Le travail présenté dans cette thèse définit une nouvelle approche pour la modélisation et la conceptualisation des processus métiers dans les entreprises afin de construire des outils logiciels d'assistance intelligents qui prennent en charge ces processus. L'approche proposée définit une ontologie dédiée à l'étude des organisations, nommée K-CRIO. Elle est décrite à l'aide du langage de représentation des connaissances OWL. Afin d'illustrer nos travaux, un système d'assistance a été implanté sur la base des résultats issus de la modélisation et de la conceptualisation d'un processus métier spécifique avec l'ontologie K-CRIO. Ce système prend la forme d'une application Web qui intègre et exploite pleinement les avantages des systèmes multiagents. L'ontologie K-CRIO est une ontologie dédiée à l'étude des organisations et à l'analyse organisationnelle des processus métiers qu'elles mettent en oeuvre. Plus précisément, elle est utilisée pour comprendre, analyser et raisonner sur ces organisations. Les organisations visées sont celles composées d'acteurs humains impliqués tout au long de la conception de produits et, pour ce faire, organisés selon un processus métier. L'éventail de ce type d'organisations est assez large. Nous avons donc limité notre étude aux organisations qui produisent des logiciels comme objectif final du processus. Dans ce contexte, l'ontologie K-CRIO peut être utilisée pour modéliser la structure organisationnelle du processus et les activités qui en résultent. Cette ontologie peut ensuite être exploitée afin de concevoir des outils d'assistance à la mise en oeuvre des processus ciblés au sein des organisations décrites. Plus précisément, l'ontologie fournit des moyens de raisonnement, d'annotation des ressources, et de suivi des processus de conception, permettant des recherches et de proposer pro-activement des conseils et des contenus appropriés. Afin d'illustrer l'utilisation de K-CRIO, nous appliquons K-CRIO sur deux processus différents: le modèle en cascade et la méthodologie Scrum. Ces exemples sont des processus de développement de logiciels classiques. En outre, pour le processus Scrum, qui est un processus agile de développement de logiciel, largement utilisé dans les entreprises de logiciels, nous avons conçu et développé un outil d'assistance intelligent. Cet outil contribue principalement à aider les Scrum Masters en leur fournissant des indicateurs pour les assister dans leurs prises de décisions ainsi que par la constitution d'une base de connaissances sur les activités / The work presented in this PhD thesis defines a new approach for the modeling and the conceptualization of enterprise business processes in the perspective of building intelligent assistance software tools to support these processes. The proposed approach defines an organizational ontology, named K-CRIO. Its description is based on the Ontology Web Language. To illustrate our work, an intelligent assistance system has been designed and implemented according to the result from the modeling and conceptualization of a specific business process with the K-CRIO Ontology. It is a web-based application that integrates and takes full advantage of multi-agent systems.The K-CRIO Ontology is an Ontology dedicated to the study of organizations and the analysis of business processes adopting an organizational point of view. Specifically, it is used to understand, analyze and reason about organizations and the processes they implement. The targeted organizations are those composed of entities involved throughout products' design and, to do so, following a defined business process. The range of this type of organizations is quite wide. We have thus limited our study to organizations that produce software as the final process goal, specifically IT enterprises delivering software products or services. In this context, the K-CRIO ontology could be used to model structure of the considered organizations and model human activities appearing in their business processes. This ontology could be used to support process assistance within the described organizations. More specifically, the ontology could provide means for reasoning, annotating resources, monitoring design processes, enabling searches and pro-actively proposing tips and proper content. In order to illustrate the usage of K-CRIO, we apply K-CRIO on two different processes: the Waterfall Model and the Scrum methodology. These examples are both classical software-development processes. Moreover, for Scrum, the famous agile software-development process widely used in software enterprises, we have designed and developed an intelligent assistance tool. This tool mainly helps Scrum Masters to make decision by monitoring Scrum project teams' activities within their various projects and collecting knowledge about these activities.

對於現有文獻中討論的預測市場模型，嘗試加入風險與聲譽變數，觀察與分析其成效，並參考文獻中的代理人系統實驗方法，對論文中相關部分進行修正、設計並模擬之預測市場模型。 / In this research, we proposed two variables that could be incorporated with prediction markets: Reputation and Risk. Instead of attracting new players, The reputation system could stop losing bankrupted player, Player willing to help bankrupted player will gain reputation, and bankrupted player will lose reputation. Previous works suggest longshot bias is related to the risk-neutrality of players. Our approach is to experiment dierent risk distribution. We observe the impact of these variables in an agent-based model of prediction markets. We use zero-intelligence agents, where human qualities such as maximizing prot, learning or obeserving are missing. We further discuss the result, and the impact of risk and reputation.

All-electric ship power systems have limited generation capacity and finite rotating inertia compared with large power systems. Moreover, all-electric ship power systems include large portions of nonlinear loads and dynamic loads relative to the total power capacity, which may significantly reduce the stability margin. Pulse loads and other high-energy weapon loads in the system draw a large amount of power intermittently, which may cause significant frequency and voltage oscillations in the system. Thus, an effective real-time load management technique is needed to dynamically balance the load and generation to operate the system normally. Multi-agent systems, inspired by biological phenomena, aim to cooperatively achieve system objectives that are difficult to reach by a single agent or centralized controller. Since power systems include various electrical components with different dynamical systems, conventional homogeneous multi-agent system cooperative controllers have difficulties solving the real-time load management problem with heterogeneous agents. In this dissertation, a novel heterogeneous multi-agent system cooperative control methodology is presented based on artificial potential functions and reduced-order agent models to cooperatively achieve real-time load management for all-electric ship power systems. The technique integrates high-order system dynamics and various kinds of operational constraints into the multi-agent system, which improves the accuracy of the cooperative controller. The multi-agent system includes a MVAC multiagent system and a DC zone multi-agent, which are coordinated by an AC-DC communication agent. The developed multi-agent system framework and the notional all-electric ship power system model were simulated in PSCAD software. Case studies and performance analysis of the MVAC multi-agent system and the DC zone multi-agent system were performed. The simulation results indicated that propulsion loads and pulse loads can be successfully coordinated to reduce the impact of pulse loads on the power quality of all-electric ship power systems. Further, the switch status or power set-point of loads in DC zones can be optimally determined to dynamically balance the generation and load while satisfying the operational constraints of the system and considering load priorities. The method has great potential to be extended to other isolated power systems, such as microgrids.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

In this thesis, we discuss models for multi-agent dynamical systems. We study the tracking/migration problem for flocks and a theoretical framework for design and analysis of flocking algorithm is presented. The interactions between agents in the systems are denoted by potential functions that act as distance functions, hence, the design of proper potential functions are crucial in modelling and analyzing the flocking problem for multi-agent dynamical systems. Constructions for both non-smooth potential functions and smooth potential functions with finite cut-off are investigated in detail. The main contributions of this thesis are to extend the literature of continuous flocking models with impulsive control and delay. Lyapunov function techniques and techniques for stability of continuous and impulsive switching system are used, we study the asymptotic stability of the equilibrium of our models with impulsive control and discovery that by applying impulsive control to Olfati-Saber's continuous model, we can remove the damping term and improve the performance by avoiding the deficiency caused by time delay in velocity sensing. Additionally, we discuss both free-flocking and constrained-flocking algorithm for multi-agent dynamical system, we extend literature results by applying velocity feedbacks which are given by the dynamical obstacles in the environment to our impulsive control and successfully lead to flocking with obstacle avoidance capability in a more energy-efficient way. Simulations are given to support our results, some conclusions are made and future directions are given.

In this thesis, I propose an agent-based cooperative model for supply chains to commit orders by satisfying constraints. Due to the limitation of the real world environment, the centralized schedule model to handle constraint satisfaction is impractical, it is important to excise the distributed constraint satisfaction model to meet the outsourcing paradigm of supply chain management. I introduce a multi-agent system based coordination mechanism that integrates theories of negotiation and distributed constraint satisfaction problem to resolve the constraints in supply chain. I adopt the asynchronous weak-commitment search, a DCSP algorithm to resolve the global constraint in supply chain. Asynchronous weak-commitment search is complete backtracking algorithms that guarantee to find a solution if there is a solution existing and asynchronous weak-commitment search provide priority dynamic mechanism that help us to find a solution quickly than other backtracking algorithms. We construct a coordination agent for each business entity in supplier chain. The agent embedded in the ability to resolve the constraints autonomously. We expect this agent-based coordination mechanism can make supply chain more efficient and enhance supply chain's agility.

In the thesis, we provide a solution to solve a schema change problem. In a data warehouse system, if schema changes occur in a data source, the overall system will lose the consistency between the data sources and the data warehouse. These schema changes will render the data warehouse obsolete. We have developed three stages to handle schema changes occurring in databases. They are change detection, diagnosis, and handling. Recommendations are generated by DB-agent to information DW-agent to notify the DBA what and where a schema change affects the star schema. In the study, we mainly handle seven schema changes in a relational database. All of them, we not only handle non-adding schema changes but also handling adding schema changes. A non-adding schema change in our experiment has high correct mapping rate as using a traditional mappings between a data warehouse and a database. For an adding schema change, it has many uncertainties to diagnosis and handle. For this reason, we compare similarity between an adding relation or attribute and the ontology concept or concept attribute to generate a good recommendation. The evaluation results show that the proposed approach is capable to detect these schema changes correctly and to recommend the DBA about the changes appropriately.

With rapid development of information technology, the company can find and communicate with new partners easily, which makes the relation more complex and unstable. How to improve the performance of the supply chain is an important issue. In this study, we try to discuss the problem from the three factors including switching cost, trust and information sharing. First, we propose a conceptual model of switching cost, trust, information sharing and supply chain performance. The performance indexes include the average fulfillment rate, average stock cost, and average cycle time. Second, in order to verify the model, we construct a simulation environment of supply chain and construct a decision mechanism based on the conceptual model. Final, we observe the effect and variation of the different order demand environment, the different information sharing threshold, different propensity of switching cost and trust, and different product price and different producing technology.

Heterogeneous multi-agent systems are currently used in a wide variety of situations, including search and rescue, military applications, and off-world exploration, however it is difficult to understand the actions of these systems or naturalistically assign these mixed teams to tasks. These agents, which may be human, robot or software, have different capabilities but will need to coordinate effectively with humans in order to operate. The first and largest contributing factor to this challenge is the processing, understanding and representing of elements of the natural world in a manner that can be utilized by artificial agents. A second contributing factor is that current abstractions and robot architectures are ill-suited to address this problem. This dissertation addresses the lack of a high-level abstraction for the naturalistic coordination of teams of heterogeneous robots, humans and other agents through the development of roles. Roles are a fundamental concept of social science that may provide this necessary abstraction. Roles are not a new concept and have been used in a number of related areas. This work draws from these fields and constructs a coherent and usable model of roles for robotics. This research is focussed on answering the following question: Can the use of social roles enable the naturalistic coordinated operation of robots in a mixed setting? In addition to this primary question, related research includes defining the key concepts important to artificial systems, providing a mapping and implementation from these concepts to a usable robot framework and identifies a set of robot-specific roles used for human-robot interaction. This research will benefit both the artificial intelligence agent and robotics communities. It poses a fundamental contribution to the multi-agent community because it extends and refines the role concept. The application of roles in a principled and complete implementation is a novel contribution to both software and robotic agents. The creation of an open source operational architecture which supports taskable robots is also a major contribution.