Дисертації з теми "Markov Decision Process Planning"
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Geng, Na. "Combinatorial optimization and Markov decision process for planning MRI examinations." Phd thesis, Saint-Etienne, EMSE, 2010. http://tel.archives-ouvertes.fr/tel-00566257.
Повний текст джерелаDai, Peng. "FASTER DYNAMIC PROGRAMMING FOR MARKOV DECISION PROCESSES." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/428.
Повний текст джерелаAlizadeh, Pegah. "Elicitation and planning in Markov decision processes with unknown rewards." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCD011/document.
Повний текст джерелаMarkov decision processes (MDPs) are models for solving sequential decision problemswhere a user interacts with the environment and adapts her policy by taking numericalreward signals into account. The solution of an MDP reduces to formulate the userbehavior in the environment with a policy function that specifies which action to choose ineach situation. In many real world decision problems, the users have various preferences,and therefore, the gain of actions on states are different and should be re-decoded foreach user. In this dissertation, we are interested in solving MDPs for users with differentpreferences.We use a model named Vector-valued MDP (VMDP) with vector rewards. We propose apropagation-search algorithm that allows to assign a vector-value function to each policyand identify each user with a preference vector on the existing set of preferences wherethe preference vector satisfies the user priorities. Since the user preference vector is notknown we present several methods for solving VMDPs while approximating the user’spreference vector.We introduce two algorithms that reduce the number of queries needed to find the optimalpolicy of a user: 1) A propagation-search algorithm, where we propagate a setof possible optimal policies for the given MDP without knowing the user’s preferences.2) An interactive value iteration algorithm (IVI) on VMDPs, namely Advantage-basedValue Iteration (ABVI) algorithm that uses clustering and regrouping advantages. Wealso demonstrate how ABVI algorithm works properly for two different types of users:confident and uncertain.We finally work on a minimax regret approximation method as a method for findingthe optimal policy w.r.t the limited information about user’s preferences. All possibleobjectives in the system are just bounded between two higher and lower bounds while thesystem is not aware of user’s preferences among them. We propose an heuristic minimaxregret approximation method for solving MDPs with unknown rewards that is faster andless complex than the existing methods in the literature
Ernsberger, Timothy S. "Integrating Deterministic Planning and Reinforcement Learning for Complex Sequential Decision Making." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354813154.
Повний текст джерелаAl, Sabban Wesam H. "Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82297/1/Wesam%20H_Al%20Sabban_Thesis.pdf.
Повний текст джерелаPoulin, Nolan. "Proactive Planning through Active Policy Inference in Stochastic Environments." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1267.
Повний текст джерелаPokharel, Gaurab. "Increasing the Value of Information During Planning in Uncertain Environments." Oberlin College Honors Theses / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1624976272271825.
Повний текст джерелаStárek, Ivo. "Plánování cesty robota pomocí dynamického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228655.
Повний текст джерелаPinheiro, Paulo Gurgel 1983. "Localização multirrobo cooperativa com planejamento." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276155.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Em um problema de localização multirrobô cooperativa, um grupo de robôs encontra-se em um determinado ambiente, cuja localização exata de cada um dos robôs é desconhecida. Neste cenário, uma distribuição de probabilidades aponta as chances de um robô estar em um determinado estado. É necessário então, que os robôs se movimentem pelo ambiente e gerem novas observações que serão compartilhadas, para calcular novas estimativas. Nos últimos anos, muitos trabalhos têm focado no estudo de técnicas probabilísticas, modelos de comunicação e modelos de detecções, para resolver o problema de localização. No entanto, a movimentação dos robôs é, em geral, definida por ações aleatórias. Ações aleatórias geram observações que podem ser inúteis para a melhoria da estimativa. Este trabalho apresenta uma proposta de localização com suporte a planejamento de ações. O objetivo é apresentar um modelo cujas ações realizadas pelos robôs são definidas por políticas. Escolhendo a melhor ação a ser realizada, é possível receber informações mais úteis dos sensores internos e externos e estimar as posturas mais rapidamente. O modelo proposto, denominado Modelo de Localização Planejada - MLP, utiliza POMDPs para modelar os problemas de localização e algoritmos específicos de geração de políticas. Foi utilizada a localização de Markov como técnica probabilística de localização e implementadas versões de modelos de detecção e propagação de informação. Neste trabalho, um simulador de problemas de localização multirrobô foi desenvolvido, no qual foram realizados experimentos em que o modelo proposto foi comparado a um modelo que não faz uso de planejamento de ações. Os resultados obtidos apontam que o modelo proposto é capaz de estimar as posturas dos robôs com uma menor quantidade de passos, sendo significativamente mais e ciente do que o modelo comparado sem planejamento.
Abstract: In a cooperative multi-robot localization problem, a group of robots is in a certain environment, where the exact location of each robot is unknown. In this scenario, there is only a distribution of probabilities indicating the chance of a robot to be in a particular state. It is necessary for the robots to move in the environment generating new observations, which will be shared to calculate new estimates. Currently, many studies have focused on the study of probabilistic techniques, models of communication and models of detection to solve the localization problem. However, the movement of robots is generally defined by random actions. Random actions generate observations that can be useless for improving the estimate. This work describes a proposal for multi-robot localization with support planning of actions. The objective is to describe a model whose actions performed by robots are defined by policies. Choosing the best action to be performed, the robot gets more useful information from internal and external sensors and estimates the posture more quickly. The proposed model, called Model of Planned Localization - MPL, uses POMDPs to model the problems of location and specific algorithms to generate policies. The Markov localization was used as probabilistic technique of localization and implemented versions of detection models and information propagation model. In this work, a simulator to multi-robot localization problems was developed, in which experiments were performed. The proposed model was compared to a model that does not make use of planning actions. The results showed that the proposed model is able to estimate the positions of robots with lower number of steps, being more e-cient than model compared.
