A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Recent methods of image analysis in remote sensing lack a sufficient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal effects, different locations, atmospheric conditions, or even a slightly different sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents’ behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

A novel soft computing approach based on FIR to model and predict energy dynamic systems

Tesi en modalitat compendi de publicacionsWe are facing a global climate crisis that is demanding a change in the status quo of how we produce, distribute and consume energy. In the last decades, this is being redefined through Smart Grids(SG), an intelligent electrical network more observable, controllable, automated, fully integrated with energy services and the end-users. Most of the features and proposed SG scenarios are based on reliable, robust and fast energy predictions. For instance, for proper planning activities, such as generation, purchasing, maintenance and investment; for demand side management, like demand response programs; for energy trading, especially at local level, where productions and consumptions are more stochastics and dynamic; better forecasts also increase grid stability and thus supply security. A large variety of Artificial Intelligence(AI) techniques have been applied in the field of Short-term electricity Load Forecasting(SLF) at consumer level in low-voltage system, showing a better performance than classical techniques. Inaccuracy or failure in the SLF process may be translated not just in a non-optimal (low prediction accuracy) solution but also in frustration of end-users, especially in new services and functionalities that empower citizens. In this regard, some limitations have been observed in energy forecasting models based on AI such as robustness, reliability, accuracy and computation in the edge. This research proposes and develops a new version of Fuzzy Inductive Reasoning(FIR), called Flexible FIR, to model and predict the electricity consumption of an entity in the low-voltage grid with high uncertainties, and information missing, as well as the capacity to be deployed either in the cloud or locally in a new version of Smart Meters(SMs) based on Edge Computing(EC). FIR has been proved to be a powerful approach for model identification and system ’s prediction over dynamic and complex processes in different real world domains but not yet in the energy domain. Thus, the main goal of this thesis is to demonstrate that a new version of FIR, more robust, reliable and accurate can be a referent Soft Computing(SC) methodology to model and predict dynamic systems in the energy domain and that it is scalable to an EC integration. The core developments of Flexible FIR have been an algorithm that can cope with missing information in the input values, as well as learn from instances with Missing Values(MVs) in the knowledge-based, without compromising significantly the accuracy of the predictions. Moreover, Flexible FIR comes with new forecasting strategies that can cope better with loss of causality of a variable and dispersion of output classes than classical k nearest neighbours, making the FIR forecasting process more reliable and robust. Furthermore, Flexible FIR addresses another major challenge modelling with SC techniques, which is to select best model parameters. One of the most important parameters in FIR is the number k of nearest neighbours to be used in the forecast process. The challenge to select the optimal k, dynamically, is addressed through an algorithm, called KOS(K nearest neighbour Optimal Selection), which has been developed and tested also with real world data. It computes a membership aggregation function of all the neighbours with respect their belonging to the output classes.While with KOS the optimal parameter k is found online, with other approaches such as genetic algorithms or reinforcement learning is not, which increases the computational time.Ens trobem davant una crisis climàtica global que exigeix un canvi al status quo de la manera que produïm, distribuïm i consumim energia. En les darreres dècades, està sent redefinit gràcies a les xarxa elèctriques intel·ligents(SG: Smart Grid) amb millor observabilitat, control, automatització, integrades amb nous serveis energètics i usuaris finals. La majoria de les funcionalitats i escenaris de les SG es basen en prediccions de la càrrega elèctrica confiables, robustes i ràpides. Per les prediccions de càrregues elèctriques a curt termini(SLF: Short-term electricity Load Forecasting), a nivell de consumidors al baix voltatge, s’han aplicat una gran varietat de tècniques intel·ligència Artificial(IA) mostrant millor rendiment que tècniques estadístiques tradicionals. Un baix rendiment en SLF, pot traduir-se no només en una solució no-òptima (baixa precisió de predicció) sinó també en la frustració dels usuaris finals, especialment en nous serveis i funcionalitats que empoderarien als ciutadans. En el marc d’aquesta investigació es proposa i desenvolupa una nova versió de la metodologia del Raonament Inductiu Difús(FIR: Fuzzy Inductive Reasoning), anomenat Flexible FIR, capaç de modelar i predir el consum d’electricitat d’una entitat amb un grau d’incertesa molt elevat, inclús amb importants carències d’informació (missing values). A més, Flexible FIR té la capacitat de desplegar-se al núvol, així como localment, en el que podria ser una nova versió de Smart Meters (SM) basada en tecnologia d’Edge Computing (EC). FIR ja ha demostrat ser una metodologia molt potent per la generació de models i prediccions en processos dinàmics en diferents àmbits, però encara no en el de l’energia. Per tant, l’objectiu principal d’aquesta tesis és demostrar que una versió millorada de FIR, més robusta, fiable i precisa pot consolidar-se com una metodologia Soft Computing SC) de referencia per modelar i predir sistemes dinàmics en aplicacions per al sector de l’energia i que és escalable a una integració d’EC. Les principals millores de Flexible FIR han estat, en primer lloc, el desenvolupament i test d’un algorisme capaç de processar els valors d’entrada d’un model FIR tot i que continguin Missing Values (MV). Addicionalment, aquest algorisme també permet aprendre d’instàncies amb MV en la matriu de coneixement d’un model FIR, sense comprometre de manera significativa la precisió de les prediccions. En segon lloc, s’han desenvolupat i testat noves estratègies per a la fase de predicció, comportant-se millor que els clàssics k veïns més propers quan ens trobem amb pèrdua de causalitat d’una variable i dispersió en les classes de sortida, aconseguint un procés d’aprenentatge i predicció més confiable i robust. En tercer lloc, Flexible FIR aborda un repte molt comú en tècniques de SC: l’òptima parametrització del model. En FIR, un dels paràmetres més determinants és el número k de veïns més propers que s’utilitzaran durant la fase de predicció. La selecció del millor valor de k es planteja de manera dinàmica a través de l’algorisme KOS (K nearest neighbour Optimal Selection) que s’ha desenvolupat i testat també amb dades reals. Mentre que amb KOS el paràmetre òptim de k es calcula online, altres enfocaments mitjançant algoritmes genètics o aprenentatge per reforç el càlcul és offline, incrementant significativament el temps de resposta, sent a més a més difícil la implantació en escenaris d’EC. Aquestes millores fan que Flexible FIR es pugui adaptar molt bé en aplicacions d’EC. En aquest sentit es proposa el concepte d’un SM de segona generació basat en EC, que integra Flexible FIR com mòdul de predicció d’electricitat executant-se en el propi dispositiu i un agent EC amb capacitat per el trading d'energia produïda localment. Aquest agent executa un innovador mecanisme basat en incentius, anomenat NRG-X-Change que utilitza una nova moneda digital descentralitzada per l’intercanvi d’energia, que s’anomena NRGcoin.Estamos ante una crisis climática global que exige un cambio del status quo de la manera que producimos, distribuimos y consumimos energía. En las últimas décadas, este status quo está siendo redefinido debido a: la penetración de las energías renovables y la generación distribuida; nuevas tecnologías como baterías y paneles solares con altos rendimientos; y la forma en que se consume la energía, por ejemplo, a través de vehículos eléctricos o con la electrificación de los hogares. Estas palancas requieren una red eléctrica inteligente (SG: Smart Grid) con mayor observabilidad, control, automatización y que esté totalmente integrada con nuevos servicios energéticos, así como con sus usuarios finales. La mayoría de las funcionalidades y escenarios de las redes eléctricas inteligentes se basan en predicciones de la energía confiables, robustas y rápidas. Por ejemplo, para actividades de planificación como la generación, compra, mantenimiento e inversión; para la gestión de la demanda, como los programas de demand response; en el trading de electricidad, especialmente a nivel local, donde las producciones y los consumos son más estocásticos y dinámicos; una mejor predicción eléctrica también aumenta la estabilidad de la red y, por lo tanto, mejora la seguridad. Para las predicciones eléctricas a corto plazo (SLF: Short-term electricity Load Forecasting), a nivel de consumidores en el bajo voltaje, se han aplicado una gran variedad de técnicas de Inteligencia Artificial (IA) mostrando mejor rendimiento que técnicas estadísticas convencionales. Un bajo rendimiento en los modelos predictivos, puede traducirse no solamente en una solución no-óptima (baja precisión de predicción) sino también en frustración de los usuarios finales, especialmente en nuevos servicios y funcionalidades que empoderan a los ciudadanos. En este sentido, se han identificado