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Ann Nowé
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- affiliation: Vrije Universiteit Brussel, Artificial Intelligence Lab, Belgium
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2020 – today
- 2024
- [j78]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. RLJ 1: 108-129 (2024) - [j77]Mathieu Reymond
, Conor F. Hayes
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. Expert Syst. Appl. 249: 123686 (2024) - [j76]Gaoyuan Liu
Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning. IEEE Robotics Autom. Lett. 9(6): 5974-5981 (2024) - [j75]Arnau Dillen
User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface. Sensors 24(16): 5253 (2024) - [j74]Qingshuang Sun
Dynamic Size Message Scheduling for Multi-Agent Communication Under Limited Bandwidth. IEEE Trans. Mob. Comput. 23(12): 15080-15097 (2024) - [j73]Diana Gomes, Kyriakos Efthymiadis, Ann Nowé, Peter Vrancx:
Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior. Trans. Mach. Learn. Res. 2024 (2024) - [c204]Ann Nowé:
Trustworthy Reinforcement Learning: Opportunities and Challenges. AAMAS 2024: 1 - [c203]Mathieu Reymond, Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé:
Interactively Learning the User's Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. AAMAS 2024: 1611-1620 - [c202]Alexandra Cimpean, Catholijn M. Jonker, Pieter Libin, Ann Nowé:
A Reinforcement Learning Framework for Studying Group and Individual Fairness. AAMAS 2024: 2216-2218 - [c201]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. ICLR 2024 - [i53]Willem Röpke, Mathieu Reymond, Patrick Mannion, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning. CoRR abs/2402.07182 (2024) - [i52]Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann Nowé, Guillermo A. Pérez:
Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies. CoRR abs/2402.13785 (2024) - [i51]Axel Abels, Elias Fernández Domingos, Ann Nowé, Tom Lenaerts:
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News. CoRR abs/2403.08829 (2024) - [i50]Yannick Molinghen, Raphaël Avalos, Mark Van Achter, Ann Nowé, Tom Lenaerts:
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks. CoRR abs/2404.03596 (2024) - [i49]Florian Felten, Umut Ucak, Hicham Azmani, Gao Peng, Willem Röpke, Hendrik Baier, Patrick Mannion, Diederik M. Roijers, Jordan K. Terry, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Roxana Radulescu:
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning. CoRR abs/2407.16312 (2024) - [i48]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. CoRR abs/2407.18812 (2024) - [i47]Senne Deproost, Denis Steckelmacher, Ann Nowé:
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution. CoRR abs/2410.21940 (2024) - [i46]Nathanaël Fijalkow, Jan Kretínský, Ann Nowé, Gabriel Bathie:
Stochastic Games (Dagstuhl Seminar 24231). Dagstuhl Reports 14(6): 1-18 (2024) - 2023
- [j72]Mathieu Reymond
Actor-critic multi-objective reinforcement learning for non-linear utility functions. Auton. Agents Multi Agent Syst. 37(2): 23 (2023) - [j71]Axel Abels
Dealing with expert bias in collective decision-making. Artif. Intell. 320: 103921 (2023) - [j70]Nassim Versbraegen, Barbara Gravel, Charlotte Nachtegael
Faster and more accurate pathogenic combination predictions with VarCoPP2.0. BMC Bioinform. 24(1): 179 (2023) - [j69]Alexandre Renaux
A knowledge graph approach to predict and interpret disease-causing gene interactions. BMC Bioinform. 24(1): 324 (2023) - [j68]Gaoyuan Liu
Synergistic Task and Motion Planning With Reinforcement Learning-Based Non-Prehensile Actions. IEEE Robotics Autom. Lett. 8(5): 2764-2771 (2023) - [j67]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. Trans. Mach. Learn. Res. 2023 (2023) - [c200]Willem Röpke, Carla Groenland, Roxana Radulescu, Ann Nowé, Diederik M. Roijers:
Bridging the Gap Between Single and Multi Objective Games. AAMAS 2023: 224-232 - [c199]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
A Study of Nash Equilibria in Multi-Objective Normal-Form Games. AAMAS 2023: 269-271 - [c198]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c197]Lucas Nunes Alegre, Ana L. C. Bazzan, Diederik M. Roijers, Ann Nowé, Bruno C. da Silva:
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. AAMAS 2023: 2003-2012 - [c196]Yannick Molinghen, Raphaël Avalos, Mark Van Achter, Ann Nowé, Tom Lenaerts:
Laser Learning Environment: A New Environment for Coordination-Critical Multi-agent Tasks. BNAIC/BENELEARN 2023: 135-154 - [c195]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. ICLR 2023 - [c194]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. ICML 2023: 79-90 - [c193]Willem Röpke
Distributional Multi-Objective Decision Making. IJCAI 2023: 5711-5719 - [c192]Lucas Nunes Alegre, Ana L. C. Bazzan, Ann Nowé, Bruno C. da Silva:
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models. NeurIPS 2023 - [c191]Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan, El-Ghazali Talbi, Grégoire Danoy, Bruno C. da Silva:
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. NeurIPS 2023 - [e6]Kobi Gal, Ann Nowé, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu:
ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland - Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023). Frontiers in Artificial Intelligence and Applications 372, IOS Press 2023, ISBN 978-1-64368-436-9 [contents] - [i45]Willem Röpke, Carla Groenland, Roxana Radulescu, Ann Nowé, Diederik M. Roijers:
Bridging the Gap Between Single and Multi Objective Games. CoRR abs/2301.05755 (2023) - [i44]Lucas Nunes Alegre, Ana L. C. Bazzan, Diederik M. Roijers, Ann Nowé, Bruno C. da Silva:
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. CoRR abs/2301.07784 (2023) - [i43]Hélène Plisnier, Denis Steckelmacher, Jeroen Willems
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem. CoRR abs/2301.12820 (2023) - [i42]Alexandra Cimpean, Timothy Verstraeten, Lander Willem, Niel Hens, Ann Nowé, Pieter Libin:
Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration. CoRR abs/2301.12822 (2023) - [i41]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. CoRR abs/2303.03284 (2023) - [i40]Florent Delgrange, Ann Nowé, Guillermo A. Pérez:
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. CoRR abs/2303.12558 (2023) - [i39]Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke, Ann Nowé, Maarten Messagie:
Safe reinforcement learning with self-improving hard constraints for multi-energy management systems. CoRR abs/2304.08897 (2023) - [i38]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. CoRR abs/2305.01063 (2023) - [i37]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. CoRR abs/2305.05560 (2023) - [i36]Qingshuang Sun, Denis Steckelmacher, Yuan Yao, Ann Nowé, Raphaël Avalos:
