Publications

Dissertation/Theses:

G. Chasparis, “Distributed Learning and Efficient Outcomes in Uncertain and Dynamic Environments,” PhD Dissertation, Department of Mechanical & Aerospace Engineering, University of California Los Angeles, Los Angeles, June 2008.
G. Chasparis, “Linear-Programming-Based Multi-Vehicle Path Planning with Adversaries,” Master’s Thesis, Department of Mechanical & Aerospace Engineering, University of California Los Angeles, Los Angeles, June 2004.
G. Chasparis, “Analysis and Model-Based Control with Servomechanisms with Friction (in Greek),” Diploma Thesis, Department of Mechanical Engineering, National Technical University of Athens, Athens, November 2001.

Journal Papers/Preprints:

[22] Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher, “Specialized Deep Residual Policy Reinforcement Learning Framework for Safe and Adaptive Continuous Control,” IET Control Theory & Applications, 2026. (accepted for publication)

[21] Almhaithawi, Doaa, Alessandro Bellini, Georgios C. Chasparis, and Tania Cerquitelli. “Investigating the Potential of Latent Space for the Classification of Paint Defects.” Journal of Imaging 11, no. 2 (February 2025): 33. https://doi.org/10.3390/jimaging11020033.

[20] Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz, Houda Briwa, Andres Alonso Perez, Gabriele Baldissone, Micaela Demichela, Georgios C. Chasparis, John D. Kelleher and Maria Chiara Leva (2024). 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. International Journal of Human–Computer Interaction, 1–35. https://doi.org/10.1080/10447318.2024.2391605

[19] S. Luftensteiner and G. Chasparis, “Integrating Expert Knowledge into Feature Selection,” Knowledge-based Systems, 2023 (submitted).

[18] A. Abbas, G. Chasparis, J. Kelleher, “Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance,” Data & Knowledge Engineering, vol 149, 2024. https://doi.org/10.1016/j.datak.2023.102240

[17] A. Kychkin, G. Chasparis, “Feature and Model Selection for Day-ahead Electricity Load Forecasting in Residential Buildings,” Energy and Buildings, vol. 249, 2021, DOI: https://doi.org/10.1016/j.enbuild.2021.111200

[16] S. Luftensteiner, M. Mayr, G. Chasparis, M. Pichler, “A Versatile Usable Big Data Infrastructure for Process Industry and Its Monitoring Applications,” Frontiers of Chemical Engineering: Computational Methods for Chemical Engineering, vol. 3, 2021, DOI: 10.3389/fceng.2021.665545.

[15] G. Chasparis, M. Pichler, J. Spreitzhofer and T. Esterl, “A Cooperative Demand-Response Framework for Day-ahead Optimization in Battery Pools,” Energy Informatics, vol. 2, 2019.

[14] G. Chasparis, “Measurement-based Efficient Resource Allocation with Demand-Side Adjustments“, Automatica, vol. 106, 2019. (also available in arXiv)

[13] G. Chasparis, “Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games“, IEEE Transactions on Automatic Control, 2019. (also available in arXiv)

[12] G. Chasparis, M. Rossbory, “Efficient Dynamic Pinning of Parallelized Applications by Distributed Reinforcement Learning,” International Journal of Parallel Programming, vol 47(1), pp. 24-38, 2019. (also available in arXiv)

[11] G. Chasparis, M. Rossbory, V. Haunschmidt, and C. Lettner “An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems”, (submitted to European Journal of Operational Research), 2017. (also available in arXiv)

[10] G. Chasparis, T. Natschlaeger, “Supervisory Output Prediction for Bilinear Systems by Reinforcement Learning“, IET Control Theory and Applications, vol 11(10), 2017, pp. 1514-1521.

[9] T. Grubinger, G. Chasparis, T. Natschlaeger, “Generalized Online Transfer Learning for Climate Control in Residential Buildings“, Energy and Buildings, vol 139, 2017, pp. 63-71.

[8] G. Chasparis, T. Natschlaeger, “Regression Models for Output Prediction of Thermal Dynamics in Buildings“, ASME Journal of Dynamic Systems, Measurement and Control, doi:10.1115/1.4034746, 2016. (arXiv)

[7] G. Chasparis, M. Maggio, E. Bini and K.-E. Arzen, “Design and Implementation of Game-Theoretic Resource Management for Time-Sensitive Applications,” Automatica (2016), pp. 44-53, 2015.

[6] G. Chasparis, J. Shamma and A. Rantzer, “Nonconvergence to Saddle Boundary Points under Perturbed Reinforcement Learning,” International Journal of Game Theory, Sep. 2014. (A Preliminary Draft was presented at GAMES 2012.)

[5] G. Chasparis, A. Arapostathis, J. Shamma, “Aspiration Learning in Coordination Games,” SIAM Journal on Control and Optimization, Vol 51, No 1, 2013, pp. 465-490. (also available in arXiv)

[4] G. Chasparis and J. Shamma, “Network Formation: Neighborhood Structures, Establishment Costs, and Distributed Learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol 43, No 6, Dec 2013, pp. 1950-1962.

[3] G. Chasparis and J. Shamma, “Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination and Network Formation,” Dynamic Games and Applications, Vol 2, no 1, 2012, pp. 18-50.

[2] G. Chasparis and J. Shamma, “LP-based multi-vehicle path planning with adversaries,” pages 261-279. In Shamma, Jeff (ed.), Cooperative Control of Distributed Multi-Agent Systems, John Wiley & Sons, February 2008.

[1] E. Papadopoulos and G. Chasparis, “Analysis and Model-based Control of Servomechanisms with Friction” ASME J. Dynamic Systems, Measurement and Control, Vol. 126, No. 4, December 2004.

Conference Papers:

[49] L. Gaisberger, G. Chasparis, W. Traunmüller, “Load Shifting in Energy Communities by Providing User-Centered Recommendations – Forecast, Optimization and Potential,” EU PVSEC 2024, 23-27 Sep 2024. DOI: 10.4229/EUPVSEC2024/5DV.2.17

[48] M. Mayr, G. Chasparis, and J. Küng, “Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry,” R. Wrembel et al. (Eds.): DaWaK 2024, LNCS 14912, pp. 34-47, 2024. DOI: https://doi.org/10.1007/978-3-031-68323-7_3

[47] S. Luftensteiner, G. Chasparis, and J. Küng, “PAS- A Feature Selection Process Definition for Industrial Settings,” Procedia Computer Science, vol 232, pp. 308-316, 2024. DOI: 10.1016/j.procs.2024.01.030

[46] A. Kychkin and G. Chasparis, “AI-Powered Predictions for Electricity Load in Prosumer Communities,” 18 Symposium Energieinnovation, 14-16.02.2024, Graz/Austria.

[45] A. Kychkin and G. Chasparis, “Automated Cross Channel Temperature Predictions for the PFR Lime Kiln Operating Support,” 31st Mediterranean Conference on Control and Automation, 2023. doi: 10.1109/MED59994.2023.10185790

[44] G. Chasparis, “Predictive Modeling for Flexibility Load Forecasting in Prosumer Communities“, 13. Internationale Energiewirtschaftstagung (IEWT 2023), Vienna, Austria, 2023. (presentation)

[43] S. Luftensteiner, G. Chasparis, M. Mayr, “Gathering expert knowledge in Process Industry,” Procedia Computer Science, vol 217, pp 960-968, 2023. doi: https://doi.org/10.1016/j.procs.2022.12.293

[42] A. Abbas, G. Chasparis, J. Kelleher, “Deep Residual Policy Reinforcement Learning as a Corrective Term in Process Control for Alarm Reduction: A Preliminary Report,” Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022), 28th August – 1st September 2022, Dublin, Ireland doi:10.3850/978-981-18-5183-4_S33-07-668-cd

[41] M. Geiss, M. Baresch, G. Chasparis, E. Schweiger, N. Teringl, M. Zwick, “Fast and Automatic
Object Registration for Human-Robot Collaboration in Industrial Manufacturing,” Communications in Computer and Information Science, 1633 CCIS, pp. 232-242, 2022. DOI: 10.1007/978-3-031-14343-4_22

[40] A. Abbas, G. Chasparis, J. Kelleher, “Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13428 LNCS, pp. 133–148.

[39] G. Chasparis, S. Luftensteiner, and M. Mayr, “Generalized Input-Output Hidden-Markov-
Models for Supervising Industrial Processes,” Procedia Computer Science, vol 200, 2022, pp. 1402-1411.

[38] M. Mayr, S. Luftensteiner, and G. Chasparis, “Abstracting Process Mining Event Logs from
Process-State Data to Monitor Control-Flow of Industrial Processes,” Procedia Computer Science, vol 200, 2022, pp. 1442-1450.

[37] S. Luftensteiner, M. Mayr, G. Chasparis, “Filter-based Feature Selection Methods for Industrial Sensor Data: A Review,” Lecture Notes in Computer Science, 12925 LNCS, pp. 242-249, 2021. DOI: 10.1007/978-3-030-86534-4_23

[36] G. Chasparis and C. Lettner, “Reinforcement-Learning-based Optimization for Day-ahead Flexibility Extraction in Battery Pools,” IFAC World Congress, Berlin, 2020 (IFAC Papers-Online, vol 53(2), 2020, pp 13351-13358, https://doi.org/10.1016/j.ifacol.2020.12.170).

[35] A. Kychkin and G. Chasparis, “Feature Extraction for Day-ahead Electricity-Load Forecasting in Residential Buildings,” IFAC World Congress, Berlin, 2020 (IFAC Papers-Online, vol 53(2), 2020, pp 13094-13100, https://doi.org/10.1016/j.ifacol.2020.12.2269) .

[34] G. Chasparis, “Stochastic Stability of Perturbed Learning Automata in Positive Utility Games,” Learning Evolution and Games 2019, Tel-Aviv, Israel, 2019. paper, presentation

[33] G. Chasparis, M. Pichler and T. Natschlaeger, “A Demand-Response Framework in Balance Groups through Direct Battery-Storage Control,” European Control Conference 2019, Naples, Italy, 2019. paper, presentation

[32] G. Chasparis, M. Pichler, J. Spreitzhofer and T. Esterl, “A Cooperative Demand-Response Framework for Day-ahead Optimization in Battery Pools,” DACH+ Energy Informatics Conference, Salzburg, 2019.

[31] G. Chasparis, V. Janjic, M. Rossbory, and K. Hammond, “Learning-based Dynamic Pinning of Parallelized Applications in Many-Core Systems,” Euromicro PDP’19, Pavia, Italy, 2019.

[30] M. Pichler, et al., “Decentralized Energy Networks Based on Blockchain: Background, Overview and Concept Discussion,” BIS 2018 International Workshops, Berlin, Germany, July 18–20, 2018.

[29] G. Chasparis, “Aspiration-based Perturbed Learning Automata,” European Control Conference 2018, Cyprus, 2018. (also available in arXiv)

[28] F. Moisl, M. Pichler, G. Chasparis, K. Leonhartsberger, and G. Lettner, “Development of a Decentralized Small Battery Energy Storage Network to Compensate for Schedule Deviations,” In D. Schulze (Ed.), NEIS 2017, Conference on Sustainable Energy Supply and Energy Storage Systems, Hamburg, 21-22 September, pp. 169-174, 2017.

[27] G. Chasparis, M. Rossbory, and V. Janjic, “Efficient Dynamic Pinning of Parallelized Applications by Reinforcement Learning with Applications,” LNCS 10417, Euro-Par 2017, pp. 1-13, 2017.

[26] G. Chasparis, “Stochastic Stability Analysis of Perturbed Learning Automata with Constant Step-Size in Strategic-Form Games,” 2017 American Control Conference, Seattle, USA, May, 2017. (also available at arXiv)

[25] G. Chasparis, and M. Rossbory, “Efficient Dynamic Pinning of Parallelized Applications by Distributed Reinforcement Learning,” High-Level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU), Stockholm, Sweden, 2017. (also available in arXiv)

[24] G. Chasparis, W. Zellinger, V. Haunschmidt, M. Riedenbauer, and R. Stumptner, “On the Optimization of Material Usage in Power Transformers Manufacturing,” IEEE 8th International Conference on Intelligent Systems, Sofia, Bulgaria, 2016.

[23] T. Grubinger, G. Chasparis, T. Natschlaeger, “Online Transfer Learning for Climate Control in Residential Buildings,” European Control Conference, Aalborg, Denmark, 2016.

[22] G. Chasparis, and M. Rossbory, “Efficient Dynamic Pinning of Parallelized Applications by Distributed Reinforcement Learning,” 9th International Symposium on High-Level Parallel Programming and Applications, Münster, Germany, 2016. (also available in arXiv)

[21] M. Rossbory, and G. Chasparis, “Parallelization of Stochastic-Local-Search Algorithms using High-Level Parallel Patterns,” High-Level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU ’16), Prague, Czech Republic, 2016.

[20] J. Martinez-Gil, G. Chasparis, A. Boegl, C. Illibauer, B. Freudenthaler, and T. Natschläger, Framework for fast prototzping of energy-saving controllers,” 2015 26th International Workshop on Database and Expert Systems Applications (DEXA), 2015.

[19] G. Chasparis, “Reinforcement-Learning-Based Efficient Resource Allocation with Demand-Side Adjustments,” 2015 European Control Conference, Linz, Austria, July 15-17, 2015, pp. 3071-3077.

[18] G. Chasparis, T. Natschlaeger, “Supervisory System Identification for Bilinear Systems with Application to Thermal Dynamics in Buildings,” Multiconference on Systems and Control, Antibes-Nice, France, 2014.

[17] G. Chasparis, T. Natschlaeger, “Nonlinear System Identification of Thermal Dynamics in Buildings,” 13th European Control Conference, Strasbourg, France, 2014, pp. 1649 – 165.

[16] M. Maggio, E. Bini, G. Chasparis and K.-E. Årzen, ” A Game-Theoretic Resource Manager for RT Applications,” 25th Euromicro Conference on Real-Time Systems (ECRTS), 2013.

[15] G. Chasparis, A. Ranzter and K. Jörnsten, “A Decomposition Approach to Multi-Region Optimal Power Flow in Electricity Networks,” 12th European Control Conference, 2013, pp.

[14] G. Chasparis, M. Maggio, K.-E. Årzen and E. Bini, “Distributed Management of CPU Resources for Time-Sensitive Applications,” 2012. Accepted for publication at the American Control Conference, 2013.

[13] G. Chasparis, A. Arapostathis and J. Shamma, “Fair Scheduling in Common-Pool Games by Aspiration Learning,” 8th International Workshop on Resource Allocation and Cooperation in Wireless Networks, 2012.

[12] G. Chasparis, J. Shamma and A. Rantzer, “Nonconvergence to Saddle Boundary Points under Perturbed Reinforcement Learning,” 4th World Congress of the Game Theory Society, July 2012.

[11] G. Chasparis and J. Shamma, “Control of Preferences in Social Networks,” 4th World Congress of the Game Theory Society, July 2012.

[10] G. Chasparis, J. Shamma and A. Rantzer, “Perturbed Learning Automata in Potential Games,” 50th IEEE Conference on Decision and Control, Dec. 2011.

[9] G. Chasparis and J. Shamma, “Information Flow and Active Social Influence in Social Networks,” Interdisciplinary Workshop on Information and Decision in Social Networks (WIDS), May 31st-June 1st, 2011, MIT, Boston, MA. presentation

[8] G. Chasparis and J. Shamma, “Control of Preferences in Social Networks,” 49th IEEE Conference on Decision and Control, Dec. 2010.

[7] G. Chasparis, J. Shamma and A. Arapostathis, “Aspiration Learning in Coordination Games,” 49th IEEE Conference on Decision and Control, Dec. 2010.

[6] G. Chasparis and J. Shamma, “Efficient Network Formation by Distributed Reinforcement,” 47th IEEE Conference on Decision and Control, Dec. 2008.

[5] G. Chasparis and J. Shamma, “Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination,” Third World Congress of the Game Theory Society, July 2008.

[4] G. Chasparis and J. Shamma, “Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination,” European Control Conference (ECC), Greece, Kos, July 2-5, 2007.

[3] G. Chasparis and J. Shamma, “The Emergence of Efficient Social Networks by Dynamic Reinforcement,” Presented at the 4th Lake Arrowhead Conference on Human Complex Systems, April 25-29, 2007.

[2] G. Chasparis and J. Shamma, “Linear-Programming-Based Multi-Vehicle Path Planning with Adversaries,” 24th American Control Conference (ACC), Portland, Oregon, Jun. 8-10, 2005.

[1] E. Papadopoulos and G. Chasparis, “Analysis and Model-based Control of Servomechanisms with Friction,” Proc. of the 2002 IEEE/RSG Int. Conference on Intelligent Robots and Systems (IROS ’02), Lausanne, Switzerland, October 2002.

Technical Reports:

[3] G. Chasparis, M. Maggio, E. Bini and K.-E. Arzen, “Design and Implementation of Game-Theoretic Resource Management for Time-Sensitive Applications,” Technical Report SCCH-TR-1328, Software Competence Center Hagenberg, Hagenberg, Austria, 2013.
[2] G. Chasparis, B. Moser, “Architecture for modeling and optimization of agro-silvo-pastoral (ASP) systems,” Technical Report SCCH-TR-1302, Software Competence Center Hagenberg, Hagenberg, Austria, 2013.
[1] G. Chasparis, M. Maggio, E. Bini and K.-E. Arzen, “Distributed Management of CPU Resources for Time-Sensitive Applications,” Technical Report ISRN LUTFD2/TFRT–7625–SE, Department of Automatic Control, Lund University, Sweden, September 2012.

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