limitaciones en modelos de predicción de energía basados en IA, como la robustez, fiabilidad, precisión i computación en el borde. En el marco de esta investigación se propone y desarrolla una nueva versión de la metodología de Razonamiento Inductivo Difuso (FIR: Fuzzy Inductive Reasoning), que hemos llamado Flexible FIR, capaz de modelar y predecir el consumo de electricidad de una entidad con altos grados de incertidumbre e incluso con importantes carencias de información (missing values). Además, Flexible FIR tiene la capacidad de desplegarse en la nube, así como localmente, en lo que podría ser una nueva versión de Smart Meters (SM) basada en tecnología de Edge Computing (EC). En el pasado, ya se ha demostrado que FIR es una metodología muy potente para la generación de modelos y predicciones en procesos dinámicos, sin embargo, todavía no ha sido demostrado en el campo de la energía. Por tanto, el objetivo principal de esta tesis es demostrar que una versión mejorada de FIR, más robusta, fiable y precisa puede consolidarse como metodología Soft Computing (SC) de referencia para modelar y predecir sistemas dinámicos en aplicaciones para el sector de la energía y que es escalable hacia una integración de EC. Las principales mejoras en Flexible FIR han sido, en primer lugar, el desarrollo y testeo de un algoritmo capaz de procesar los valores de entrada en un modelo FIR a pesar de que contengan Missing Values (MV). Además, dicho algoritmo también permite aprender de instancias con MV en la matriz de conocimiento de un modelo FIR, sin comprometer de manera significativa la precisión de las predicciones. En segundo lugar, se han desarrollado y testeado nuevas estrategias para la fase de predicción de un modelo FIR, comportándose mejor que los clásicos k vecinos más cercanos ante la pérdida de causalidad de una variable y dispersión de clases de salida, consiguiendo un proceso de aprendizaje y predicción más confiable y robusto. En tercer lugar, Flexible FIR aborda un desafío muy común en técnicas de SC: la óptima parametrización del modelo. En FIR, uno de los parámetros más determinantes es el número k de vecinos más cercanos que se utilizarán en la fase de predicción. La selección del mejor valor de k se plantea de manera dinámica a través del algoritmo KOS (K nearest neighbour Optimal Selection) que se ha desarrollado y probado también con datos reales. Dicho algoritmo calcula una función de membresía agregada, de todos los vecinos, con respecto a su pertenencia a las clases de salida. Mientras que con KOS el parámetro óptimo de k se calcula online, otros enfoques mediante algoritmos genéticos o aprendizaje por refuerzo, el cálculo es offline incrementando significativamente el tiempo de respuesta, siendo además difícil su implantación en escenarios de EC. Estas mejoras hacen que Flexible FIR se adapte muy bien en aplicaciones de EC, en las que la analítica de datos en streaming debe ser fiable, robusta y con un modelo suficientemente ligero para ser ejecutado en un IoT Gateway o dispositivos más pequeños. También, en escenarios con poca conectividad donde el uso de la computación en la nube es limitado y los parámetros del modelo se calculan localmente. Con estas premisas, en esta tesis, se propone el concepto de un SM de segunda generación basado en EC, que integra Flexible FIR como módulo de predicción de electricidad ejecutándose en el dispositivo y un agente EC con capacidad para el trading de energía producida localmente. Dicho agente ejecuta un novedoso mecanismo basado en incentivos, llamado NRG-X-Change que utiliza una nueva moneda digital descentralizada para el intercambio de energía, llamada NRGcoin.Postprint (published version

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Diabetic retinopathy diagnosis through multi-agent approaches

Programa Doutoral em Engenharia BiomédicaDiabetic retinopathy has been revealed as a serious public health problem in occidental world, since it is the most common cause of vision impairment among people of working age. The early diagnosis and an adequate treatment can prevent loss of vision. Thus, a regular screening program to detect diabetic retinopathy in the early stages could be efficient for the prevention of blindness. Due to its characteristics, digital color fundus photographs have been the preferred eye examination method adopted in these programs. Nevertheless, due to the growing incidence of diabetes in population, ophthalmologists have to observe a huge number of images. Therefore, the development of computational tools that can assist the diagnosis is of major importance. Several works have been published in the recent past years for this purpose; but an automatic system for clinical practice has yet to come. In general, these algorithms are used to normalize, segment and extract information from images to be utilized by classifiers which aim to classify the regions of the fundus image. These methods are mostly based on global approaches that cannot be locally adapted to the image properties and therefore, none of them perform as needed because of fundus images complexity. This thesis focuses on the development of new tools based on multi-agent approaches, to assist the diabetic retinopathy early diagnosis. The fundus image automatic segmentation concerning the diabetic retinopathy diagnosis should comprise both pathological (dark and bright lesions) and anatomical features (optic disc, blood vessels and fovea). In that way, systems for the optic disc detection, bright lesions segmentation, blood vessels segmentation and dark lesions segmentation were implemented and, when possible, compared to those approaches already described in literature. Two kinds of agent based systems were investigated and applied to digital color fundus photographs: ant colony system and multi-agent system composed of reactive agents with interaction mechanisms between them. The ant colony system was used to the optic disc detection and for bright lesion segmentation. Multi-agent system models were developed for the blood vessel segmentation and for small dark lesion segmentation. The multi-agent system models created in this study are not image processing techniques on their own, but they are used as tools to improve the traditional algorithms results at the micro level. The results of all the proposed approaches are very promising and reveal that the systems created perform better than other recent methods described in the literature. Therefore, the main scientific contribution of this thesis is to prove that multi-agent systems based approaches can be efficient in segmenting structures in retinal images. Such an approach overcomes the classic image processing algorithms that are limited to macro results and do not consider the local characteristics of images. Hence, multi-agent systems based approaches could be a fundamental tool, responsible for a very efficient system development to be used in screening programs concerning diabetic retinopathy early diagnosis.A retinopatia diabética tem-se revelado como um problema sério de saúde pública no mundo ocidental, uma vez que é a principal causa de cegueira entre as pessoas em idade ativa. Contudo, a perda de visão pode ser prevenida através da deteção precoce da doença e de um tratamento adequado. Por isso, um programa regular de rastreio e monitorização da retinopatia diabética pode ser eficiente na prevenção da deterioração da visão. Devido às suas características, a fotografia digital colorida do fundo do olho tem sido o exame adotado neste tipo de programas. No entanto, devido ao aumento da incidência da diabetes na população, o número de imagens a serem analisadas pelos oftalmologistas é elevado. Assim sendo, é muito importante o desenvolvimento de ferramentas computacionais para auxiliar no diagnóstico desta patologia. Nos últimos anos, têm sido vários os trabalhos publicados com este propósito; porém, não existe ainda um sistema automático (ou recomendável) para ser usado nas práticas clínicas. No geral, estes algoritmos são usados para normalizar, segmentar e extrair informação das imagens que vai ser utilizada por classificadores, cujo objetivo é identificar as regiões da imagem que se procuram. Estes métodos são maioritariamente baseados em abordagens globais que não podem ser localmente adaptadas às propriedades das imagens e, portanto, nenhum apresenta a performance necessária devido à complexidade das imagens do fundo do olho. Esta tese foca-se no desenvolvimento de novas ferramentas computacionais baseadas em sistemas multi-agente, para auxiliar na deteção precoce da retinopatia diabética. A segmentação automática das imagens do fundo do olho com o objetivo de diagnosticar a retinopatia diabética, deve englobar características patológicas (lesões claras e escuras) e anatómicas (disco ótico, vasos sanguíneos e fóvea). Deste modo, foram criados sistemas para a deteção do disco ótico e para a segmentação das lesões claras, dos vasos sanguíneos e das lesões escuras e, quando possível, estes foram comparados com abordagens já descritas na literatura. Dois tipos de sistemas baseados em agentes foram investigados e aplicados nas imagens digitais coloridas do fundo do olho: sistema de colónia de formigas e sistema multi-agente constituído por agentes reativos e com mecanismos de interação entre eles. O sistema de colónia de formigas foi usado para a deteção do disco ótico e para a segmentação das lesões claras. Modelos de sistemas multi-agente foram desenvolvidos para a segmentação dos vasos sanguíneos e das lesões escuras. Os modelos multi-agentes criados ao longo deste estudo não são por si só técnicas de processamento de imagem, mas são sim usados como ferramentas para melhorar os resultados dos algoritmos tradicionais no baixo nível. Os resultados de todas as abordagens propostas são muito promissores e revelam que os sistemas criados apresentam melhor performance que outras abordagens recentes descritas na literatura. Posto isto, a maior contribuição científica desta tese é provar que abordagens baseadas em sistemas multi-agente podem ser eficientes na segmentação de estruturas em imagens da retina. Uma abordagem deste tipo ultrapassa os algoritmos clássicos de processamento de imagem, que se limitam aos resultados de alto nível e não têm em consideração as propriedades locais das imagens. Portanto, as abordagens baseadas em sistemas multi-agente podem ser uma ferramenta fundamental, responsável pelo desenvolvimento de um sistema eficiente para ser usado nos programas de rastreio e monitorização da retinopatia diabética.Work supported by FEDER funds through the "Programa Operacional Factores de Competitividade – COMPETE" and by national funds by FCT- Fundação para a Ciência e a Tecnologia. C. Pereira thanks the FCT for the SFRH / BD / 61829 / 2009 grant

INCUBATION OF METAHEURISTIC SEARCH ALGORITHMS INTO NOVEL APPLICATION FIELDS

Several optimization algorithms have been developed to handle various optimization issues in many fields, capturing the attention of many researchers. Algorithm optimizations are commonly inspired by nature or involve the modification of existing algorithms. So far, the new algorithms are set up and focusing on achieving the desired optimization goal. While this can be useful and efficient in the short term, in the long run, this is not enough as it needs to repeat for any new problem that occurs and maybe in specific difficulties, therefore one algorithm cannot be used for all real-world problems. This dissertation provides three approaches for implementing metaheuristic search (MHS) algorithms in fields that do not directly solve optimization issues. The first approach is to study parametric studies on MHS algorithms that attempt to understand how parameters work in MHS algorithms. In this first direction, we choose the Jaya algorithm, a relatively recent MHS algorithm defined as a method that does not require algorithm-specific control parameters. In this work, we incorporate weights as an extra parameter to test if Jaya’s approach is actually "parameter-free." This algorithm’s performance is evaluated by implementing 12 unconstrained benchmark functions. The results will demonstrate the direct impact of parameter adjustments on algorithm performance. The second approach is to embed the MHS algorithm on the Blockchain Proof of Work (PoW) to deal with the issue of excessive energy consumption, particularly in using bitcoin. This study uses an iterative optimization algorithm to solve the Traveling Salesperson Problem (TSP) as a model problem, which has the same concept as PoW and requires extending the Blockchain with additional blocks. The basic idea behind this research is to increase the tour cost for the best tour found for n blocks, extended by adding one more city as a requirement to include a new block in the Blockchain. The results reveal that the proposed concept can improve the way the current system solves complicated cryptographic problems Furthermore, MHS are implemented in the third direction approach to solving agricultural problems, especially the cocoa flowers pollination. We chose the problem in pollination in cacao flowers since they are distinctive and different from other flowers due to their small size and lack of odor, allowing just a few pollinators to successfully pollinate them, most notably a tiny midge called Forcipomyia Inornatipennis (FP). This concept was then adapted and implemented into an Idle-Metaheuristic for simulating the pollination of cocoa flowers. We analyze how MHS algorithms derived from three well-known methods perform when used to flower pollination problems. Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems Search are the three methodologies studied here. The results shows that the Multi-Agent System search performs better than other methods. The findings of the three approaches reveal that adopting an MHS algorithms can solve the problem in this study by indirectly solving the optimization problem using the same problem model concept. Furthermore, the researchers concluded that parameter settings in the MHS algorithms are not so difficult to use, and each parameter can be adjusted to solve the real-world issue. This study is expected to encourage other researchers to improve and develop the performance of MHS algorithms used to deal with multiple real-world problems.九州工業大学博士学位論文 学位記番号: 情工博甲第367号 学位授与年月日: 令和4年3月25日1 Introduction|2 Traditional Metaheuristic Search Optimization|3 Parametric Study of Metaheuristic Search Algorithms|4 Embedded Metaheuristic Search Algorithms for Blockchain Proof-of-Work|5 Idle-Metaheuristic for Flower Pollination Simulation|6 Conclusion and Future Works九州工業大学令和3年

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications