Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.

The industry of the civil Unmanned Aerial Vehicles (UAVs) has been growing rapidly in past few years. In many scenarios, accomplishing a task using a single UAV is either not cost-effective due to the size of the project or not even feasible due to the existence of unforeseen environment conditions and constraints (e.g., weather conditions and/or physical obstacles). This limitation motivates the need to move to solutions that incorporate a network of autonomous UAVs that carry out a joint and coordinated mission. This thesis introduces a multi-agent system and related algorithms that solve the graph traversal problem in a distributed and decentralized manner while optimizing a set of costs. The environment is modelled as a graph where every node is the point for the agents to accomplish some task or to distinguish the point as an obstacle where traveling is not possible. The online distributed algorithms are implemented on a network of UAVs and we report the results of rigorous simulations and real experiments with a network of UAVs. The results clearly validate our claim that a network UAVs can be effectively employed to accomplish a given task. / Thesis / Master of Science (MSc)

<p dir="ltr">The problem of system identification is to learn the system dynamics from data. While classical system identification theories focused primarily on achieving asymptotic consistency, recent efforts have sought to characterize the number of samples needed to achieve a desired level of accuracy in the learned model. This thesis focuses on finite sample analysis for identifying/learning dynamical systems.</p><p dir="ltr">In the first part of this thesis, we provide novel results on finite sample analysis for learning different linear systems. We first consider the system identification problem of a fully observed system (i.e., all states of the system can be perfectly measured), leveraging data generated from an auxiliary system that shares ``similar" dynamics. We provide insights on the benefits of using the auxiliary data, and guidelines on selecting the weight parameter during the model training process. Subsequently, we consider the system identification problem for a partially observed autonomous linear system, where only a subset of states and multiple short trajectories of the system can be observed. We present a finite sample error bound and characterize the learning rate. </p><p dir="ltr">In the second part of this thesis, we explore the practical usage of finite sample analysis under several different scenarios. We first consider a parameter learning problem in a distributed setting, where a group of agents wishes to collaboratively learn the underlying model. We propose a distributed parameter estimation algorithm and provide finite time bounds on the estimation error. We show that our analysis allows us to determine a time at which the communication can be stopped (due to the costs associated with communications), while meeting a desired estimation accuracy. Subsequently, we consider the problem of online change point detection for a linear system, where the user observes data in an online manner, and the goal is to determine when the underlying system dynamics change. We provide an online change point detection algorithm, and a data-dependent threshold that allows one to achieve a pre-specified upper bound on the probability of making a false alarm. We further provide a finite-sample-based lower bound for the probability of detecting a change point with a certain delay.</p><p dir="ltr">Finally, we extend the results to linear model identification from non-linear systems. We provide a data acquisition algorithm followed by a regularized least squares algorithm, along with an associated finite sample error bound on the learned linearized dynamics. Our error bound demonstrates a trade-off between the error due to nonlinearity and the error due to noise, and shows that one can learn the linearized dynamics with arbitrarily small error given sufficiently many samples.</p>

The global value creation networks have experienced increased volatility and dynamic behavior in recent years, resulting in an acceleration of a trend already evident in the shortening of product and technology cycles. In addition, the manufacturing industry is demonstrating a trend of allowing customers to make specific adjustments to their products at the time of ordering. Not only do these changes require a high level of flexibility and adaptability from the cyber-physical systems, but also from the employees and the supervisory production planning. As a result, the development of control and monitoring mechanisms becomes more complex. It is also necessary to adjust the production process dynamically if there are unforeseen events (disrupted supply chains, machine breakdowns, or absences of staff) in order to make the most effective and efficient use of the available production resources. In recent years, reinforcement learning (RL) research has gained increasing popularity in strategic planning as a result of its ability to handle uncertainty in dynamic environments in real time. RL has been extended to include multiple agents cooperating on complex tasks as a solution to complex problems. Despite its potential, the real-world application of multi-agent reinforcement learning (MARL) to manufacturing problems, such as flexible job-shop scheduling, has been less frequently approached. The main reason for this is most of the applications in this field are frequently subject to specific requirements as well as confidentiality obligations. Due to this, it is difficult for the research community to obtain access to them, which presents substantial challenges for the implementation of these tools. ...

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