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth. CoRR abs/2306.10134 (2023) - 2022
- [j66]Conor F. Hayes
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j65]Willem Röpke
On nash equilibria in normal-form games with vectorial payoffs. Auton. Agents Multi Agent Syst. 36(2): 53 (2022) - [j64]Charlotte Nachtegael
Scaling up oligogenic diseases research with OLIDA: the Oligogenic Diseases Database. Database J. Biol. Databases Curation 2022(2022) (2022) - [j63]Roxana Radulescu
Opponent learning awareness and modelling in multi-objective normal form games. Neural Comput. Appl. 34(3): 1759-1781 (2022) - [c190]Florent Delgrange, Ann Nowé, Guillermo A. Pérez
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes. AAAI 2022: 6497-6505 - [c189]Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Pareto Conditioned Networks. AAMAS 2022: 1110-1118 - [c188]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2022: 1524-1526 - [c187]Jeroen Willems
Fast Initialization of Control Parameters using Supervised Learning on Data from Similar Assets. CCTA 2022: 1214-1221 - [c186]Nixon K. Ronoh
Natural gradient evolution strategies for adaptive sampling. GECCO Companion 2022: 73-74 - [d2]Alexandre Renaux
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph. Version 1.0. Zenodo, 2022 [all versions] - [d1]Alexandre Renaux
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph. Version 1.0. Zenodo, 2022 [all versions] - [i35]Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. CoRR abs/2204.05027 (2022) - [i34]Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Pareto Conditioned Networks. CoRR abs/2204.05036 (2022) - [i33]Glenn Ceusters, Luis Ramirez Camargo
Safe reinforcement learning for multi-energy management systems with known constraint functions. CoRR abs/2207.03830 (2022) - 2021
- [j62]Ulle Endriss
Autonomous agents and multiagent systems: perspectives on 20 years of AAMAS. AI Matters 7(3): 29-37 (2021) - [j61]Joris De Winter
Autonomous assembly planning of demonstrated skills with reinforcement learning in simulation. Auton. Robots 45(8): 1097-1110 (2021) - [j60]Gianluca Bontempi
The CLAIRE COVID-19 initiative: approach, experiences and recommendations. Ethics Inf. Technol. 23(S1): 127-133 (2021) - [j59]Yannick De Bock
The energy saving potential of retrofitting a smart heating system: A residence hall pilot study. Sustain. Comput. Informatics Syst. 31: 100585 (2021) - [c185]Gaoyuan Liu
MoveRL: To a Safer Robotic Reinforcement Learning Environment. BNAIC/BENELEARN 2021: 239-253 - [e5]Frank Dignum, Alessio Lomuscio, Ulle Endriss, Ann Nowé:
AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021. ACM 2021, ISBN 978-1-4503-8307-3 [contents] - [i32]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i31]Glenn Ceusters
Model-predictive control and reinforcement learning in multi-energy system case studies. CoRR abs/2104.09785 (2021) - [i30]Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker, Ann Nowé:
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning. CoRR abs/2106.06009 (2021) - [i29]Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Dealing with Expert Bias in Collective Decision-Making. CoRR abs/2106.13539 (2021) - [i28]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Preference Communication in Multi-Objective Normal-Form Games. CoRR abs/2111.09191 (2021) - [i27]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
On Nash Equilibria in Normal-Form Games With Vectorial Payoffs. CoRR abs/2112.06500 (2021) - [i26]Florent Delgrange
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report). CoRR abs/2112.09655 (2021) - [i25]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2112.12458 (2021) - 2020
- [j58]Roxana Radulescu
Multi-objective multi-agent decision making: a utility-based analysis and survey. Auton. Agents Multi Agent Syst. 34(1): 10 (2020) - [j57]Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé:
AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings). J. Mach. Learn. Res. 21: 102:1-102:12 (2020) - [j56]Roxana Radulescu
A utility-based analysis of equilibria in multi-objective normal-form games. Knowl. Eng. Rev. 35: e32 (2020) - [j55]Gabriel de Oliveira Ramos
Toll-based reinforcement learning for efficient equilibria in route choice. Knowl. Eng. Rev. 35: e8 (2020) - [j54]Yannick De Bock
Nonparametric user activity modelling and prediction. User Model. User Adapt. Interact. 30(5): 803-831 (2020) - [c184]Gabriel de Oliveira Ramos, Roxana Radulescu, Ann Nowé, Anderson R. Tavares:
Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences. AAMAS 2020: 1098-1106 - [c183]Timothy Verstraeten, Eugenio Bargiacchi, Pieter J. K. Libin, Diederik M. Roijers, Ann Nowé:
Thompson Sampling for Factored Multi-Agent Bandits. AAMAS 2020: 2029-2031 - [c182]Yijie Zhang, Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games. AAMAS 2020: 2080-2082 - [c181]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. AAMAS 2020: 2158-2160 - [c180]Timothy Verstraeten
Fleet Control Using Coregionalized Gaussian Process Policy Iteration. ECAI 2020: 1571-1578 - [c179]Isel Grau
An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. FUZZ-IEEE 2020: 1-8 - [c178]Axel Abels
Collective Decision-Making as a Contextual Multi-armed Bandit Problem. ICCCI 2020: 113-124 - [c177]Axel Abels
How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems. ICCCI 2020: 125-138 - [c176]Yailen Martínez Jiménez
Multi-Agent Reinforcement Learning Tool for Job Shop Scheduling Problems. OLA 2020: 3-12 - [c175]Pieter J. K. Libin, Arno Moonens
Deep Reinforcement Learning for Large-Scale Epidemic Control. ECML/PKDD (5) 2020: 155-170 - [c174]Diederik M. Roijers
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. ECML/PKDD (3) 2020: 463-478 - [c173]Youri Coppens
Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning. TAILOR 2020: 163-179 - [i24]Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé:
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping. CoRR abs/2001.07527 (2020) - [i23]Roxana Radulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé:
A utility-based analysis of equilibria in multi-objective normal form games. CoRR abs/2001.08177 (2020) - [i22]Isel Grau, Dipankar Sengupta, María Matilde García Lorenzo, Ann Nowé:
An interpretable semi-supervised classifier using two different strategies for amended self-labeling. CoRR abs/2001.09502 (2020) - [i21]Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé:
Deep reinforcement learning for large-scale epidemic control. CoRR abs/2003.13676 (2020) - [i20]Roxana Radulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games. CoRR abs/2011.07290 (2020)
2010 – 2019
- 2019
- [j53]Oliver Roesler
Action learning and grounding in simulated human-robot interactions. Knowl. Eng. Rev. 34: e13 (2019) - [j52]Alexandre Renaux
ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations. Nucleic Acids Res. 47(Webserver-Issue): W93-W98 (2019) - [j51]Joris De Winter, Albert De Beir, Ilias El Makrini
Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot. Robotics 8(4): 104 (2019) - [c172]Youri Coppens, Eugenio Bargiacchi, Ann Nowé:
A Virtual Maze Game to Explain Reinforcement Learning. BNAIC/BENELEARN 2019 - [c171]Jannick Hemelhof, Mihail Mihaylov, Ann Nowé:
Improving Zero-Intelligence Plus for Call Markets. BNAIC/BENELEARN 2019 - [c170]Pieter Libin, Nassim Versbraegen, Ana B. Abecasis, Perpetua Gomes, Tom Lenaerts, Ann Nowé:
Towards a Phylogenetic Measure to Quantify HIV Incidence. BNAIC/BENELEARN 2019 - [c169]Pieter Libin, Nassim Versbraegen, Ana B. Abecasis
Towards a Phylogenetic Measure to Quantify HIV Incidence. BNAIC/BENELEARN (Selected Papers) 2019: 34-50 - [c168]Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Wenjia Wang, Kristof Theys, Ann Nowé:
Thompson Sampling for m-top Exploration. BNAIC/BENELEARN 2019 - [c167]Regis Loeb, Timothy Verstraeten, Ann Nowé, Ann Dooms:
Privacy Preserving Reinforcement Learning over Distributed Datasets. BNAIC/BENELEARN 2019 - [c166]Jessica Coto Palacio, Yailen Martínez Jiménez, Ann Nowé:
Multi-Agent Reinforcement Learning Tool for Job Shop Scheduling Problems. BNAIC/BENELEARN 2019 - [c165]Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers, Ann Nowé:
Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors. BNAIC/BENELEARN 2019 - [c164]Oliver Roesler, Ann Nowé:
Action Learning and Grounding in Simulated Human-Robot Interactions. BNAIC/BENELEARN 2019 - [c163]Willem Röpke, Roxana Radulescu, Kyriakos Efthymiadis, Ann Nowé:
Training a Speech-to-Text Model for Dutch on the Corpus Gesproken Nederlands. BNAIC/BENELEARN 2019 - [c162]Willem Röpke, Roxana Radulescu, Kyriakos Efthymiadis, Ann Nowé:
DuStt - A Speech-to-Text Engine for Dutch. BNAIC/BENELEARN 2019 - [c161]Denis Steckelmacher, Hélène Plisnier, Ann Nowé:
A Motorized Wheelchair that Learns to Make its Way through a Crowd. BNAIC/BENELEARN 2019 - [c160]Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, Ann Nowé:
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. BNAIC/BENELEARN 2019 - [c159]Timothy Verstraeten, Ann Nowé, Jan Helsen:
Failure Avoidance for Wind Turbines through Fleetwide Control. BNAIC/BENELEARN 2019 - [c158]Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten, Ann Nowé:
Deep hybrid approach for 3D plane segmentation. ESANN 2019 - [c157]Beatriz M. Méndez-Hernández
A Multi-objective Reinforcement Learning Algorithm for JSSP. ICANN (1) 2019: 567-584 - [c156]Axel Abels
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. ICML 2019: 11-20 - [c155]Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. ICML 2019: 2644-2652 - [c154]Pieter Libin
Bayesian Anytime m-top Exploration. ICTAI 2019: 1422-1428 - [c153]Richar Sosa, Alejandro Alfonso, Gonzalo Nápoles
Synaptic Learning of Long-Term Cognitive Networks with Inputs. IJCNN 2019: 1-8 - [c152]Denis Steckelmacher
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. ECML/PKDD (3) 2019: 19-34 - [c151]Angel Luis Scull Pupo
GUARDIAML: Machine Learning-Assisted Dynamic Information Flow Control. SANER 2019: 624-628 - [i19]Hélène Plisnier
The Actor-Advisor: Policy Gradient With Off-Policy Advice. CoRR abs/1902.02556 (2019) - [i18]Denis Steckelmacher
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. CoRR abs/1903.04193 (2019) - [i17]Timothy Verstraeten, Ann Nowé, Jonathan Keller, Yi Guo, Shuangwen Sheng, Jan Helsen:
Fleetwide data-enabled reliability improvement of wind turbines. CoRR abs/1903.11518 (2019) - [i16]Hélène Plisnier
Transfer Learning Across Simulated Robots With Different Sensors. CoRR abs/1907.07958 (2019) - [i15]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. CoRR abs/1909.02964 (2019) - [i14]Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten, Ann Nowé:
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers. CoRR abs/1909.13726 (2019) - [i13]Timothy Verstraeten, Eugenio Bargiacchi, Pieter J. K. Libin, Diederik M. Roijers, Ann Nowé:
Thompson Sampling for Factored Multi-Agent Bandits. CoRR abs/1911.10120 (2019) - [i12]Timothy Verstraeten, Pieter J. K. Libin, Ann Nowé:
Fleet Control using Coregionalized Gaussian Process Policy Iteration. CoRR abs/1911.10121 (2019) - 2018
- [j50]Yaima Filiberto Cabrera
A New Method For Personnel Selection Based On Ranking Aggregation Using A Reinforcement Learning Approach. Computación y Sistemas 22(2) (2018) - [j49]Sofie De Clercq, Steven Schockaert
Modelling incomplete information in Boolean games using possibilistic logic. Int. J. Approx. Reason. 93: 1-23 (2018) - [j48]Huong Thi Thu Vu
ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses. Sensors 18(7): 2389 (2018) - [c150]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning With Options That Terminate Off-Policy. AAAI 2018: 3173-3182 - [c149]Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, Peter Vrancx, Hélène Plisnier, Ann Nowé:
Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets. AAAI 2018: 4099-4106 - [c148]Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé:
Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees. AAAI 2018: 7831-7836 - [c147]Lázaro Lugo, Marilyn Bello, Ann Nowé
A Solution for the Team Selection Problem Using ACO. ANTS Conference 2018: 325-332 - [c146]Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé:
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making. AAMAS 2018: 1477-1485 - [c145]Roxana Radulescu
Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population. BNCAI 2018: 90-105 - [c144]Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt:
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems. ICML 2018: 491-499 - [c143]Felipe Gomez Marulanda, Pieter Libin
IPC-Net: 3D Point-Cloud Segmentation Using Deep Inter-Point Convolutional Layers. ICTAI 2018: 293-301 - [c142]Beatriz M. Méndez-Hernández, Jessica Coto Palacio, Yailen Martínez Jiménez, Ann Nowé
A Reinforcement Learning Approach for the Report Scheduling Process Under Multiple Constraints. IWAIPR 2018: 228-235 - [c141]Pieter J. K. Libin, Timothy Verstraeten, Diederik M. Roijers
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies. ECML/PKDD (3) 2018: 456-471 - [c140]Christophe Patyn
Intelligent Electric Water Heater Control with Varying State Information. SmartGridComm 2018: 1-6 - [i11]Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé:
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making. CoRR abs/1802.07606 (2018) - [i10]Hélène Plisnier, Denis Steckelmacher, Tim Brys, Diederik M. Roijers, Ann Nowé:
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice. CoRR abs/1808.04096 (2018) - [i9]Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher:
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. CoRR abs/1809.07803 (2018) - 2017
- [j47]Sofie De Clercq, Kim Bauters, Steven Schockaert
Exact and heuristic methods for solving Boolean games. Auton. Agents Multi Agent Syst. 31(1): 66-106 (2017) - [j46]Pieter Libin
PhyloGeoTool: interactively exploring large phylogenies in an epidemiological context. Bioinform. 33(24): 3993-3995 (2017) - [j45]Diego S. Comas, Gustavo J. Meschino
Discovering knowledge from data clustering using automatically-defined interval type-2 fuzzy predicates. Expert Syst. Appl. 68: 136-150 (2017) - [j44]Tim Brys, Anna Harutyunyan, Peter Vrancx, Ann Nowé
Multi-objectivization and ensembles of shapings in reinforcement learning. Neurocomputing 263: 48-59 (2017) - [j43]Aleksander Byrski
Socio-cognitively inspired ant colony optimization. J. Comput. Sci. 21: 397-406 (2017) - [c139]Elias Fernández Domingos, Juan-Carlos Burguillo, Ann Nowé, Tom Lenaerts:
Coordinating Human and Agent Behavior in Collective-Risk Scenarios. AAAI 2017: 4919-4920 - [c138]Diederik M. Roijers
Interactive Thompson Sampling for Multi-objective Multi-armed Bandits. ADT 2017: 18-34 - [c137]Pieter Libin
Efficient Evaluation of Influenza Mitigation Strategies Using Preventive Bandits. AAMAS Workshops (Visionary Papers) 2017: 67-85 - [c136]Roxana Radulescu, Peter Vrancx, Ann Nowé:
Analysing Congestion Problems in Multi-agent Reinforcement Learning. AAMAS 2017: 1705-1707 - [c135]Leticia Arco
Clustering methodology for smart metering data based on local and global features. IML 2017: 65:1-65:13 - [c134]Steven Adriaensen, Filip Moons
An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design. LION 2017: 3-17 - [i8]Roxana Radulescu, Peter Vrancx, Ann Nowé:
Analysing Congestion Problems in Multi-agent Reinforcement Learning. CoRR abs/1702.08736 (2017) - [i7]Denis Steckelmacher
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets. CoRR abs/1708.06551 (2017) - [i6]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning with Options that Terminate Off-Policy. CoRR abs/1711.03817 (2017) - [i5]Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Jelena Grujic, Kristof Theys, Philippe Lemey, Ann Nowé:
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies. CoRR abs/1711.06299 (2017) - 2016
- [j42]Marilyn Bello-García, Rafael Bello, María Matilde García Lorenzo, Ann Nowé:
Personnel Selection in a Competitive Environment. Computación y Sistemas 20(2) (2016) - [j41]Dewan Md. Farid, M. Abdulla Al Mamun, Bernard Manderick, Ann Nowé:
An adaptive rule-based classifier for mining big biological data. Expert Syst. Appl. 64: 305-316 (2016) - [j40]Yann-Michaël De Hauwere, Sam Devlin
Context-sensitive reward shaping for sparse interaction multi-agent systems. Knowl. Eng. Rev. 31(1): 59-76 (2016) - [j39]Abdel Rodríguez
A reinforcement learning approach to coordinate exploration with limited communication in continuous action games. Knowl. Eng. Rev. 31(1): 77-95 (2016) - [j38]Kevin Tanghe, Anna Harutyunyan, Erwin Aertbeliën
Predicting Seat-Off and Detecting Start-of-Assistance Events for Assisting Sit-to-Stand With an Exoskeleton. IEEE Robotics Autom. Lett. 1(2): 792-799 (2016) - [j37]Sofie De Clercq, Steven Schockaert, Martine De Cock
Solving stable matching problems using answer set programming. Theory Pract. Log. Program. 16(3): 247-268 (2016) - [c133]Vitalio Alfonso Reguera
On the upper bound for the time to rendezvous in multi-hop cognitive radio networks. CAMAD 2016: 31-36 - [c132]Steven Adriaensen, Ann Nowé
Case study: An analysis of accidental complexity in a state-of-the-art hyper-heuristic for HyFlex. CEC 2016: 1485-1492 - [c131]Sofie De Clercq, Steven Schockaert, Ann Nowé, Martine De Cock
Formalizing Commitment-Based Deals in Boolean Games. ECAI 2016: 329-337 - [c130]Ewelina Swiderska, Jakub Lasisz, Aleksander Byrski
Measuring Diversity of Socio-Cognitively Inspired ACO Search. EvoApplications (1) 2016: 393-408 - [c129]Iwan Bugajski, Piotr Listkiewicz, Aleksander Byrski
Enhancing Particle Swarm Optimization with Socio-cognitive Inspirations. ICCS 2016: 804-813 - [c128]Steven Adriaensen, Ann Nowé:
Towards a White Box Approach to Automated Algorithm Design. IJCAI 2016: 554-560 - [c127]Dewan Md. Farid, Ann Nowé, Bernard Manderick:
Ensemble of Trees for Classifying High-Dimensional Imbalanced Genomic Data. IntelliSys (1) 2016: 172-187 - [c126]Isel Grau
Genomic Variant Classifier Tool. IntelliSys (1) 2016: 453-456 - [c125]Andres Auquilla, Yannick De Bock
Combining Occupancy User Profiles in a Multi-user Environment: An Academic Office Case Study. Intelligent Environments 2016: 186-189 - [c124]Mihail Mihaylov, Iván S. Razo-Zapata
Smart Grid Demonstration Platform for Renewable Energy Exchange. PAAMS 2016: 277-280 - [c123]Carlos Manuel García Algora, Ernesto Prieto Lopez, Vitalio Alfonso Reguera
Poster: Comparative study of EM-MAC and TSCH/orchestra for IoT. SCVT 2016: 1-6 - [c122]Iván S. Razo-Zapata
Analysing the Impact of Storage and Load Shifting on Grey Energy Demand Reduction. SMARTGREENS/VEHITS (Selected Papers) 2016: 27-48 - [c121]Iván S. Razo-Zapata, Mihail Mihaylov, Ann Nowé
Integration of Load Shifting and Storage to Reduce Gray Energy Demand. SMARTGREENS 2016: 154-165 - [c120]Ann Nowé, Tim Brys:
A Gentle Introduction to Reinforcement Learning. SUM 2016: 18-32 - 2015
- [j36]Kieu-Ha Phung
Schedule-based multi-channel communication in wireless sensor networks: A complete design and performance evaluation. Ad Hoc Networks 26: 88-102 (2015) - [j35]Peter Vrancx, Pasquale Gurzi, Abdel Rodríguez
A Reinforcement Learning Approach for Interdomain Routing with Link Prices. ACM Trans. Auton. Adapt. Syst. 10(1): 5:1-5:26 (2015) - [c119]Anna Harutyunyan, Sam Devlin, Peter Vrancx, Ann Nowé:
Expressing Arbitrary Reward Functions as Potential-Based Advice. AAAI 2015: 2652-2658 - [c118]Tim Brys, Anna Harutyunyan, Matthew E. Taylor, Ann Nowé:
Policy Transfer using Reward Shaping. AAMAS 2015: 181-188 - [c117]Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé:
Multi-Scale Reward Shaping via an Off-Policy Ensemble. AAMAS 2015: 1641-1642 - [c116]Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé:
