Modelling (complex) systems for decision making using multi-agent reinforcement learning methods (MARL) for the computation of agents’ joint policies for action in critical domains is a priority and challenge here. Specifically, we aim to resolve cases in large-scale and complex settings where agents have conflicting preferences, but need to act jointly and in coordination to resolve common problems.
For instance agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others’ payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. These problems can be also considered as Markov games in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system.
A challenge to address here concerns the level of automation that the system can accommodate, so as to address decision making transparency and trust issues.
Specific Topics
- Modelling (e.g. social, traffic, etc) real-life systems via multi-agent systems
- Reinforcement Learning for Complex Decision Making in multi-agent settings.
Recent Publications
2019 |
Kravaris, Theocharis; et al., Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods Journal Article arXiv, cs.MA , 2019. @article{354, title = {Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods}, author = {Theocharis Kravaris and et al.}, url = {https://arxiv.org/pdf/1912.06860.pdf}, year = {2019}, date = {2019-12-01}, journal = {arXiv}, volume = {cs.MA}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Spatharis, C; et al., Collaborative multiagent reinforcement learning schemes for air traffic management Conference IISA 2019, 2019. @conference{349, title = {Collaborative multiagent reinforcement learning schemes for air traffic management}, author = {C Spatharis and et al.}, year = {2019}, date = {2019-01-01}, booktitle = {IISA 2019}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Doulkeridis, Christos; Qu, Qiang; Vouros, George A; ~a, Jo Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data. Journal Article GEOINFORMATICA, 23 , 2019. @article{353, title = {Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data.}, author = {Christos Doulkeridis and Qiang Qu and George A Vouros and Jo ~a}, url = {https://link.springer.com/article/10.1007%2Fs10707-019-00374-x}, year = {2019}, date = {2019-01-01}, journal = {GEOINFORMATICA}, volume = {23}, chapter = {235}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2018 |
Vouros, George A; et al., Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges Proceeding 2018. @proceedings{329, title = {Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges}, author = {George A Vouros and et al.}, year = {2018}, date = {2018-01-01}, journal = {EDBT 2018}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Kofinas, Panagiotis; Dounis, Anastasios; Vouros, George A Fuzzy Q-Learning for Multi-Agent Decentralized Energy Management in Microgrids Journal Article Applied Energy, (accepted) , 2018. @article{332, title = {Fuzzy Q-Learning for Multi-Agent Decentralized Energy Management in Microgrids}, author = {Panagiotis Kofinas and Anastasios Dounis and George A Vouros}, year = {2018}, date = {2018-01-01}, journal = {Applied Energy}, volume = {(accepted)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |