemble de réflexions théoriques sur la diffusion inter organisationnelle des connaissances et, comme prolongement, le quatrième article intègre des spécificités d’un pôle de compétitivité. L’article 1 propose une extension dynamique de l’impact de la capacité d’absorption de Sakakibara (2003) et d’Egbetokun et Savin (2012) sur le stock de connaissances d’entreprises partenaires. L’article 2 porte sur des acteurs qui essayent de minimiser le déficit causé par une perception erronée des interactions de deux projets dans lesquels ils investissent régulièrement. Nous avons travaillé sur un grand nombre de décideurs partageant de l’information avec leurs voisins les plus proches. L’article 3 effectue une extension du modèle de l’article 1 en intégrant la mauvaise perception des informations échangées entre acteurs et étudie son impact sur l’accumulation de connaissances. L’article 4, transpose le modèle précédent en y intégrant la notion de confiance et certaines spécificités des pôles de compétitivité. / This thesis, consisting of four articles, deals with the dynamics of the diffusion of knowledge among the internal stakeholders of a French competitiveness cluster through multi-agent modeling. The first three articles are a set of theoretical reflections on inter-organizational knowledge sharing and, as an extension of this, the fourth article includes the specifics of a cluster. Article number 1 provides a dynamic extension of the impact of the absorptive capacity of Sakakibara (2003) and Egbetokun and Savin (2012) on the stock of knowledge of partner companies. Article number 2 focuses on players who try to minimize the deficit caused by a misperception of interactions of two projects in which they invest regularly. It analyzes a large number of decision makers sharing information with their closest neighbors. Article number 3 carries out an extension of the model of article number 1 based on article number 2, incorporating misperception of information exchanged between stakeholders and examines its impact on the accumulation of knowledge. The last article transposes the previous model by incorporating the notion of trust and certain specificities of French competitiveness clusters.

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Internet computing enables the construction of large-scale and complex applications by aggregating and sharing computational, data and other resources across institutional boundaries. The agent model can address the ever-increasing challenges of scalability and complexity, driven by the prevalence of Internet computing, by its intrinsic properties of autonomy and reactivity, which support the flexible management of application execution in distributed, open, and dynamic environments. However, the non-deterministic behaviour of autonomous agents leads to a lack of control, which complicates exception management in the system, thus threatening the robustness and reliability of the system, because improperly handled exceptions may cause unexpected system failure and crashes. / In this dissertation, we investigate and develop mechanisms to integrate intrinsic support for concurrency control, exception handling, recoverability, and robustness into multi-agent systems. The research covers agent specification, planning and scheduling, execution, and overall coordination, in order to reduce the impact of environmental uncertainty. Simulation results confirm that our model can improve the robustness and performance of the system, while relieving developers from dealing with the low level complexity of exception handling. / A survey, along with a taxonomy, of existing proposals and approaches for building robust multi-agent systems is provided first. In addition, the merits and limitations of each category are highlighted. / Next, we introduce the ARTS (Agent-Oriented Robust Transactional System) platform which allows agent developers to compose recursively-defined, atomically-handled tasks to specify scoped and hierarchically-organized exception-handling plans for a given goal. ARTS then supports automatic selection, execution, and monitoring of appropriate plans in a systematic way, for both normal and recovery executions. Moreover, we propose multiple-step backtracking, which extends the existing step-by-step plan reversal, to serve as the default exception handling and recovery mechanism in ARTS. This mechanism utilizes previous planning results in determining the response to a failure, and allows a substitutable path to start, prior to, or in parallel with, the compensation process, thus allowing an agent to achieve its goals more directly and efficiently. ARTS helps developers to focus on high-level business logic and relaxes them from considering low-level complexity of exception management. / One of the reasons for the occurrence of exceptions in a multi-agent system is that agents are unable to adhere to their commitments. We propose two scheduling algorithms for minimising such exceptions when commitments are unreliable. The first scheduling algorithm is trust-based scheduling, which incorporates the concept of trust, that is, the probability that an agent will comply with its commitments, along with the constraints of system budget and deadline, to improve the predictability and stability of the schedule. Trust-based scheduling supports the runtime adaptation and evolvement of the schedule by interleaving the processes of evaluation, scheduling, execution, and monitoring in the life cycle of a plan. The second scheduling algorithm is commitment-based scheduling, which focuses on the interaction and coordination protocol among agents, and augments agents with the ability to reason about and manipulate their commitments. Commitment-based scheduling supports the refactoring and parallel execution of commitments to maximize the system's overall robustness and performance. While the first scheduling algorithm needs to be performed by a central coordinator, the second algorithm is designed to be distributed and embedded into the individual agent. / Finally, we discuss the integration of our approaches into Internet-based applications, to build flexible but robust systems. Specifically, we discuss the designs of an adaptive business process management system and of robust scientific workflow scheduling.

Sharing common goals and acting cooperatively are critical issues in multiagent teamwork. Traditionally, agents cooperate with each other by inferring others' actions implicitly or explicitly, based on established norms for behavior or on knowledge about the preferences or interests of others. This kind of cooperation either requires that agents share a large amount of knowledge about the teamwork, which is unrealistic in a distributed team, or requires high-frequency message exchange, which weakens teamwork efficiency, especially for a team that may involve human members. In this research, we designed and developed a new approach called Proactive Communication, which helps to produce realistic behavior and interactions for multiagent teamwork. We emphasize that multi-agent teamwork is governed by the same principles that underlie human cooperation. Psychological studies of human teamwork have shown that members of an effective team often anticipate the needs of other members and choose to assist them proactively. Human team members are also naturally capable of observing the environment and others so they can establish certain parameters for performing actions without communicating with others. Proactive Communication endows agents with observabilities and enables agents use them to track others’ mental states. Additionally, Proactive Communication uses statistical analysis of the information production and need of team members and uses these data to capture the complex, interdependent decision processes between information needer and provider. Since not all these data are known, we use their expected values with respect to a dynamic estimation of distributions. The approach was evaluated by running several sets of experiments on a Multi- Agent Wumpus World application. The results showed that endowing agents with observability decreased communication load as well as enhanced team performance. The results also showed that with the support of dynamic distributions, estimation, and decision-theoretic modeling, teamwork efficiency were improved.