Shaping Mario with Human Advice. AAMAS 2015: 1913-1914 - [c115]Mihail Mihaylov, Sergio Jurado, Narcís Avellana, Iván S. Razo-Zapata, Kristof Van Moffaert, Leticia Arco, Maite Bezunartea, Isel Grau, Adrian Cañadas, Ann Nowé:
SCANERGY: a Scalable and Modular System for Energy Trading Between Prosumers. AAMAS 2015: 1917-1918 - [c114]Steven Adriaensen, Gabriela Ochoa
A benchmark set extension and comparative study for the HyFlex framework. CEC 2015: 784-791 - [c113]Kristof Van Moffaert, Tim Brys, Ann Nowé:
Risk-sensitivity through multi-objective reinforcement learning. CEC 2015: 1746-1753 - [c112]S. Rodrigues, Rodrigo Teixeira Pinto, Pavol Bauer
Online Distributed Voltage Control of an offshore MTdc network using reinforcement learning. CEC 2015: 1769-1775 - [c111]Sofie De Clercq, Steven Schockaert, Ann Nowé, Martine De Cock:
Multilateral Negotiation in Boolean Games with Incomplete Information Using Generalized Possibilistic Logic. IJCAI 2015: 2890-2896 - [c110]Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, Ann Nowé:
Reinforcement Learning from Demonstration through Shaping. IJCAI 2015: 3352-3358 - [c109]Ivomar Brito Soares, Yann-Michaël De Hauwere, Kris Januarius, Tim Brys, Thierry Salvant, Ann Nowé
Departure MANagement with a Reinforcement Learning Approach: Respecting CFMU Slots. ITSC 2015: 1169-1176 - [c108]Kevin Van Vaerenbergh, Yann-Michaël De Hauwere, Bruno Depraetere
A Policy Gradient with Parameter-Based Exploration Approach for Zone-Heating. SSCI 2015: 556-563 - [i4]Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé:
Off-Policy Reward Shaping with Ensembles. CoRR abs/1502.03248 (2015) - [i3]Sofie De Clercq, Steven Schockaert, Martine De Cock, Ann Nowé:
Solving stable matching problems using answer set programming. CoRR abs/1512.05247 (2015) - 2014
- [j34]Mihail Mihaylov, Karl Tuyls
A decentralized approach for convention emergence in multi-agent systems. Auton. Agents Multi Agent Syst. 28(5): 749-778 (2014) - [j33]Nashat Abughalieh, Kris Steenhaut
Turbo codes for multi-hop wireless sensor networks with decode-and-forward mechanism. EURASIP J. Wirel. Commun. Netw. 2014: 204 (2014) - [j32]Kristof Van Moffaert, Ann Nowé:
Multi-objective reinforcement learning using sets of pareto dominating policies. J. Mach. Learn. Res. 15(1): 3483-3512 (2014) - [c107]Tim Brys, Ann Nowé, Daniel Kudenko, Matthew E. Taylor:
Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. AAAI 2014: 1687-1693 - [c106]Tim Brys, Ann Nowé:
Reinforcement Learning on Multiple Correlated Signals. AAAI 2014: 3065-3066 - [c105]Madalina M. Drugan
Pareto Upper Confidence Bounds algorithms: An empirical study. ADPRL 2014: 1-8 - [c104]Tim Brys, Kristof Van Moffaert, Ann Nowé, Matthew E. Taylor:
Adaptive objective selection for correlated objectives in multi-objective reinforcement learning. AAMAS 2014: 1349-1350 - [c103]Steven Adriaensen, Tim Brys, Ann Nowé:
Designing reusable metaheuristic methods: A semi-automated approach. IEEE Congress on Evolutionary Computation 2014: 2969-2976 - [c102]Tim Brys, Matthew E. Taylor
Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems. ECAI 2014: 981-982 - [c101]Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé:
Off-Policy Shaping Ensembles in Reinforcement Learning. ECAI 2014: 1021-1022 - [c100]Ildefons Magrans de Abril, Ann Nowé:
Supervised Neural Network Structure Recovery. Neural Connectomics 2014: 35-41 - [c99]Steven Adriaensen, Tim Brys, Ann Nowé:
Fair-share ILS: a simple state-of-the-art iterated local search hyperheuristic. GECCO 2014: 1303-1310 - [c98]Sofie De Clercq, Kim Bauters, Steven Schockaert, Mihail Mihaylov, Martine De Cock
Decentralized Computation of Pareto Optimal Pure Nash Equilibria of Boolean Games with Privacy Concerns . ICAART (2) 2014: 50-59 - [c97]Kristof Van Moffaert, Tim Brys, Arjun Chandra, Lukas Esterle
A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning. IJCNN 2014: 2306-2314 - [c96]Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor
Multi-objectivization of reinforcement learning problems by reward shaping. IJCNN 2014: 2315-2322 - [c95]Kristof Van Moffaert, Kevin Van Vaerenbergh, Peter Vrancx, Ann Nowé
Multi-objective χ-Armed bandits. IJCNN 2014: 2331-2338 - [c94]Madalina M. Drugan
Scalarization based Pareto optimal set of arms identification algorithms. IJCNN 2014: 2690-2697 - [c93]Sofie De Clercq, Steven Schockaert, Martine De Cock
Possibilistic Boolean Games: Strategic Reasoning under Incomplete Information. JELIA 2014: 196-209 - [c92]Sofie De Clercq, Kim Bauters, Steven Schockaert, Martine De Cock, Ann Nowé:
Using Answer Set Programming for Solving Boolean Games. KR 2014 - [c91]Mihail Mihaylov, Sergio Jurado, Kristof Van Moffaert, Narcís Avellana, Ann Nowé:
NRG-X-Change - A Novel Mechanism for Trading of Renewable Energy in Smart Grids. SMARTGREENS 2014: 101-106 - [i2]Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé:
Off-Policy Shaping Ensembles in Reinforcement Learning. CoRR abs/1405.5358 (2014) - 2013
- [j31]Cosmin Lazar, Stijn Meganck, Jonatan Taminau, David Steenhoff, Alain Coletta, Colin Molter, David Y. Weiss Solís, Robin Duque, Hugues Bersini, Ann Nowé:
Batch effect removal methods for microarray gene expression data integration: a survey. Briefings Bioinform. 14(4): 469-490 (2013) - [j30]Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, David Y. Weiss Solís, Colin Molter, Robin Duque, Hugues Bersini, Ann Nowé:
GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes. IEEE ACM Trans. Comput. Biol. Bioinform. 10(2): 383-392 (2013) - [c90]Kristof Van Moffaert, Madalina M. Drugan
Scalarized multi-objective reinforcement learning: Novel design techniques. ADPRL 2013: 191-199 - [c89]Abdel Rodríguez
Model-free learning of wire winding control. ASCC 2013: 1-6 - [c88]Tim Brys, Madalina M. Drugan
Meta-Evolutionary Algorithms and recombination operators for satisfiability solving in fuzzy logics. IEEE Congress on Evolutionary Computation 2013: 1060-1067 - [c87]Kristof Van Moffaert, Madalina M. Drugan
Hypervolume-Based Multi-Objective Reinforcement Learning. EMO 2013: 352-366 - [c86]Tim Brys, Madalina M. Drugan
Solving satisfiability in fuzzy logics by mixing CMA-ES. GECCO 2013: 1125-1132 - [c85]Tim Brys, Madalina M. Drugan
Local search and restart strategies for satisfiability solving in fuzzy logics. GEFS 2013: 52-59 - [c84]Kristof Van Moffaert, Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé:
Reinforcement Learning for Multi-purpose Schedules. ICAART (2) 2013: 203-209 - [c83]Tim Brys, Kristof Van Moffaert, Kevin Van Vaerenbergh, Ann Nowé
On the Behaviour of Scalarization Methods for the Engagement of a Wet Clutch. ICMLA (1) 2013: 258-263 - [c82]Madalina M. Drugan
Designing multi-objective multi-armed bandits algorithms: A study. IJCNN 2013: 1-8 - [c81]Yann-Michaël De Hauwere, Kristof Van Moffaert, Paul-Armand Verhaegen, Ann Nowé
Networks as a tool to save energy while keeping up general user comfort in buildings. LANMAN 2013: 1-6 - [c80]Sofie De Clercq, Steven Schockaert, Martine De Cock
Modeling Stable Matching Problems with Answer Set Programming. RuleML 2013: 68-83 - [i1]Sofie De Clercq, Steven Schockaert, Martine De Cock, Ann Nowé:
Modeling Stable Matching Problems with Answer Set Programming. CoRR abs/1302.7251 (2013) - 2012
- [j29]Jonatan Taminau, Stijn Meganck, Cosmin Lazar, David Steenhoff, Alain Coletta, Colin Molter, Robin Duque, Virginie de Schaetzen, David Y. Weiss Solís, Hugues Bersini, Ann Nowé:
Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinform. 13: 335 (2012) - [j28]Dimitri Staessens, Didier Colle, Mario Pickavet
Optimization of common pool resource sharing in multidomain IP-over-WDM networks. Comput. Commun. 35(5): 531-540 (2012) - [j27]Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls
Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks. Int. J. Commun. Networks Distributed Syst. 9(3/4): 207-224 (2012) - [j26]Nashat Abughalieh, Kris Steenhaut, Bart Lemmens, Ann Nowé:
A Mutual Algorithm for Optimizing Distributed Source Coding in Wireless Sensor Networks. Int. J. Distributed Sens. Networks 8 (2012) - [j25]Yunierkis Pérez-Castillo
GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design. J. Chem. Inf. Model. 52(9): 2366-2386 (2012) - [j24]Wajdi Halabi
Hierarchical routing and traffic grooming in IP/MPLS-based ASON/GMPLS multi-domain networks. Photonic Netw. Commun. 23(3): 217-229 (2012) - [j23]Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, Colin Molter, Virginie de Schaetzen, Robin Duque, Hugues Bersini, Ann Nowé:
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis. IEEE ACM Trans. Comput. Biol. Bioinform. 9(4): 1106-1119 (2012) - [c79]Tim Brys, Ann Nowé:
Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination. AAAI 2012: 2415-2416 - [c78]Yann-Michaël De Hauwere, Ann Nowé:
Learning Conflicts from Experience. MAPF@AAAI 2012 - [c77]Abdel Rodríguez, Peter Vrancx, Ricardo Grau Ábalo, Ann Nowé:
An RL approach to common-interest continuous action games. AAMAS 2012: 1401-1402 - [c76]Wajdi Halabi
Cross layer routing in optical IP/WDM multi-domain networks. ICUMT 2012: 607-612 - [c75]Cosmin Lazar, Luca Demarchi
Local linear spectral unmixing via cluster analysis and non-negative matrix factorization for hyperspectral (CHRIS/PROBA) imagery. IGARSS 2012: 7267-7270 - [c74]Kevin Van Vaerenbergh, Abdel Rodríguez
Improving wet clutch engagement with reinforcement learning. IJCNN 2012: 1-8 - [c73]Wolney Leal De Mello Neto, Ann Nowé:
Insights on Social Recommender Systems. RUE@RecSys 2012: 33-38 - [c72]Bart Lemmens, Kris Steenhaut, Peter Ruckebusch, Ingrid Moerman
Network-wide synchronization in Wireless Sensor Networks. SCVT 2012: 1-5 - [p5]Ann Nowé, Peter Vrancx, Yann-Michaël De Hauwere:
Game Theory and Multi-agent Reinforcement Learning. Reinforcement Learning 2012: 441-470 - 2011
- [j22]Jonatan Taminau, David Steenhoff, Alain Coletta, Stijn Meganck, Cosmin Lazar, Virginie de Schaetzen, Robin Duque, Colin Molter, Hugues Bersini, Ann Nowé, David Y. Weiss Solís:
inSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO. Bioinform. 27(22): 3204-3205 (2011) - [j21]Yunierkis Pérez-Castillo
Molecular dynamics and docking simulations as a proof of high flexibility in E. coli FabH and its relevance for accurate inhibitor modeling. J. Comput. Aided Mol. Des. 25(4): 371-393 (2011) - [j20]Adelbert Groebbens, Didier Colle, Sophie De Maesschalck, Bart Puype, Kris Steenhaut, Mario Pickavet
Logical topology design for IP rerouting: ASONs versus static OTNs. Photonic Netw. Commun. 21(2): 170-191 (2011) - [j19]Walter Colitti, Kris Steenhaut, Didier Colle, Mario Pickavet
Integrated routing in GMPLS-based IP/WDM networks. Photonic Netw. Commun. 21(3): 238-252 (2011) - [c71]Yann-Michaël De Hauwere, Peter Vrancx
Solving Sparse Delayed Coordination Problems in Multi-Agent Reinforcement Learning. ALA 2011: 114-133 - [c70]Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls, Ann Nowé:
Distributed cooperation in wireless sensor networks. AAMAS 2011: 249-256 - [c69]Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé:
Solving delayed coordination problems in MAS. AAMAS 2011: 1115-1116 - [c68]Tim Brys, Yann-Michaël De Hauwere, Ann Nowé, Peter Vrancx
Local Coordination in Online Distributed Constraint Optimization Problems. EUMAS 2011: 31-47 - [c67]Mihail Mihaylov, Yann-Aël Le Borgne, Ann Nowé, Karl Tuyls:
Self-organizing Synchronicity and Desynchronicity using Reinforcement Learning. ICAART (2) 2011: 94-103 - [c66]Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé:
Adaptive State Representations for Multi-agent Reinforcement Learning. ICAART (2) 2011: 181-189 - [c65]Peter Vrancx, Yann-Michaël De Hauwere, Ann Nowé:
Transfer Learning for Multi-agent Coordination. ICAART (2) 2011: 263-272 - [c64]Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls
Reinforcement Learning for Self-organizing Wake-Up Scheduling in Wireless Sensor Networks. ICAART (Revised Selected Papers) 2011: 382-396 - [c63]Abdel Rodríguez, Ricardo Grau Ábalo, Ann Nowé:
Continuous Action Reinforcement Learning Automata - Performance and Convergence. ICAART (2) 2011: 473-478 - [c62]Wajdi Halabi, Kris Steenhaut, Marnix Goossens, Ann Nowé:
Routing and traffic grooming in multi-domain optical networks. ICUMT 2011: 1-7 - [c61]Pasquale Gurzi, Kris Steenhaut, Ann Nowé, Peter Vrancx:
Learning a pricing strategy in multi-domain DWDM networks. LANMAN 2011: 1-6 - [c60]Yailen Martínez, Ann Nowé, Juliett Suárez, Rafael Bello
A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem. LION 2011: 253-262 - [c59]Nashat Abughalieh, Kris Steenhaut, Bart Lemmens, Ann Nowé:
Parallel Concatenation vs. Serial Concatenation Turbo Codes for Wireless Sensor Networks. SCVT 2011: 1-6 - [c58]Wajdi Halabi
Routing and signaling in GMPLS based DWDM multi domain multilayer networks using IP/WDM router. WOCN 2011: 1-5 - 2010
- [j18]Sven Van Segbroeck, Steven de Jong, Ann Nowé, Francisco C. Santos
Learning to coordinate in complex networks. Adapt. Behav. 18(5): 416-427 (2010) - [j17]Yann-Michaël De Hauwere, Peter Vrancx
Generalized learning automata for multi-agent reinforcement learning. AI Commun. 23(4): 311-324 (2010) - [j16]Walter Colitti, Kris Steenhaut, Pasquale Gurzi, Ann Nowé, Didier Colle, Bart Puype, Mario Pickavet
Service differentiation in IP/MPLS over ASON/GMPLS networks. Photonic Netw. Commun. 19(3): 301-310 (2010) - [j15]Peter Vrancx
Analyzing the dynamics of stigmergetic interactions through pheromone games. Theor. Comput. Sci. 411(21): 2116-2126 (2010) - [c57]Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé:
Learning multi-agent state space representations. AAMAS 2010: 715-722 - [c56]Peter Vrancx, Katja Verbeeck, Ann Nowé:
Taking turns in general sum Markov games. AAMAS 2010: 1439-1440 - [c55]Jonatan Taminau, Stijn Meganck, Cosmin Lazar, David Y. Weiss Solís, Alain Coletta, Nic Walker, Hugues Bersini, Ann Nowé:
Sequential Application of Feature Selection and Extraction for Predicting Breast Cancer Aggressiveness. CSBio 2010: 46-57 - [c54]Cosmin Lazar, Danielle Nuzillard, Ann Nowé:
A New Geometrical BSS Approach for Non Negative Sources. LVA/ICA 2010: 530-537 - [c53]Nashat Abughalieh, Yann-Aël Le Borgne, Ann Nowé, Kris Steenhaut:
Lifetime optimization for sensor networks with correlated data gathering. INSS 2010: 69-72 - [c52]Yann-Aël Le Borgne, Ann Nowé, Nashat Abughalieh, Kris Steenhaut:
Distributed regression for high-level feature extraction in wireless sensor networks. INSS 2010: 249-252 - [c51]Yann-Aël Le Borgne, Ann Nowé, Kris Steenhaut, Gianluca Bontempi
Demonstrating principal component aggregation for distributed spatial pattern recognition. IPSN 2010: 430-431 - [c50]Nashat Abughalieh, Yann-Aël Le Borgne, Kris Steenhaut, Ann Nowé:
Lifetime optimization for wireless sensor networks with correlated data gathering. WiOpt 2010: 266-272 - [p4]Yann-Michaël De Hauwere, Peter Vrancx
Multi-Agent Systems and Large State Spaces. Agent and Multi-agent Technology for Internet and Enterprise Systems 2010: 181-205
2000 – 2009
- 2009
- [j14]Jonatan Taminau, Stijn Meganck, David Y. Weiss Solís, Wilma van Staveren, Geneviève Dom, David Venet, Hugues Bersini, Vincent Detours, Ann Nowé:
Validation of Merging Techniques for Cancer Microarray Data Sets. Aust. J. Intell. Inf. Process. Syst. 10(4) (2009) - [j13]Sven Van Segbroeck, Ann Nowé, Tom Lenaerts
Stochastic Simulation of the Chemoton. Artif. Life 15(2): 213-226 (2009) - [c49]Mihail Mihaylov, Karl Tuyls
Decentralized Learning in Wireless Sensor Networks. ALA 2009: 60-73 - [c48]Sven Van Segbroeck, Francisco C. Santos
The coevolution of loyalty and cooperation. IEEE Congress on Evolutionary Computation 2009: 500-505 - [c47]Joris Borms, Kris Steenhaut, Bart Lemmens, Ann Nowé:
Power Aware Fulfilment of Latency Requirements by Exploiting Heterogeneity in Wireless Sensor and Actuator Networks. DSD 2009: 597-600 - [c46]Pasquale Gurzi, Walter Colitti, Kris Steenhaut, Ann Nowé:
Maximum flow based Routing and Wavelength Assignment in all-optical networks. ICUMT 2009: 1-6 - [c45]Yudel Gómez, Rafael Bello
Multi-colony ACO and Rough Set Theory to Distributed Feature Selection Problem. IWANN (2) 2009: 458-461 - [p3]Martin Köhn, Walter Colitti, Pasquale Gurzi, Ann Nowé, Kris Steenhaut:
Multi-layer Traffic Engineering (MTE) in Grooming Enabled ASON/GMPLS Networks. COST Action 291 Final Report 2009: 237-252 - [p2]Sébastien Rumley, Christian Gaumier, Ramon Aparicio-Pardo, Ching-Hung Chang, Walter Colitti, Belen Garcia-Manrubia
Software Tools and Methods for Research and Education in Optical Networks. COST Action 291 Final Report 2009: 331-364 - 2008
- [j12]Dimitri Staessens, Didier Colle, Ilse Lievens, Mario Pickavet
Enabling high availability over multiple optical networks. IEEE Commun. Mag. 46(6): 120-126 (2008) - [j11]Yudel Gómez, Rafael Bello, Amilkar Puris, María M. García, Ann Nowé:
Two Step Swarm Intelligence to Solve the Feature Selection Problem. J. Univers. Comput. Sci. 14(15): 2582-2596 (2008) - [j10]Peter Vrancx
Decentralized Learning in Markov Games. IEEE Trans. Syst. Man Cybern. Part B 38(4): 976-981 (2008) - [c44]Hugues Smeets, Kris Steenhaut, Ann Nowé:
An Efficient Distributed Self-Organizing Routing Algorithm for Wireless Sensor Networks. CISIS 2008: 19-25 - [c43]Walter Colitti, Kris Steenhaut, Ann Nowé:
Multilayer traffic engineering and DiffServ in the next generation internet. COMSWARE 2008: 591-598 - [c42]Walter Colitti, Pasquale Gurzi, Kris Steenhaut, Ann Nowé:
Adaptive multilayer routing in the next generation GMPLS Internet. COMSWARE 2008: 768-775 - [c41]Yudel Gómez, Rafael Bello
Speeding-Up ACO Implementation by Decreasing the Number of Heuristic Function Evaluations in Feature Selection Problem. IWPACBB 2008: 223-232 - [c40]Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé:
Using Generalized Learning Automata for State Space Aggregation in MAS. KES (1) 2008: 182-193 - [c39]Maarten Peeters, Ville Könönen, Katja Verbeeck, Ann Nowé:
A Learning Automata Approach to Multi-agent Policy Gradient Learning. KES (2) 2008: 379-390 - [c38]Maarten Peeters, Ville Könönen, Katja Verbeeck, Sven Van Segbroeck, Ann Nowé:
Coordinated Exploration in Conflicting Multi-stage Games. KES (2) 2008: 391-402 - [c37]Isis Bonet
Comparing Distance Measures with Visual Methods. MICAI 2008: 90-99 - [p1]Karl Tuyls
Introduction to Game Theory. Wiley Encyclopedia of Computer Science and Engineering 2008 - [e4]Karl Tuyls
Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems, Revised Selected Papers. Lecture Notes in Computer Science 4865, Springer 2008, ISBN 978-3-540-77947-6 [contents] - 2007
- [j9]Katja Verbeeck, Ann Nowé, Johan Parent, Karl Tuyls
Exploring selfish reinforcement learning in repeated games with stochastic rewards. Auton. Agents Multi Agent Syst. 14(3): 239-269 (2007) - [c36]Nyree Lemmens, Steven de Jong, Karl Tuyls
Bee Behaviour in Multi-agent Systems. Adaptive Agents and Multi-Agents Systems 2007: 145-156 - [c35]Maarten Peeters, Katja Verbeeck, Ann Nowé:
Solving Multi-stage Games with Hierarchical Learning Automata That Bootstrap. Adaptive Agents and Multi-Agents Systems 2007: 169-187 - [c34]Peter Vrancx, Katja Verbeeck, Ann Nowé:
Networks of Learning Automata and Limiting Games. Adaptive Agents and Multi-Agents Systems 2007: 224-238 - [c33]Amilkar Puris, Rafael Bello, Yaima Trujillo, Ann Nowé, Yailen Martínez:
Two-Stage ACO to Solve the Job Shop Scheduling Problem. CIARP 2007: 447-456 - [c32]Rafael Bello, Yudel Gómez, María M. García, Ann Nowé:
Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem. ISDA 2007: 691-696 - [c31]Amilkar Puris, Rafael Bello, Yailen Martínez, Ann Nowé:
Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem. IWINAC (2) 2007: 307-316 - [c30]Peter Vrancx, Katja Verbeeck, Ann Nowé:
Optimal Convergence in Multi-Agent MDPs. KES (3) 2007: 107-114 - [c29]Walter Colitti, Kris Steenhaut, Ann Nowé:
QoS in GMPLS based IP/DWDM Metro Networks. LANMAN 2007: 84-89 - [c28]Walter Colitti, Kris Steenhaut, Ann Nowé, Jan Lemeire:
Multilayer Quality and Grade of Service Support for High Speed GMPLS IP/DWDM Networks. NBiS 2007: 187-196 - [e3]Karl Tuyls