Mestrado
Inteligencia Artificial
Mestre em Ciência da Computação
Junyent, Barbany Miquel. "Width-Based Planning and Learning." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672779.
Повний текст джерелаLa presa seqüencial de decisions òptimes és un problema fonamental en diversos camps. En els últims anys, els mètodes d'aprenentatge per reforç (RL) han experimentat un èxit sense precedents, en gran part gràcies a l'ús de models d'aprenentatge profund, aconseguint un rendiment a nivell humà en diversos dominis, com els videojocs d'Atari o l'antic joc de Go. En contrast amb l'enfocament de RL, on l'agent aprèn una política a partir de mostres d'interacció amb l'entorn, ignorant l'estructura del problema, l'enfocament de planificació assumeix models coneguts per als objectius de l'agent i la dinàmica del domini, i es basa en determinar com ha de comportar-se l'agent per aconseguir els seus objectius. Els planificadors actuals són capaços de resoldre problemes que involucren grans espais d'estats precisament explotant l'estructura del problema, definida en el model estat-acció. En aquest treball combinem els dos enfocaments, aprofitant polítiques ràpides i compactes dels mètodes d'aprenentatge i la capacitat de fer cerques en problemes combinatoris dels mètodes de planificació. En particular, ens enfoquem en una família de planificadors basats en el width (ample), que han tingut molt èxit en els últims anys gràcies a que la seva escalabilitat és independent de la mida de l'espai d'estats. L'algorisme bàsic, Iterated Width (IW), es va proposar originalment per problemes de planificació clàssica, on el model de transicions d'estat i objectius ve completament determinat, representat per conjunts d'àtoms. No obstant, els planificadors basats en width no requereixen un model de l'entorn completament definit i es poden utilitzar amb simuladors. Per exemple, s'han aplicat recentment a dominis gràfics com els jocs d'Atari. Malgrat el seu èxit, IW és un algorisme purament exploratori i no aprofita la informació de recompenses anteriors. A més, requereix que l'estat estigui factoritzat en característiques, que han de predefinirse per a la tasca en concret. A més, executar l'algorisme amb un width superior a 1 sol ser computacionalment intractable a la pràctica, el que impedeix que IW resolgui problemes de width superior. Comencem aquesta tesi estudiant la complexitat dels mètodes basats en width quan l'espai d'estats està definit per característiques multivalor, com en els problemes de RL, en lloc d'àtoms booleans. Proporcionem un límit superior més precís en la quantitat de nodes expandits per IW, així com resultats generals de complexitat algorísmica. Per fer front a problemes més complexos (és a dir, aquells amb un width superior a 1), presentem un algorisme jeràrquic que planifica en dos nivells d'abstracció. El planificador d'alt nivell utilitza característiques abstractes que es van descobrint gradualment a partir de decisions de poda en l'arbre de baix nivell. Il·lustrem aquest algorisme en dominis PDDL de planificació clàssica, així com en dominis de simuladors gràfics. En planificació clàssica, mostrem com IW(1) en dos nivells d'abstracció pot resoldre problemes de width 2. Per aprofitar la informació de recompenses passades, incorporem una política explícita en el mecanisme de selecció d'accions. El nostre mètode, anomenat π-IW, intercala la planificació basada en width i l'aprenentatge de la política usant les accions visitades pel planificador. Representem la política amb una xarxa neuronal que, al seu torn, s'utilitza per guiar la planificació, reforçant així camins prometedors. A més, la representació apresa per la xarxa neuronal es pot utilitzar com a característiques per al planificador sense degradar el seu rendiment, eliminant així el requisit d'usar característiques predefinides. Comparem π-IW amb mètodes anteriors basats en width i amb AlphaZero, un mètode que també intercala planificació i aprenentatge, i mostrem que π-IW té un rendiment superior en entorns simples. També mostrem que l'algorisme π-IW supera altres mètodes basats en width en els jocs d'Atari. Finalment, mostrem que el mètode IW jeràrquic proposat pot integrar-se fàcilment amb el nostre esquema d'aprenentatge de la política, donant com a resultat un algorisme que supera els planificadors no jeràrquics basats en IW en els jocs d'Atari amb recompenses distants.
La toma secuencial de decisiones óptimas es un problema fundamental en diversos campos. En los últimos años, los métodos de aprendizaje por refuerzo (RL) han experimentado un éxito sin precedentes, en gran parte gracias al uso de modelos de aprendizaje profundo, alcanzando un rendimiento a nivel humano en varios dominios, como los videojuegos de Atari o el antiguo juego de Go. En contraste con el enfoque de RL, donde el agente aprende una política a partir de muestras de interacción con el entorno, ignorando la estructura del problema, el enfoque de planificación asume modelos conocidos para los objetivos del agente y la dinámica del dominio, y se basa en determinar cómo debe comportarse el agente para lograr sus objetivos. Los planificadores actuales son capaces de resolver problemas que involucran grandes espacios de estados precisamente explotando la estructura del problema, definida en el modelo estado-acción. En este trabajo combinamos los dos enfoques, aprovechando políticas rápidas y compactas de los métodos de aprendizaje y la capacidad de realizar búsquedas en problemas combinatorios de los métodos de planificación. En particular, nos enfocamos en una familia de planificadores basados en el width (ancho), que han demostrado un gran éxito en los últimos años debido a que su escalabilidad es independiente del tamaño del espacio de estados. El algoritmo básico, Iterated Width (IW), se propuso originalmente para problemas de planificación clásica, donde el modelo de transiciones de estado y objetivos viene completamente determinado, representado por conjuntos de átomos. Sin embargo, los planificadores basados en width no requieren un modelo del entorno completamente definido y se pueden utilizar con simuladores. Por ejemplo, se han aplicado recientemente en dominios gráficos como los juegos de Atari. A pesar de su éxito, IW es un algoritmo puramente exploratorio y no aprovecha la información de recompensas anteriores. Además, requiere que el estado esté factorizado en características, que deben predefinirse para la tarea en concreto. Además, ejecutar el algoritmo con un width superior a 1 suele ser computacionalmente intratable en la práctica, lo que impide que IW resuelva problemas de width superior. Empezamos esta tesis estudiando la complejidad de los métodos basados en width cuando el espacio de estados está definido por características multivalor, como en los problemas de RL, en lugar de átomos booleanos. Proporcionamos un límite superior más preciso en la cantidad de nodos expandidos por IW, así como resultados generales de complejidad algorítmica. Para hacer frente a problemas más complejos (es decir, aquellos con un width superior a 1), presentamos un algoritmo jerárquico que planifica en dos niveles de abstracción. El planificador de alto nivel utiliza características abstractas que se van descubriendo gradualmente a partir de decisiones de poda en el árbol de bajo nivel. Ilustramos este algoritmo en dominios PDDL de planificación clásica, así como en dominios de simuladores gráficos. En planificación clásica, mostramos cómo IW(1) en dos niveles de abstracción puede resolver problemas de width 2. Para aprovechar la información de recompensas pasadas, incorporamos una política explícita en el mecanismo de selección de acciones. Nuestro método, llamado π-IW, intercala la planificación basada en width y el aprendizaje de la política usando las acciones visitadas por el planificador. Representamos la política con una red neuronal que, a su vez, se utiliza para guiar la planificación, reforzando así caminos prometedores. Además, la representación aprendida por la red neuronal se puede utilizar como características para el planificador sin degradar su rendimiento, eliminando así el requisito de usar características predefinidas. Comparamos π-IW con métodos anteriores basados en width y con AlphaZero, un método que también intercala planificación y aprendizaje, y mostramos que π-IW tiene un rendimiento superior en entornos simples. También mostramos que el algoritmo π-IW supera otros métodos basados en width en los juegos de Atari. Finalmente, mostramos que el IW jerárquico propuesto puede integrarse fácilmente con nuestro esquema de aprendizaje de la política, dando como resultado un algoritmo que supera a los planificadores no jerárquicos basados en IW en los juegos de Atari con recompensas distantes.
Lacerda, Dênis Antonio. "Aprendizado por reforço em lote: um estudo de caso para o problema de tomada de decisão em processos de venda." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03072014-101251/.
Повний текст джерелаProbabilistic planning studies the problems of sequential decision-making of an agent, in which actions have probabilistic effects, and can be modeled as a Markov decision process (MDP). Given the probabilities and reward values of each action, it is possible to determine an action policy (in other words, a mapping between the state of the environment and the agent\'s actions) that maximizes the expected reward accumulated by executing a sequence of actions. In cases where the MDP model is not completely known, the best policy needs to be learned through the interaction of the agent in the real environment. This process is called reinforcement learning. However, in applications where it is not allowed to perform experiments in the real environment, for example, sales process, it is possible to perform the reinforcement learning using a sample of past experiences. This process is called Batch Reinforcement Learning. In this work, we study techniques of batch reinforcement learning (BRL), in which learning is done using a history of past interactions, stored in a processes database. As a case study, we apply this technique for learning policies in the sales process for large format printers, whose goal is to build a action recommendation system for beginners sellers.
Borges, Igor Oliveira. "Estratégias para otimização do algoritmo de Iteração de Valor Sensível a Risco." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-09012019-103826/.
Повний текст джерелаRisk Sensitive Markov Decision Process (RS-MDP) allows modeling risk-averse and risk-prone attitudes in decision-making process using a risk factor to represent the risk-attitude. For this model, there are operators that are based on a piecewise linear transformation function that includes a risk factor and a discount factor. In this dissertation we formulate two Risk Sensitive Value Iteration algorithms based on one of these operators, these algorithms are called Synchronous Risk Sensitive Value Iteration (RSVI) and Asynchronous Risk Sensitive Value Iteration (A-RSVI). We also propose two heuristics that can be used to initialize the value of the RSVI or A-RSVI algorithms in order to make them more efficient. The results of experiments with the River domain in two distinct rewards scenarios show that: (i) the processing cost in extreme risk policies, for both risk-averse and risk-prone, is high; (ii) a high discount value increases the convergence time and reinforces the chosen risk attitude; (iii) policies with intermediate risk factor values have a low computational cost and show a certain sensitivity to risk based on the discount factor; and (iv) the A-RSVI algorithm with the heuristic based on the risk factor can decrease the convergence time of the algorithm, especially when we need a solution for extreme values of the risk factor
Holguin, Mijail Gamarra. "Planejamento probabilístico usando programação dinâmica assíncrona e fatorada." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-14042013-131306/.
Повний текст джерелаMarkov Decision Process (MDP) model problems of sequential decision making, where the possible actions have probabilistic effects on the successor states (defined by state transition matrices). Real-time dynamic programming (RTDP), is a technique for solving MDPs when there exists information about the initial state. Traditional approaches show better performance in problems with sparse state transition matrices, because they can achieve the convergence to optimal policy efficiently, without visiting all states. But, this advantage can be lose in problems with dense state transition matrices, in which several states can be achieved in a step (for example, control problems with exogenous events). An approach to overcome this limitation is to explore regularities existing in the domain dynamics through a factored representation, i.e., a representation based on state variables. In this master thesis, we propose a new algorithm called FactRTDP (Factored RTDP), and its approximate version aFactRTDP (Approximate and Factored RTDP), that are the first factored efficient versions of the classical RTDP algorithm. We also propose two other extensions, FactLRTDP and aFactLRTDP, that label states for which the value function has converged to the optimal. The experimental results show that when these new algorithms are executed in domains with dense transition matrices, they converge faster. And they have a good online performance in domains with dense transition matrices and few dependencies among state variables.
Sandino, Mora Juan David. "Autonomous decision-making for UAVs operating under environmental and object detection uncertainty." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/232513/1/Juan%20David_Sandino%20Mora_Thesis.pdf.
Повний текст джерелаFreitas, Elthon Manhas de. "Planejamento probabilístico sensível a risco com ILAO* e função utilidade exponencial." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-17012019-092638/.
Повний текст джерелаMarkov Decision Process (MDP) has been used very efficiently to solve sequential decision-making problems. There are problems where dealing with environmental risks to get a reliable result is more important than maximizing the expected average return. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). Among the several variations of RSMDP are the works based on exponential utility that use a risk factor, which models the agent\'s risk attitude that can be prone or averse. The algorithms in the literature to solve this type of RSMDPs are inefficient when compared to other MDP algorithms. In this project, a solution is presented that can be used in larger problems, either by performing calculations only in relevant states to reach a set of meta states starting from an initial state, or by allowing the processing of numbers with very high exponents for the current computational environments. The experiments show that (i) the proposed algorithm is more efficient when compared to state-of-the-art algorithms for RSMDPs; and (ii) the LogSumExp technique solves the problem of working with very large exponents in RSMDPs
Hireche, Chabha. "Etude et implémentation sur SoC-FPGA d'une méthode probabiliste pour le contrôle de mission de véhicule autonome Embedded context aware diagnosis for a UAV SoC platform, in Microprocessors and Microsystems 51, June 2017 Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs, in MDPI-Sensors Journal, December 2018." Thesis, Brest, 2019. http://www.theses.fr/2019BRES0067.
Повний текст джерелаAutonomous systems embed different types of sensors, applications and powerful calculators. Thus, they are used in different fields of application and perform various simple or complex tasks. Generally, these missions are executed in nondeterministic environments with the presence of random events that can affect the mission's progress. Therefore, it is necessary to regularly assess the health of the system and its hardware and software components in order to detect failures using Bayesian Networks.Subsequently, a decision is made by the mission planner by generating a new mission plan that ensures the mission in response to the detected event. This decision is made using the Markov Decision Process model based on constraints such as the mission objective, the health status of sensors and embedded applications, the mission policy "safety policy" or "mission first policy", etc. As autonomous systems perform different tasks that require different performance, it is necessary to consider the use of hardware accelerators on SoC-FPGA in order to meet high-performance computing constraints and unload the CPU if needed
Hoock, Jean-Baptiste. "Contributions to Simulation-based High-dimensional Sequential Decision Making." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00912338.
Повний текст джерелаRegatti, Jayanth Reddy. "Dynamic Routing for Fuel Optimization in Autonomous Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524145002064074.
Повний текст джерелаSantos, Felipe Martins dos. "Soluções eficientes para processos de decisão markovianos baseadas em alcançabilidade e bissimulações estocásticas." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-12022014-140538/.
Повний текст джерелаPlanning in artificial intelligence is the task of finding actions to reach a given goal. In planning under uncertainty, the actions can have probabilistic effects. This problems are modeled using Markov Decision Processes (MDPs), models that enable the computation of optimal solutions considering the expected value of each action when applied in each state. However, to solve big probabilistic planning problems, i.e., those with a large number of states and actions, is still a challenge. Large MDPs can be reduced by computing stochastic bisimulations, i.e., equivalence relations over the original MDP states. From the stochastic bisimulations, that can be exact or approximated, it is possible to get an abstract reduced model that can be easier to solve than the original MDP. But, for some problems, the stochastic bisimulation computation over the whole state space is unfeasible. The algorithms proposed in this work extend the algorithms that are used to compute stochastic bisimulations for MDPs in a way that they can be computed over the reachable set of states with a given initial state, which can be much smaller than the complete set of states. The empirical results show that it is possible to solve large probabilistic planning problems with better performance than the known techniques of stochastic bisimulation.
Skoglund, Caroline. "Risk-aware Autonomous Driving Using POMDPs and Responsibility-Sensitive Safety." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300909.
Повний текст джерелаAutonoma fordon förutspås spela en stor roll i framtiden med målen att förbättra effektivitet och säkerhet för vägtransporter. Men även om vi sett flera exempel av autonoma fordon ute på vägarna de senaste åren är frågan om hur säkerhet ska kunna garanteras ett utmanande problem. Det här examensarbetet har studerat denna fråga genom att utveckla ett ramverk för riskmedvetet beslutsfattande. Det autonoma fordonets dynamik och den oförutsägbara omgivningen modelleras med en partiellt observerbar Markov-beslutsprocess (POMDP från engelskans “Partially Observable Markov Decision Process”). Ett riskmått föreslås baserat på ett säkerhetsavstånd förkortat RSS (från engelskans “Responsibility-Sensitive Safety”) som kvantifierar det minsta avståndet till andra fordon för garanterad säkerhet. Riskmåttet integreras i POMDP-modellens belöningsfunktion för att åstadkomma riskmedvetna beteenden. Den föreslagna riskmedvetna POMDP-modellen utvärderas i två fallstudier. I ett scenario där det egna fordonet följer ett annat fordon på en enfilig väg visar vi att det egna fordonet kan undvika en kollision då det framförvarande fordonet bromsar till stillastående. I ett scenario där det egna fordonet ansluter till en huvudled från en ramp visar vi att detta görs med ett tillfredställande avstånd till andra fordon. Slutsatsen är att den riskmedvetna POMDP-modellen lyckas realisera en avvägning mellan säkerhet och användbarhet genom att hålla ett rimligt säkerhetsavstånd och anpassa sig till andra fordons beteenden.
Desquesnes, Guillaume Louis Florent. "Distribution de Processus Décisionnels Markoviens pour une gestion prédictive d’une ressource partagée : application aux voies navigables des Hauts-de-France dans le contexte incertain du changement climatique." Thesis, Ecole nationale supérieure Mines-Télécom Lille Douai, 2018. http://www.theses.fr/2018MTLD0001/document.
Повний текст джерелаThe work of this thesis aims to introduce and implement a predictive management under uncertainties of the water resource for inland waterway networks. The objective is to provide a water management plan to optimize the navigation conditions of the entire supervised network over a specified horizon. The expected solution must render the network resilient to probable effects of the climate change and changes in waterway traffic. Firstly, a generic modeling of a resource distributed on a network is proposed. This modeling, based on Markovian Decision Processes, takes into account the numerous uncertainties affecting considered networks. The objective of this modeling is to cover all possible cases, foreseen or not, in order to have a resilient management of those networks. The second contribution consists in a distribution of the model over several agents to facilitate the scaling. This consists of a repartition of the network's control capacities among the agents. Thus, each agent has only local knowledge of the supervised network. As a result, agents require coordination to provide an efficient management of the network. An iterative resolution, with exchanges of temporary plans from each agent, is used to obtain local management policies for each agent. Finally, experiments were carried out on realistic and real networks of the French waterways to observe the quality of the solutions produced. Several different climatic scenarios have been simulated to test the resilience of the produced policies
Sun-Hosoya, Lisheng. "Meta-Learning as a Markov Decision Process." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS588/document.
Повний текст джерелаMachine Learning (ML) has enjoyed huge successes in recent years and an ever- growing number of real-world applications rely on it. However, designing promising algorithms for a specific problem still requires huge human effort. Automated Machine Learning (AutoML) aims at taking the human out of the loop and develop machines that generate / recommend good algorithms for a given ML tasks. AutoML is usually treated as an algorithm / hyper-parameter selection problems, existing approaches include Bayesian optimization, evolutionary algorithms as well as reinforcement learning. Among them, auto-sklearn which incorporates meta-learning techniques in their search initialization, ranks consistently well in AutoML challenges. This observation oriented my research to the Meta-Learning domain. This direction led me to develop a novel framework based on Markov Decision Processes (MDP) and reinforcement learning (RL).After a general introduction (Chapter 1), my thesis work starts with an in-depth analysis of the results of the AutoML challenge (Chapter 2). This analysis oriented my work towards meta-learning, leading me first to propose a formulation of AutoML as a recommendation problem, and ultimately to formulate a novel conceptualisation of the problem as a MDP (Chapter 3). In the MDP setting, the problem is brought back to filling up, as quickly and efficiently as possible, a meta-learning matrix S, in which lines correspond to ML tasks and columns to ML algorithms. A matrix element S(i, j) is the performance of algorithm j applied to task i. Searching efficiently for the best values in S allows us to identify quickly algorithms best suited to given tasks. In Chapter 4 the classical hyper-parameter optimization framework (HyperOpt) is first reviewed. In Chapter 5 a first meta-learning approach is introduced along the lines of our paper ActivMetaL that combines active learning and collaborative filtering techniques to predict the missing values in S. Our latest research applies RL to the MDP problem we defined to learn an efficient policy to explore S. We call this approach REVEAL and propose an analogy with a series of toy games to help visualize agents’ strategies to reveal information progressively, e.g. masked areas of images to be classified, or ship positions in a battleship game. This line of research is developed in Chapter 6. The main results of my PhD project are: 1) HP / model selection: I have explored the Freeze-Thaw method and optimized the algorithm to enter the first AutoML challenge, achieving 3rd place in the final round (Chapter 3). 2) ActivMetaL: I have designed a new algorithm for active meta-learning (ActivMetaL) and compared it with other baseline methods on real-world and artificial data. This study demonstrated that ActiveMetaL is generally able to discover the best algorithm faster than baseline methods. 3) REVEAL: I developed a new conceptualization of meta-learning as a Markov Decision Process and put it into the more general framework of REVEAL games. With a master student intern, I developed agents that learns (with reinforcement learning) to predict the next best algorithm to be tried. To develop this agent, we used surrogate toy tasks of REVEAL games. We then applied our methods to AutoML problems. The work presented in my thesis is empirical in nature. Several real world meta-datasets were used in this research. Artificial and semi-artificial meta-datasets are also used in my work. The results indicate that RL is a viable approach to this problem, although much work remains to be done to optimize algorithms to make them scale to larger meta-learning problems
Berggren, Andreas, Martin Gunnarsson, and Johannes Wallin. "Artificial intelligence as a decision support system in property development and facility management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-25535.
Повний текст джерелаByggbranschen har länge varit tveksamt till att applicera nya tekniker. Inom fastighetsutveckling bygger branschen mycket på att anställda tar med sig erfarenheter från ett projekt till ett annat. Dessa anställda lär sig hantera risker i samband med förvärv av mark men när dessa personer slutar eller går i pension försvinner kunskapen. Ett AI baserat beslutssystem som tar risk och marknad i beaktning vid förvärv av mark kan lära sig av varje projekt och ta med dessa kunskaper till framtida projekt. Inom fastighetsförvaltning skulle artificiell intelligens kunna effektivisera allokerandet av personal i den pågående verksamheten. Syftet med studien är att analysera hur företag i fastighetsbranschen kan förbättra sitt beslutstagande med hjälp av AI i utveckling av fastigheter samt fastighetsförvaltning. I denna studien har två fallstudier av två olika aktörer i fastighetsbranschen utförts. Ena aktören, Bygg-Fast, representerar fastighetsutveckling och den andra aktören, VGR, representerar fastighetsförvaltning. Studien bygger på intervjuer, diskussioner och insamlade data. Genom att kartlägga och sedan kvantifiera de risker samt marknadsindikatorer som är indata i processen kan ett underlag skapas. Underlaget kan användas för en modell som lägger grunden för ett AI baserat beslutsstödsystem som ska hjälpa fastighetsutvecklaren med att ta kalkylerade beslut i mark förvärvsprocessen. Genom att kartlägga hur ett flöde genom en fastighet ser ut kan mätpunkter sättas ut för att analysera hur lång tid aktiviteterna tar i den specifika verksamheten. Dessa mätvärden ger en samlad data som gör det lättare att planera verksamheten som bedrivs i fastigheten. Ett effektivare flöde kan uppnås genom att visualisera hela processen så personal kan allokeras till rätt del av flödet. Genom att vara flexibel och kunna planera om verksamheten snabbt ifall planering störs kan en hög effektivitet nås. Detta skulle kunna göras av ett AI baserat beslutsstödsystem som simulerar alternativa dagsplaneringar.
So, Mee Chi. "Optimizing credit limit policy by Markov decision process models." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/68761/.
Повний текст джерелаLiu, Yaxin. "Decision-Theoretic Planning under Risk-Sensitive Planning Objectives." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6959.
Повний текст джерелаZang, Peng. "Scaling solutions to Markov Decision Problems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42906.
Повний текст джерелаHudson, Joshua. "A Partially Observable Markov Decision Process for Breast Cancer Screening." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154437.
Повний текст джерелаTabaeh, Izadi Masoumeh. "On knowledge representation and decision making under uncertainty." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103012.
Повний текст джерелаIn this thesis, we present a two-fold approach for improving the tractability of POMDP planning. First, we focus on designing good heuristics for POMDP approximation algorithms. We aim to scale up the efficiency of a class of POMDP approximations called point-based planning methods by designing a good planning space. We study the effect of three properties of reachable belief state points that may influence the performance of point-based approximation methods. Second, we investigate approaches to designing good controllers using an alternative representation of systems with partial observability called Predictive State Representation (PSR). This part of the thesis advocates the usefulness and practicality of PSRs in planning under uncertainty. We also attempt to move some useful characteristics of the PSR model, which has a predictive view of the world, to the POMDP model, which has a probabilistic view of the hidden states of the world. We propose a planning algorithm motivated by the connections between the two models.
Lommel, Peter Hans. "An extended Kalman filter extension of the augmented Markov decision process." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32453.
Повний текст джерелаIncludes bibliographical references (p. 99-102).
As the field of robotics continues to mature, individual robots are increasingly capable of performing multiple complex tasks. As a result, the ability for robots to move autonomously through their environments is a fundamental necessity. If perfect knowledge of the robot's position is available, the robot motion planning problem can be solved efficiently using any of a number of existing algorithms. Frequently though, the robot's position can only be estimated using incomplete and imperfect information from its sensors and an approximate model of its dynamics. Algorithms which assume perfect knowledge of the robot's position can still be applied by treating the mean or maximum likelihood estimate of the robot's position as certain. However, unless the uncertainty in the agent's position is very small, this approach is not reliable. In order to perform optimally in this situation, planners, such as the partially observable Markov decision process, plan over the entire set of beliefs (distributions over the robot's position). Unfortunately, this approach is only tractable for problems with very few states. Between these two extreme approaches, however, lies a continuum of possible planners which plan over a subset of the belief space. The difficulty that these planners face is choosing and representing a minimal subset of the belief space which spans the set of beliefs that the robot will actually experience. In this paper, we show that there exists a very natural such set, the set, of Gaussian beliefs. By combining an extended Kalman filter with an augmented Markov decision process, we create a path planner which efficiently plans over a discrete approximation of the set of Gaussian beliefs.
(cont.) The resulting planner is demonstrated via simulation to be both computationally tractable and robust to uncertainty in the robot's position.
by Peter Hans Lommel.
S.M.
Hamroun, Youcef F. "The decision-making process in metropolitan planning organizations." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.19 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1435819.
Повний текст джерелаBalali, Samaneh. "Incorporating expert judgement into condition based maintenance decision support using a coupled hidden markov model and a partially observable markov decision process." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19510.
Повний текст джерелаChang, Yanling. "A leader-follower partially observed Markov game." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54407.
Повний текст джерелаWoodward, Mark P. "Framing Human-Robot Task Communication as a Partially Observable Markov Decision Process." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10188.
Повний текст джерелаEngineering and Applied Sciences
Morad, Olivia. "Prefetching control for on-demand contents distribution : a Markov decision process study." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20111/document.
Повний текст джерелаThe thesis context is concerned with the control of theOn-demand contents distribution networks. The performance of suchinteractive distributed systems basically depends on the prediction ofthe user behavior and the bandwidth as a critical network resource.Prefetching is a well-known predictive approach in the World Wide Webwhich avoids the response delays by exploiting some downtime thatpermits to anticipate the user future requests and takes advantage ofthe available network resources. Prefetching control is a vitaloperation for the On-demand interactive systems where the instantaneousresponse is the crucial factor for the system success. The controller insuch type of interactive system operates in an uncertain environment andmakes sequences of decisions with long and short term stochasticeffects. The difficulty, then, is to determine at every system statewhich contents to prefetch into the cache. The prefetching plan duringan interactive streaming session can be modeled as a sequential decisionmaking problem by a Markov Decision Process (MDP). We focus on theprefetching control problem in which the controller seeks to reach aZero-Cost system state as quickly as possible. We model this controlproblem as a Negative Stochastic Dynamic Programming problem in which weminimize the undiscounted total expected cost. Within this context, weaddressed the following research questions: 1) How to provide anoptimal/approximate-optimal prefetching policy that, maximizes thebandwidth utilization while minimizes the user's blocking and latencycosts incurred along the way? 2) How to exploit structure in theprefetching control model to help efficiently compute such prefetchingcontrol policy with both computational efforts and storage memoryreduction? 3) How to conduct a performance evaluation study to evaluatedifferent prefetching heuristic algorithms based on the context of thecontrol optimization rather than the context of theempirical/simulation. For studying our research problem, we developedour MDP prefetching control model, PREF-CT, we established itstheoretical properties and we solved it by the Value Iteration algorithmas MDP algorithm for computing the optimal prefetching policy. Forcomputing the optimal prefetching policy efficiently, we detected aspecial structure that achieves more compact control model. This specialstructure permits to develop two strategically different algorithmswhich improve the complexities of computing the optimal prefetchingpolicy: - the first one is the ONE-PASS which is based mainly on solvinga system of linear equations simultaneously in only one iteration,whereas the second is the TREE-DEC which is based on Markov decisiontree decomposition in which sequential sets of systems of equations aresolved. For overcoming the problem of the curse of dimensionalityresulting from the computation of the optimal prefetching policy, weproposed the prefetching heuristic algorithm: the Relevant BlocksPrefetching algorithm (RBP). For evaluating and comparing prefetchingpolicies computed by different prefetching heuristic algorithms, wepresented a framework based on different performance measures. Weapplied the suggested framework under different costs configurations anddifferent user behaviors to evaluate the prefetching policies computedby our proposed prefetching heuristic algorithm; the RBP. Compared tothe optimal prefetching policies, the experimental analysis provedsignificant performance of the prefetching policies of the RBP heuristicalgorithm. In addition, the RBP prefetching heuristic algorithm isdistinguished by a clustering property which is of importance to reducesignificantly the memory necessary to store the prefetching policy tothe controller
HUANG, HEXIANG. "APPLICATION OF VISUALIZATION IN URBAN PLANNING DECISION-MAKING PROCESS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100979099.
Повний текст джерелаSelvi, Ersin Suleyman. "Cognitive Radar Applied To Target Tracking Using Markov Decision Processes." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/81968.
Повний текст джерелаMaster of Science
Castro, Rivadeneira Pablo Samuel. "On planning, prediction and knowledge transfer in fully and partially observable Markov decision processes." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104525.
Повний текст джерелаCette thèse traite le problème de prises de décisions séquentielles en grand domaines. Les formalismes utilisés pour étudier ce problème sont processus de décision Markoviens entièrement ou partiellement observables (MDP et POMDPs, respectivement).La première contribution de cette thèse est une analyse théorique du comportement des POMDPs lorsque seulement sous-ensembles de l'ensemble d'observations sont utilisés. L'un de ces sous-ensembles est utilisé pour mettre à jour la confiance de l'agent sur son état actuel, tandis que l'autre est utilisé pour mesurer la performance de l'agent. Les comportements sont formalisés avec trois types de relations d'equivalence. La première relation place les états dans le même groupe en fonction de leurs valeurs en vertu des politiques optimales ou générales; la second relation place les etats dans le même groupe en fonction de leur capacité a predire sequences d'observations; la troisième relation est basé sur la bisimulation, qui est une relation d'equivalence bien connu emprunté à la théorie de la concurrence.Les relations de bisimulation peuvent être généralisés à métriques de bisimulation. Cette thèse présente métriques de bisimulation pour une MDP avec des actions prolongées (formalisées comme des options) et propose une nouvelle métrique de bisimulation qui fournit un resserrement des limites sur la différence de valeurs optimales. Une nouvelle preuve est fournie pour la convergence d'une méthode d'approximation pour le calcul le du métrique de bisimulation qui est basé sur un échantillonnage statistique. La nouvelle preuve permet de déterminer le nombre minimal d'échantillons nécessaires pour atteindre la qualité souhaitée de rapprochement avec une forte probabilité.Bien que mêtriques de bisimulation ont été précédemment utilisés pour la compression de l'espace d'état, cette thèse propose de les utiliser pour transférer des politiques d'un MDP à l'autre. Contrairement aux travaux de transfert existants,le mappage entre les deux systèmes est déterminé automatiquement par les métriques de bisimulation. Résultats théoriques sont présentés que limite la perte de l'optimalité encourus par la police transferée. Un certain nombre d'algorithmes sont introduites, qui sont évalués de façon empirique dans le contexte de la planification et de l'apprentissage.
Chen, Yu Fan Ph D. Massachusetts Institute of Technology. "Hierarchical decomposition of multi-agent Markov decision processes with application to health aware planning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93795.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 99-104).
Multi-agent robotic systems have attracted the interests of both researchers and practitioners because they provide more capabilities and afford greater flexibility than single-agent systems. Coordination of individual agents within large teams is often challenging because of the combinatorial nature of such problems. In particular, the number of possible joint configurations is the product of that of every agent. Further, real world applications often contain various sources of uncertainties. This thesis investigates techniques to address the scalability issue of multi-agent planning under uncertainties. This thesis develops a novel hierarchical decomposition approach (HD-MMDP) for solving Multi-agent Markov Decision Processes (MMDPs), which is a natural framework for formulating stochastic sequential decision-making problems. In particular, the HD-MMDP algorithm builds a decomposition structure by exploiting coupling relationships in the reward function. A number of smaller subproblems are formed and are solved individually. The planning spaces of each subproblem are much smaller than that of the original problem, which improves the computational efficiency, and the solutions to the subproblems can be combined to form a solution (policy) to the original problem. The HD-MMDP algorithm is applied on a ten agent persistent search and track (PST) mission and shows more than 35% improvement over an existing algorithm developed specifically for this domain. This thesis also contributes to the development of the software infrastructure that enables hardware experiments involving multiple robots. In particular, the thesis presents a novel optimization based multi-agent path planning algorithm, which was tested in simulation and hardware (quadrotor) experiment. The HD-MMDP algorithm is also used to solve a multi-agent intruder monitoring mission implemented using real robots.
by Yu Fan Chen.
S.M.
West, Aaron P. "A decision support system for fabrication process planning in stereolithography." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/16896.
Повний текст джерелаRezende, Marisa Barcia Guaraldo Marcondes. "Planning and citizenship : decision-making in the planning process in Campo Grande, MS, Brazil." Thesis, University of Sheffield, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240366.
Повний текст джерелаErdogdu, Utku. "Efficient Partially Observable Markov Decision Process Based Formulation Of Gene Regulatory Network Control Problem." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614317/index.pdf.
Повний текст джерелаthey are mostly simulated as mathematical models that represent relationships between genes. Though it turns into a more challenging problem, we argue that partial observability would be a more natural and realistic method for handling the control of GRNs. Partial observability is a fundamental aspect of the problem
it is mostly ignored and substituted by the assumption that states of GRN are known precisely, prescribed as full observability. On the other hand, current works addressing partially observability focus on formulating algorithms for the nite horizon GRN control problem. So, in this work we explore the feasibility of realizing the problem in a partially observable setting, mainly with Partially Observable Markov Decision Processes (POMDP). We proposed a POMDP formulation for the innite horizon version of the problem. Knowing the fact that POMDP problems suer from the curse of dimensionality, we also proposed a POMDP solution method that automatically decomposes the problem by isolating dierent unrelated parts of the problem, and then solves the reduced subproblems. We also proposed a method to enrich gene expression data sets given as input to POMDP control task, because in available data sets there are thousands of genes but only tens or rarely hundreds of samples. The method is based on the idea of generating more than one model using the available data sets, and then sampling data from each of the models and nally ltering the generated samples with the help of metrics that measure compatibility, diversity and coverage of the newly generated samples.
Ni, Wenlong. "Optimal call admission control policies in wireless cellular networks using Semi Markov Decision Process /." Connect to full text in OhioLINK ETD Center, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1227028023.
Повний текст джерелаWongsuphasawat, Luxmon. "Extended metropolitanisation and the process of industrial location decision-making in Thailand." Thesis, University of Hull, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264954.
Повний текст джерелаChippa, Mukesh K. "Goal-seeking Decision Support System to Empower Personal Wellness Management." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1480413936639467.
Повний текст джерелаAllen, Martha Paralee. "A constructivist study of the decision-making process in permanency planning." CSUSB ScholarWorks, 1993. https://scholarworks.lib.csusb.edu/etd-project/688.
Повний текст джерелаGokhale, Mihir. "Use of analytical hierarchy process in university strategy planning." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.mst.edu/thesis/pdf/thesis_mihir_09007dcc804ef452.pdf.
Повний текст джерелаVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 29, 2008) Includes bibliographical references (p. 96-100).
Alasmari, Khalid R. "Novel methods for Reduced Energy and Time Consumption for Mobile Devices using Markov Decision Process." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1587906971444328.
Повний текст джерелаHuh, Keun S. M. Massachusetts Institute of Technology. "Asian real estate investment : data utilization for the decision making process." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42020.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaf 38).
Many investors in developed countries believe the Asian emerging market to be highly risky due to numerous uncertainties including limited market information to make sound investment decisions. However, still successful investment deals were completed by many investors who are already in such markets, and how those investors make sound investment decisions is in need on explanation. Therefore, this thesis explores institutional real estate investors' behavior on entrance to Asian emerging markets, their investment decision making process, and data utilization within each process. Through 14 qualitative in-depth interviews with key institutional investors in the U.S. and Korea, investment decision making processes and data utilization behaviors are examined and summarized in a generalized format. Interview results indicate that an initial entrance to Asian emerging markets occurs rather through a passive introduction of a potential deal by third party than an active search for a target deal by the investor. Also, the general decision-making process for real estate investment starts from sourcing and sorting, followed by review of a potential deal, primary investment committee meeting, due diligence, secondary investment committee meeting, and close of the deal. Each stage of the decision-making process is highly interconnected with simultaneous communication among responsible teams and the investment committee. The Korean real estate market, at least for the office sector, has such abundant market information available that it is rather perceived as "emerged." Investors also have extensive experience in the market and use their market knowledge and third party information as a reference point to verify the deal information. However, they also physically collect market data and use their own internal transaction record when necessary. Furthermore, data utilization in Korea is found to be easy and convenient especially when combined with extensive market experience and a wide professional network in the industry.
by Keun Huh.
S.M.in Real Estate Development
Ngai, Ka-kui. "Web-based intelligent decision support system for optimization of polishing process planning." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558472.
Повний текст джерелаNgai, Ka-kui, and 魏家駒. "Web-based intelligent decision support system for optimization of polishing process planning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558472.
Повний текст джерела