24 citations · 45 across the 21 of their papers we have counts for
26 papers
Aspiration-based Perturbed Learning Automata in Weakly-Acyclic Games with Noisy Utility Measurements
Georgios C. Chasparis
Reinforcement-based learning dynamics may exhibit several limitations when applied in a distributed setup. In (repeatedly-played) multi-player/action strategic-form games, and when…
Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games
Georgios C. Chasparis
Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measurement- or payoff-based optimization…
Causally-Guided Pairwise Transformer -- Towards Foundational Digital Twins in Process Industry
Michael Mayr, Georgios C. Chasparis
Foundational modelling of multi-dimensional time-series data in industrial systems presents a central trade-off: channel-dependent (CD) models capture specific cross-variable dynam…
Hourly Short Term Load Forecasting for Residential Buildings and Energy Communities
Aleksei Kychkin, Georgios C. Chasparis
Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenou…
Causal Time-Series Synchronization for Multi-Dimensional Forecasting
Michael Mayr, Georgios C. Chasparis, Josef Küng
The process industry's high expectations for Digital Twins require modeling approaches that can generalize across tasks and diverse domains with potentially different data dimensio…
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry
Michael Mayr, Georgios C. Chasparis, Josef Küng
Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticate…
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis +4
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrate…
Automated Knowledge Graph Learning in Industrial Processes
Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr +1
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for auto…
AI-Powered Predictions for Electricity Load in Prosumer Communities
Aleksei Kychkin, Georgios C. Chasparis
The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a.k.a., prosume…
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz +7
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and appl…
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learni…
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situation…
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines
Ammar N. Abbas, Georgios Chasparis, John D. Kelleher
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages…
Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing
Manuela Geiß, Martin Baresch, Georgios Chasparis +3
We present an end-to-end framework for fast retraining of object detection models in human-robot-collaboration. Our Faster R-CNN based setup covers the whole workflow of automatic…
Aspiration-based Perturbed Learning Automata
Georgios C. Chasparis
This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedly-played strategic-form games. Standard reinforcement-based learning exhibits se…
Learning-based Dynamic Pinning of Parallelized Applications in Many-Core Systems
Georgios C. Chasparis, Vladimir Janjic, Michael Rossbory
Motivated by the need for adaptive, secure and responsive scheduling in a great range of computing applications, including human-centered and time-critical applications, this paper…
Measurement-based Efficient Resource Allocation with Demand-Side Adjustments
Georgios Chasparis
The problem of efficient resource allocation has drawn significant attention in many scientific disciplines due to its direct societal benefits, such as energy savings. Traditional…
Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games
Georgios C. Chasparis
This paper considers a class of reinforcement-based learning (namely, perturbed learning automata) and provides a stochastic-stability analysis in repeatedly-played, positive-utili…
An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems
Georgios C. Chasparis, Michael Rossbory, Verena Haunschmid
We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem. Cutting-stock problems are…
Stochastic Stability Analysis of Perturbed Learning Automata with Constant Step-Size in Strategic-Form Games
Georgios C. Chasparis
This paper considers a class of reinforcement-learning that belongs to the family of Learning Automata and provides a stochastic-stability analysis in strategic-form games. For thi…
Supervisory Output Prediction for Bilinear Systems by Reinforcement Learning
Georgios C. Chasparis, Thomas Natschlaeger
Online output prediction is an indispensable part of any model predictive control implementation, especially when simplifications of the underlying physical model have been conside…
Generalized Online Transfer Learning for Climate Control in Residential Buildings
Thomas Grubinger, Georgios Chasparis, Thomas Natschlaeger
This paper presents an online transfer learning framework for improving temperature predictions in residential buildings. In transfer learning, prediction models trained under a se…
Regression Models for Output Prediction of Thermal Dynamics in Buildings
Georgios C. Chasparis, Thomas Natschlaeger
Standard (black-box) regression models may not necessarily suffice for accurate identification and prediction of thermal dynamics in buildings. This is particularly apparent when e…
Efficient Dynamic Pinning of Parallelized Applications by Distributed Reinforcement Learning
Georgios C. Chasparis, Michael Rossbory
This paper introduces a resource allocation framework specifically tailored for addressing the problem of dynamic placement (or pinning) of parallelized applications to processing…