Knowledge Discovery and Emergent Complexity in Bioinformatics, First International Workshop, KDECB 2006, Ghent, Belgium, May 10, 2006. Revised Selected Papers. Lecture Notes in Computer Science 4366, Springer 2007, ISBN 978-3-540-71036-3 [contents] - 2006
- [c27]Peter Vrancx, Ann Nowé:
Using Pheromone Repulsion to Find Disjoint Paths. ANTS Workshop 2006: 522-523 - [c26]Rafael Bello, Amilkar Puris, Ann Nowé, Yailen Martínez, María M. García:
Two Step Ant Colony System to Solve the Feature Selection Problem. CIARP 2006: 588-596 - [c25]Ann Nowé, Katja Verbeeck, Maarten Peeters:
Learning Automata as a Basis for Multi Agent Reinforcement Learning. EUMAS 2006 - [c24]Ronald L. Westra, Karl Tuyls
Knowledge Discovery and Emergent Complexity in Bioinformatics. KDECB 2006: 1-9 - [c23]Peter Vrancx, Katja Verbeeck, Ann Nowé:
Analyzing Stigmergetic Algorithms Through Automata Games. KDECB 2006: 145-156 - [c22]Yaile Caballero
A New Measure Based in the Rough Set Theory to Estimate the Training Set Quality. SYNASC 2006: 133-140 - 2005
- [j8]Karl Tuyls
Evolutionary game theory and multi-agent reinforcement learning. Knowl. Eng. Rev. 20(1): 63-90 (2005) - [c21]Katja Verbeeck, Ann Nowé, Maarten Peeters, Karl Tuyls
Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games. Adaptive Agents and Multi-Agent Systems 2005: 275-294 - [c20]Katja Verbeeck, Ann Nowé, Karl Tuyls
Coordinated exploration in multi-agent reinforcement learning: an application to load-balancing. AAMAS 2005: 1105-1106 - [c19]Johan Parent, Ann Nowé, Anne Defaweux, Kris Steenhaut:
Compressed Linear Genetic Programming: empirical parameter study on the Even-n-parity problem. BNAIC 2005: 373-374 - [c18]Johan Parent, Ann Nowé, Kris Steenhaut, Anne Defaweux:
Linear genetic programming using a compressed genotype representation. Congress on Evolutionary Computation 2005: 1164-1171 - [c17]Peter Vrancx, Ann Nowé, Kris Steenhaut:
Multi-type ACO for light path protection. EUMAS 2005: 513 - [c16]Pieter Beyens, Ann Nowé, Kris Steenhaut:
High-density wireless sensor networks: a new clustering approach for prediction-based monitoring. EWSN 2005: 188-196 - [c15]Rafael Bello
A model based on ant colony system and rough set theory to feature selection. GECCO 2005: 275-276 - [c14]Ann Nowé, Katja Verbeeck, Maarten Peeters:
Learning Automata as a Basis for Multi Agent Reinforcement Learning. LAMAS 2005: 71-85 - [c13]Peter Vrancx
Multi-type ACO for Light Path Protection. LAMAS 2005: 207-215 - [e2]Katja Verbeeck, Karl Tuyls, Ann Nowé, Bernard Manderick, Bart Kuijpers:
BNAIC 2005 - Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence, Brussels, Belgium, October 17-18, 2005. Koninklijke Vlaamse Academie van Belie voor Wetenschappen en Kunsten 2005 [contents] - [e1]Marie-Pierre Gleizes, Gal A. Kaminka, Ann Nowé, Sascha Ossowski, Karl Tuyls, Katja Verbeeck:
EUMAS 2005 - Proceedings of the Third European Workshop on Multi-Agent Systems, Brussels, Belgium, December 7-8, 2005. Koninklijke Vlaamse Academie van Belie voor Wetenschappen en Kunsten 2005 [contents] - 2004
- [j7]Johan Parent, Katja Verbeeck, Jan Lemeire, Ann Nowé, Kris Steenhaut, Erik F. Dirkx:
Adaptive load balancing of parallel applications with multi-agent reinforcement learning on heterogeneous systems. Sci. Program. 12(2): 71-79 (2004) - [j6]Karl Tuyls
An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems. Synth. 139(2): 297-330 (2004) - [c12]Maarten Peeters, Katja Verbeeck, Ann Nowé:
Multi-Agent Learning in Conflicting Multi-Level Games with Incomplete Information. AAAI Technical Report (2) 2004: 73-80 - [c11]Ann Nowé, Katja Verbeeck, Peter Vrancx:
Multi-type Ant Colony: The Edge Disjoint Paths Problem. ANTS Workshop 2004: 202-213 - 2003
- [j5]Adelbert Groebbens, Didier Colle, Sophie De Maesschalck, Ilse Lievens, Mario Pickavet, Piet Demeester, Lan Tran, Kris Steenhaut, Ann Nowé:
Efficient Protection in MPλS Networks Using Backup Trees: Part One - Concepts and Heuristics. Photonic Netw. Commun. 6(3): 191-206 (2003) - [j4]Adelbert Groebbens, Didier Colle, Sophie De Maesschalck, Ilse Lievens, Mario Pickavet, Piet Demeester, Lan Tran, Kris Steenhaut, Ann Nowé:
Efficient Protection in MPλS Networks Using Backup Trees: Part Two - Simulations. Photonic Netw. Commun. 6(3): 207-222 (2003) - [c10]Karl Tuyls
Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems. ECML 2003: 421-431 - [c9]Lan Tran, Kris Steenhaut, Ann Nowé, Mario Pickavet, Piet Demeester:
Efficient Usage of Capacity Resources in Survivable MP lambda S Networks. QoS-IP 2003: 204-217 - 2002
- [j3]Katja Verbeeck, Ann Nowé:
Colonies of learning automata. IEEE Trans. Syst. Man Cybern. Part B 32(6): 772-780 (2002) - [c8]Katja Verbeeck, Ann Nowé, Tom Lenaerts
Learning to Reach the Pareto Optimal Nash Equilibrium as a Team. Australian Joint Conference on Artificial Intelligence 2002: 407-418 - [c7]Johan Parent, Ann Nowé:
Evolving Compression Preprocessors With Genetic Programming. GECCO 2002: 861-867 - [c6]Katja Verbeeck, Johan Parent, Ann Nowé:
Homo Egualis Reinforcement Learning Agents for Load Balancing. WRAC 2002: 81-91 - 2001
- [c5]Ann Nowé, Johan Parent, Katja Verbeeck:
Social Agents Playing a Periodical Policy. ECML 2001: 382-393
1990 – 1999
- 1999
- [c4]Ann Nowé, Katja Verbeeck:
Formalizing the Ant Algorithms in Terms of Reinforcement Learning. ECAL 1999: 616-620 - 1998
- [j2]Ann Nowé:
Sugeno, Mamdani, and fuzzy Mamdani controllers put in a uniform interpolation framework. Int. J. Intell. Syst. 13(2-3): 243-256 (1998) - [c3]Ann Nowé, Kris Steenhaut, Mohamed Fakir, Katja Verbeeck:
Q-learning for adaptive load based routing. SMC 1998: 3965-3970 - 1993
- [c2]Ann Nowé, Ranjan Vepa:
A Reinforcement Learning Algorithm based on "Safety". FLAI 1993: 47-58 - 1992
- [j1]Ann Nowé:
A self-tuning robust fuzzy controller. Microprocess. Microprogramming 35(1-5): 719-726 (1992)
1980 – 1989
- 1989
- [c1]Viviane Jonckers, Ann Nowé:
An environment for knowledge based transformational implementation. IEA/AIE (2) 1989: 610-619
Coauthor Index
Rafael Bello 0001
aka: Rafael Bello Pérez
aka: Rafael Bello Pérez
Pieter Libin
aka: Pieter J. K. Libin
aka: Pieter J. K. Libin
María Matilde García Lorenzo
aka: María M. García
aka: María M. García
[j78] [j77] [c203] [c201] [i53] [i49] [i48] [j72] [j67] [c200] [c199] [c198] [c197] [c193] [i45] [i44] [i41] [i37] [j66] [j65] [j63] [c188] [i35] [i32] [i28] [i27] [i25] [j58] [j57] [j56] [c183] [c182] [c181] [c174] [i24] [i23] [i20] [c168] [c165] [c160] [c156] [c154] [c152] [i19] [i18] [i16] [i15] [i13] [c149] [c146] [c145] [c144] [c141] [i11] [i10] [i9] [c138] [c137] [i7] [i5]
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