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Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach

Tseremoglou, I. (author), Santos, Bruno F. (author)

In the Condition-Based Maintenance (CBM) context, the definition of optimal maintenance plans for an aircraft fleet depends on an efficient integration of : (i) the probabilistic predictions of the health condition of the components and (ii) the stochastic arrival of the corrective maintenance tasks, together with consideration of the...

journal article 2024

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Investigating Inverse Reinforcement Learning from Human Behavior: Effect of Demonstrations with Temporal Biases on Learning Rewards using Inverse Reinforcement Learning

Zatezalo, Mateja (author)

Inverse Reinforcement Learning (IRL) is a machine learning technique used for learning rewards from the behavior of an expert agent. With complex agents, such as humans, the maximized reward may not be easily retrievable. This is because humans are prone to cognitive biases. Cognitive biases are a form of deviation from rationality that affects...

bachelor thesis 2023

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Incorporating climate change in I&M planning for engineering structures - A Deep Reinforcement Learning Framework

Knoppert, Sammie (author)

In the last decades, climate change is causing our environment to change rapidly, unprecedented in recent history. Civil engineering structures are dependent on the deteriorating environment they are situated in. Changes can cause an increase in loading due to, for example, extreme weather events or alter the structure’s resistance by, for...

master thesis 2023

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On the road from Model-Based Dynamical Programming to Model-Free Reinforcement Learning: A sample efficient approach

Mur Uribe, Pol (author)

This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations...

master thesis 2023

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A Comparative Study of Optimization Models for Condition-Based Maintenance Scheduling of an Aircraft Fleet

Tseremoglou, I. (author), van Kessel, Paul J. (author), Santos, Bruno F. (author)

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of...

journal article 2023

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Distributionally Robust Strategy Synthesis for Switched Stochastic Systems

Gracia, Ibon (author), Boskos, D. (author), Laurenti, L. (author), Mazo, M. (author)

We present a novel framework for formal control of uncertain discrete-time switched stochastic systems against probabilistic reach-avoid specifications. In particular, we consider stochastic systems with additive noise, whose distribution lies in an ambiguity set of distributions that are ε−close to a nominal one according to the Wasserstein...

conference paper 2023

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Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

Morato, P. G. (author), Andriotis, C. (author), Papakonstantinou, K. G. (author), Rigo, P. (author)

In the context of modern engineering, environmental, and societal concerns, there is an increasing demand for methods able to identify rational management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I...

journal article 2023

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Interval Markov Decision Processes with Continuous Action-Spaces

Delimpaltadakis, Giannis (author), Lahijanian, Morteza (author), Mazo, M. (author), Laurenti, L. (author)

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with...

conference paper 2023

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Dynamic Programming based basicity control of an experimental smelting furnace prototype

Vitanov, George (author)

This thesis discusses the chemical composition (basicity) control problem of HIsarna, an experimental iron furnace which operates with 30% less CO2 emissions than its traditional blast furnace counterparts. The control challenge is keeping the basicity of the plant in a narrow operating region. A mass balance model of the plant was constructed -...

master thesis 2022

document

Generalized Models of Sequential Decision-Making under Uncertainty

Neustroev, G. (author)

Sequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable...

doctoral thesis 2022

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Optimal maintenance of deteriorating systems integrating deep reinforcement learning and Bayesian inference

Lathourakis, Christos (author)

An issue of utmost significance constitutes the maintenance of engineering systems exposed to corrosive environments, e.g. coastal and marine environments, highly acidic environments, etc. The most beneficial sequence of maintenance decisions, i.e. the one that corresponds to the minimum maintenance cost, can be sought as the solution to an...

master thesis 2022

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Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Meijer, Caspar (author)

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

bachelor thesis 2022

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REAL Reinforcement Learning: Planning with adversarial models

Foffano, Daniele (author)

Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...

master thesis 2022

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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

abstract 2022

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Migration-aware Network Services with Edge Computing

Mukhopadhyay, Atri (author), Iosifidis, G. (author), Ruffini, Marco (author)

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we...

journal article 2022

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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity...

journal article 2022

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Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning

Altamimi, Abdulelah (author), Lagoa, Constantino (author), Borges, José G. (author), McDill, Marc E. (author), Andriotis, C. (author), Papakonstantinou, K. G. (author)

Forest management can be seen as a sequential decision-making problem to determine an optimal scheduling policy, e.g., harvest, thinning, or do-nothing, that can mitigate the risks of wildfire. Markov Decision Processes (MDPs) offer an efficient mathematical framework for optimizing forest management policies. However, computing optimal MDP...

journal article 2022

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A Markovian decision model of adaptive cancer treatment and quality of life

Bayer, Péter (author), Brown, Joel S. (author), Dubbeldam, J.L.A. (author), Broom, Mark (author)

This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patient's Markov Decision Problem, with the objective of maximizing the patient's discounted expected quality of life years. Patients make decisions on the duration of therapy based on the progression of the disease as well as...

journal article 2022

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Formal Control Synthesis for Stochastic Neural Network Dynamic Models

Adams, S.J.L. (author), Lahijanian, Morteza (author), Laurenti, L. (author)

Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components. Due to complexity of NNs, however, existing methods are unable to synthesize complex behaviors with guarantees for NN dynamic models (NNDMs). This letter introduces a control synthesis framework for...

journal article 2022

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Optimizing deep reinforcement learning policies for deteriorating systems considering ordered action structuring and value of information

Andriotis, C. (author), Papakonstantinou, K.G. (author)

Inspection and maintenance (I&M) optimization entails many sources of computational complexity, among others, due to high-dimensional decision and state variables in multi-component systems, long planning horizons, stochasticity of objectives and constraints, and inherent uncertainties in measurements and models. This paper studies how the...

conference paper 2022