京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18408号 / 情博第523号 / 新制||情||92(附属図書館) / 31266 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 多々納 裕一, 教授 石田 亨, 准教授 畑山 満則 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Market globalization and mass customization requirements are forcing companies towards automation of their product development processes. Many task-specific software solutions provide localized automation. Coordinating these local solutions to automate higher-level processes requires significant software maintenance costs due to the incompatibility of the software tools and the dynamic nature of the product development environment. Current automation methods do not provide the required level of flexibility to operate in this dynamic environment. An autonomic product development process automation strategy is proposed in order to provide a flexible, standardized approach to product development process automation and to significantly reduce the software maintenance costs associated with traditional automation methods. Key elements of the strategy include a formal approach to decompose product development processes into services, a method to describe functional and quality attributes of services, a process modeling algorithm to configure processes composed of services, a method to evaluate process utility based on quality metrics and user preferences, and an implementation that allows a user to instantiate the optimal process. Because the framework allows a user to rapidly reconfigure and select optimal processes as new services are introduced or as requirements change, the framework should reduce burdensome software maintenance costs associated with traditional automation methods and provide a more flexible approach.

Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.

The numerous previous attempts to simulate financial markets tended to be based on strong assumptions about markets or their participants. This thesis describes a more general kind of model - one in which deep reinforcement learning is used to train agents to make a profit while trading with each other on a virtual exchange. Such a model carries less inductive bias than most others - in theory, a neural network is capable of learning arbitrary decision rules. The model itself led to very simple results, but the conclusions from its construction will hopefully be of guidance to anyone implementing such a model in the future. / De många tidigare försöken att simulera finansmarknader har ofta byggt på starka antaganden om marknaderna eller deras deltagare. I den här avhandlingen beskrivs en mer allmän typ av modell - en modell där djup förstärkningsinlärning används för att träna agenter att göra vinst när de handlar med varandra på en virtuell börs. En sådan modell har mindre induktiva fördomar än de flesta andra - i teorin kan ett neuralt nätverk lära sig godtyckliga beslutsregler. Själva modellen ledde till mycket enkla resultat, men slutsatserna från dess konstruktion kommer förhoppningsvis att vara vägledande för alla som tillämpar en sådan modell i framtiden.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

Mycorrhizal Networks (MNs) facilitate the exchange of resources including energy, water, nutrients, and information between trees and plants in forest ecosystems. This work explored MNs as an inspiration for new market models in transactive energy networks, which similarly involve exchanges of energy and information between buildings in local communities. Specific insights from the literature on the structure and function of MNs were translated into an energy model with the aim of addressing challenges associated with the proliferation of distributed energy resources (DERs) at the grid edge and the incorporation of DER aggregations into wholesale energy markets. First, a systematic review of bio-inspired computing interventions applied to microgrids and their interactions with modern energy markets established a technical knowledge base within the context of distributed electrical systems. Second, a bio-inspired design process built on this knowledge base to yield a structural and functional blueprint for a computational mycorrhizal energy market simulation. Lastly, that computational model was implemented and simulated on a blockchain-compatible, multi-agent software platform to determine the effect that mycorrhizal strategies have on transactive energy market performance. The structural translation of a mapped ectomycorrhizal network of Douglas-firs in Oregon, USA called the 'wood-wide web' created an effective framework for the organization of a novel mycorrhizal energy market model that enabled participating buildings to redistribute percentages of their energy assets on different competing exchanges throughout a series of week-long simulations. No significant changes in functional performance –- as determined by economic, technical, and ecological metrics – were observed when the mycorrhizal results were compared to those of a baseline transactive energy community without periodic energy asset redistribution. Still, the model itself is determined to be a useful tool for further exploration of innovative, automated strategies for DER integration into modern energy market structures and electrical infrastructure in the age of Web3, especially as new science emerges to better explain trigger and feedback mechanisms for carbon exchange through MNs and how mycorrhizae adapt to changes in the environment. This dissertation concludes with a brief discussion of policy implications and an analysis applying the ecological principles of robustness, biodiversity, and altruism to the collective energy future of the human species. / Doctor of Philosophy / Beneath the forest floor, a network of fungi connects trees and plants and allows them to exchange energy and other resources. This dissertation compares this mycorrhizal network (mycorrhiza = fungus + root) to a group of solar-powered buildings generating energy and exchanging it in a local community marketplace (transactive energy markets). In the analogy, the buildings become the plants, the solar panels become the leaves, and the electrical grid represents the mycorrhizal network. Trees and plants produce their own energy through photosynthesis and then send large portions of it down to the roots, where they can trade it or send it to neighbors via the mycorrhizal network. Similarly, transactive energy markets are designed to allow buildings to sell the energy they produce on-site to neighbors, usually at better rates. This helps address a major infrastructure challenge that is arising with more people adding roof-top solar to their homes. The grid that powers our buildings is old now and it was designed to send power from a central power plant out to its edges where most homes and businesses are located. When too many homes produce solar power at the same time, there is nowhere for it to go, and it can easily overload the grid leading to fires, equipment failures, and power outages. Mycorrhizal networks solve this problem in part through local energy balancing driven by cooperative feedback patterns that have evolved over millennia to sustain forest ecosystems. This work applies scientific findings on the structure and function of mycorrhizal networks (MNs) to energy simulation methods in order to better understand the potential for building bio-inspired energy infrastructure in local communities. Specifically, the mapped structure of a MN of douglas-fir trees in Oregon, USA was adapted into a digital transactive energy market (TEM) model. This adaptation process revealed that a single building can connect to many TEMs simultaneously and that the number of connections can change over time just as symbiotic connections between organisms grow, decay, and adapt to a changing environment. The behavior of MNs in terms of when those connections are added and subtracted informed the functionality of the TEM model, which adds connections when community energy levels are high and subtracts connections when energy levels are low. The resulting 'mycorrhizal' model of the TEM was able to change how much energy each connected household traded on it by changing the number of connections (more connections mean more energy and vice versa). Though the functional performance of the mycorrhizal TEM did not change significantly from that of a typical TEM when they were the context of decentralized computer networks (blockchains) and distributed artificial intelligence. A concluding discussion addresses ways in which elements of this new model could transform energy distribution in communities and improve the resilience of local energy systems in the face of a changing climate.

<p dir="ltr">This dissertation presents decentralized and agent-based solutions for organizing machine learning resources, such as datasets and learning models. It aims to democratize the analysis of these resources through a simple yet flexible query structure, automate common ML tasks such as training, testing, model selection, and hyperparameter tuning, and enable privacy-centric building of ML models over distributed datasets. Based on networked multi-agent systems, the proposed approach represents ML resources as autonomous and self-reliant entities. This representation makes the resources easily movable, scalable, and independent of geographical locations, alleviating the need for centralized control and management units. Additionally, as all machine learning and data mining tasks are conducted near their resources, providers can apply customized rules independently of other parts of the system. </p><p><br></p>

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

Les besoins croissants en puissance de calcul sont généralement satisfaits en fédérant de plus en plus d’ordinateurs (ou noeuds) pour former des infrastructures distribuées. La tendance actuelle est d’utiliser la virtualisation système dans ces infrastructures, afin de découpler les logiciels des noeuds sous-jacents en les encapsulant dans des machines virtuelles. Pour gérer efficacement ces infrastructures virtualisées, de nouveaux gestionnaires logiciels ont été mis en place. Ces gestionnaires sont pour la plupart hautement centralisés (les tâches de gestion sont effectuées par un nombre restreint de nœuds dédiés). Cela limite leur capacité à passer à l’échelle, autrement dit à gérer de manière réactive des infrastructures de grande taille, qui sont de plus en plus courantes. Au cours de cette thèse, nous nous sommes intéressés aux façons d’améliorer cet aspect ; l’une d’entre elles consiste à décentraliser le traitement des tâches de gestion, lorsque cela s’avère judicieux. Notre réflexion s’est concentrée plus particulièrement sur l’ordonnancement dynamique des machines virtuelles, pour donner naissance à la proposition DVMS (Distributed Virtual Machine Scheduler). Nous avons mis en œuvre un prototype, que nous avons validé au travers de simulations (notamment via l’outil SimGrid), et d’expériences sur le banc de test Grid’5000. Nous avons pu constater que DVMS se montrait particulièrement réactif pour gérer des infrastructures virtualisées constituées de dizaines de milliers de machines virtuelles réparties sur des milliers de nœuds. Nous nous sommes ensuite penchés sur les perspectives d’extension et d’amélioration de DVMS. L’objectif est de disposer à terme d’un gestionnaire décentralisé complet, objectif qui devrait être atteint au travers de l’initiative Discovery qui fait suite à ces travaux. / The increasing need in computing power has been satisfied by federating more and more computers (called nodes) to build the so-called distributed infrastructures. Over the past few years, system virtualization has been introduced in these infrastructures (the software is decoupled from the hardware by packaging it in virtual machines), which has lead to the development of software managers in charge of operating these virtualized infrastructures. Most of these managers are highly centralized (management tasks are performed by a restricted set of dedicated nodes). As established, this restricts the scalability of managers, in other words their ability to be reactive to manage large-scale infrastructures, that are more and more common. During this Ph.D., we studied how to mitigate these concerns ; one solution is to decentralize the processing of management tasks, when appropriate. Our work focused in particular on the dynamic scheduling of virtual machines, resulting in the DVMS (Distributed Virtual Machine Scheduler) proposal. We implemented a prototype, that was validated by means of simulations (especially with the SimGrid tool) and with experiments on the Grid’5000 test bed. We observed that DVMS was very reactive to schedule tens of thousands of virtual machines distributed over thousands of nodes. We then took an interest in the perspectives to improve and extend DVMS. The final goal is to build a full decentralized manager. This goal should be reached by the Discovery initiative,that will leverage this work.

This thesis addresses the problem of positional consensus of multi-agent systems. A positional consensus is achieved when the agents converge to a point. Some applications of this class of problem is in mid-air refueling of the aircraft or UAVs, targeting a geographical location, etc. In this research work some positional consensus algorithms have been developed. They can be categorized in two part (i) Broadcast control based algorithm (ii) Distributed control based algorithm. In case of broadcast based algorithm control strategies for a group of agents is developed to achieve positional consensus. The problem is constrained by the requirement that every agent must be given the same control input through a broadcast communication mechanism. Although the control command is computed using state information in a global framework, the control input is implemented by the agents in a local coordinate frame. The mathematical formulation has been done in a linear programming framework that is computationally less intensive than earlier proposed methods. Moreover, a random perturbation input in the control command, that helps to achieve reasonable proximity among agents even for a large number of agents, which was not possible with the existing strategy in the literature, is introduced. This method is extended to achieve positional consensus at a pre-specified location. A comparison between the LP approach and the existing SOCP based approach is also presented. Some of the algorithm has been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots. In the second case of broadcast based algorithm, a decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus has been developed using the broadcast concept. Even here, the mathematical formulation has done using a linear programming framework. Each agent has some sensing radius and it is capable of sensing position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. In case of distributed algorithms, a computationally efficient distributed rendezvous algorithm for a group of autonomous agents has been developed. The algorithm uses a rectilinear decision domain (RDD), as against the circular decision domain assumed in earlier work available in the literature. This helps in reducing its computational complexity considerably. An extensive mathematical analysis has been carried out to prove the convergence of the algorithm. The algorithm has also been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots.