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Diederik M. Roijers
Diederik Marijn Roijers
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- affiliation: Vrije Universiteit Brussel, Belgium
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2020 – today
- 2024
- [j20]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) - [c60]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 - [c59]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [c58]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 - [c57]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. RLC 2024: 108-129 - [i41]Peter Vamplew, Cameron Foale
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - [i40]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) - [i39]Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers, Charalampos Andriotis, Yingqian Zhang, Zaharah Bukhsh:
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization. CoRR abs/2406.06184 (2024) - [i38]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) - [i37]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. CoRR abs/2407.18812 (2024) - 2023
- [j19]Mathieu Reymond
Actor-critic multi-objective reinforcement learning for non-linear utility functions. Auton. Agents Multi Agent Syst. 37(2): 23 (2023) - [j18]Conor F. Hayes
Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning. Auton. Agents Multi Agent Syst. 37(2): 26 (2023) - [j17]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) - [c56]Willem Röpke, Carla Groenland, Roxana Radulescu, Ann Nowé, Diederik M. Roijers:
Bridging the Gap Between Single and Multi Objective Games. AAMAS 2023: 224-232 - [c55]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
A Study of Nash Equilibria in Multi-Objective Normal-Form Games. AAMAS 2023: 269-271 - [c54]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Friedrik Hentz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar Reward is Not Enough. AAMAS 2023: 839-841 - [c53]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 - [c52]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 - [c51]Willem Röpke
Distributional Multi-Objective Decision Making. IJCAI 2023: 5711-5719 - [c50]Zuzanna Osika, Jazmin Zatarain Salazar
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization. IJCAI 2023: 6741-6749 - [i36]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) - [i35]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) - [i34]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) - [i33]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. CoRR abs/2305.05560 (2023) - [i32]Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers, Frans A. Oliehoek, Pradeep K. Murukannaiah:
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization. CoRR abs/2311.11288 (2023) - 2022
- [j16]Conor F. Hayes
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j15]Peter Vamplew
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Auton. Agents Multi Agent Syst. 36(2): 41 (2022) - [j14]Willem Röpke
On nash equilibria in normal-form games with vectorial payoffs. Auton. Agents Multi Agent Syst. 36(2): 53 (2022) - [j13]Roxana Radulescu
Opponent learning awareness and modelling in multi-objective normal form games. Neural Comput. Appl. 34(3): 1759-1781 (2022) - [j12]Gongjin Lan
Time efficiency in optimization with a bayesian-Evolutionary algorithm. Swarm Evol. Comput. 69: 100970 (2022) - [c49]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2022: 1524-1526 - [c48]Conor F. Hayes, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Decision-Theoretic Planning for the Expected Scalarised Returns. AAMAS 2022: 1621-1623 - [c47]Shang Wang, Mathieu Reymond, Athirai A. Irissappane, Diederik M. Roijers:
Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning. AAMAS 2022: 1756-1758 - [i31]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) - [i30]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models. CoRR abs/2207.00368 (2022) - [i29]Cláudia Fonseca Pinhão, Chris Eijgenstein, Iva Gornishka
Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds. CoRR abs/2211.04108 (2022) - [i28]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning. CoRR abs/2211.13032 (2022) - 2021
- [j11]Gongjin Lan
Learning directed locomotion in modular robots with evolvable morphologies. Appl. Soft Comput. 111: 107688 (2021) - [j10]Jie Jiang, Qiuqiang Kong, Mark D. Plumbley, Nigel Gilbert
Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances. ACM Trans. Knowl. Discov. Data 15(3): 50:1-50:21 (2021) - [c46]Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers:
Cooperative Prioritized Sweeping. AAMAS 2021: 160-168 - [c45]Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi, Diederik M. Roijers, Pieter J. K. Libin, Jan Helsen:
Scalable Optimization for Wind Farm Control using Coordination Graphs. AAMAS 2021: 1362-1370 - [c44]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. AAMAS 2021: 1530-1532 - [i27]Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi, Diederik M. Roijers, Pieter J. K. Libin, Jan Helsen:
Scalable Optimization for Wind Farm Control using Coordination Graphs. CoRR abs/2101.07844 (2021) - [i26]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search. CoRR abs/2102.00966 (2021) - [i25]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) - [i24]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making. CoRR abs/2106.01048 (2021) - [i23]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Preference Communication in Multi-Objective Normal-Form Games. CoRR abs/2111.09191 (2021) - [i22]Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
On Nash Equilibria in Normal-Form Games With Vectorial Payoffs. CoRR abs/2112.06500 (2021) - [i21]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2112.12458 (2021) - [i20]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). CoRR abs/2112.15422 (2021) - 2020
- [j9]Roxana Radulescu
Multi-objective multi-agent decision making: a utility-based analysis and survey. Auton. Agents Multi Agent Syst. 34(1): 10 (2020) - [j8]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) - [j7]Roxana Radulescu
A utility-based analysis of equilibria in multi-objective normal-form games. Knowl. Eng. Rev. 35: e32 (2020) - [j6]Shiyu Zhang, Sander Bakkes
Avatars of a Feather Flock Together: Gender Homophily in Online Video Games Revealed via Exponential Random Graph Modeling. IEEE Trans. Games 12(1): 86-100 (2020) - [j5]Felipe Leno da Silva
Coordination of Electric Vehicle Charging Through Multiagent Reinforcement Learning. IEEE Trans. Smart Grid 11(3): 2347-2356 (2020) - [c43]Xiaodong Nian, Athirai Aravazhi Irissappane, Diederik M. Roijers:
DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments. AAMAS 2020: 931-938 - [c42]Timothy Verstraeten, Eugenio Bargiacchi, Pieter J. K. Libin, Diederik M. Roijers, Ann Nowé:
Thompson Sampling for Factored Multi-Agent Bandits. AAMAS 2020: 2029-2031 - [c41]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 - [c40]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 - [c39]Wan-Jui Lee, Helia Jamshidi, Diederik M. Roijers
Deep Reinforcement Learning for Solving Train Unit Shunting Problem with Interval Timing. EDCC Workshops 2020: 99-110 - [c38]Diederik M. Roijers
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. ECML/PKDD (3) 2020: 463-478 - [i19]Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé:
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping. CoRR abs/2001.07527 (2020) - [i18]Gongjin Lan
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies. CoRR abs/2001.07804 (2020) - [i17]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) - [i16]Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben:
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm. CoRR abs/2005.04166 (2020) - [i15]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
- [c37]Mark de Blaauw, Diederik M. Roijers, Vesa Muhonen:
A Scalable Logo Recognition Model with Deep Meta-Learning. BNAIC/BENELEARN 2019 - [c36]Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Wenjia Wang, Kristof Theys, Ann Nowé:
Thompson Sampling for m-top Exploration. BNAIC/BENELEARN 2019 - [c35]Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers, Ann Nowé:
Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors. BNAIC/BENELEARN 2019 - [c34]Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, Ann Nowé:
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. BNAIC/BENELEARN 2019 - [c33]Axel Abels
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. ICML 2019: 11-20 - [c32]Pieter Libin
Bayesian Anytime m-top Exploration. ICTAI 2019: 1422-1428 - [c31]Denis Steckelmacher
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. ECML/PKDD (3) 2019: 19-34 - [i14]Hélène Plisnier
The Actor-Advisor: Policy Gradient With Off-Policy Advice. CoRR abs/1902.02556 (2019) - [i13]Denis Steckelmacher
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics. CoRR abs/1903.04193 (2019) - [i12]Hélène Plisnier
Transfer Learning Across Simulated Robots With Different Sensors. CoRR abs/1907.07958 (2019) - [i11]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) - [i10]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) - 2018
- [j4]Stéphane Doncieux, David Filliat, Natalia Díaz Rodríguez
Open-Ended Learning: A Conceptual Framework Based on Representational Redescription. Frontiers Neurorobotics 12: 59 (2018) - [c30]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 - [c29]Diederik M. Roijers, Erwin Walraven, Matthijs T. J. Spaan:
Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs. ICAPS 2018: 218-226 - [c28]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 - [c27]Roxana Radulescu
Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population. BNCAI 2018: 90-105 - [c26]Gongjin Lan
Real-Time Robot Vision on Low-Performance Computing Hardware. ICARCV 2018: 1959-1965 - [c25]Jie Jiang, Mark Hoogendoorn, Qiuqiang Kong, Diederik M. Roijers
Predicting Appliance Usage Status In Home Like Environments. DSP 2018: 1-5 - [c24]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 - [c23]Milan Jelisavcic
Analysing the Relative Importance of Robot Brains and Bodies. ALIFE 2018: 327-334 - [c22]Pieter J. K. Libin, Timothy Verstraeten, Diederik M. Roijers
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies. ECML/PKDD (3) 2018: 456-471 - [c21]Gongjin Lan
Directed Locomotion for Modular Robots with Evolvable Morphologies. PPSN (1) 2018: 476-487 - [i9]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) - [i8]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) - [i7]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
- [b1]Diederik M. Roijers
Multi-Objective Decision Making. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2017, ISBN 978-3-031-00448-3 - [c20]Diederik M. Roijers
Interactive Thompson Sampling for Multi-objective Multi-armed Bandits. ADT 2017: 18-34 - [c19]Ayumi Igarashi, Diederik M. Roijers
Multi-criteria Coalition Formation Games. ADT 2017: 197-213 - [c18]Pieter Libin
Efficient Evaluation of Influenza Mitigation Strategies Using Preventive Bandits. AAMAS Workshops (Visionary Papers) 2017: 67-85 - [i6]Denis Steckelmacher
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets. CoRR abs/1708.06551 (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
- [j3]Diederik M. Roijers:
Multi-objective decision-theoretic planning: abstract. AI Matters 2(4): 11-12 (2016) - [c17]Joris Scharpff, Diederik M. Roijers, Frans A. Oliehoek, Matthijs T. J. Spaan, Mathijs Michiel de Weerdt:
Solving Transition-Independent Multi-Agent MDPs with Sparse Interactions. AAAI 2016: 3174-3180 - [c16]Maarten de Waard, Diederik M. Roijers
Monte Carlo Tree Search with options for general video game playing. CIG 2016: 1-8 - [c15]Auke J. Wiggers, Frans A. Oliehoek
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information. ECAI 2016: 1628-1629 - [c14]Joost van Doorn, Daan Odijk, Diederik M. Roijers, Maarten de Rijke
Balancing Relevance Criteria through Multi-Objective Optimization. SIGIR 2016: 769-772 - [i4]Auke J. Wiggers, Frans A. Oliehoek, Diederik M. Roijers:
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information. CoRR abs/1606.06888 (2016) - [i3]Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers, Shimon Whiteson:
Multi-Objective Deep Reinforcement Learning. CoRR abs/1610.02707 (2016) - 2015
- [j2]Diederik Marijn Roijers
Computing Convex Coverage Sets for Faster Multi-objective Coordination. J. Artif. Intell. Res. 52: 399-443 (2015) - [c13]Chiel Kooijman, Maarten de Waard, Maarten Inja, Diederik M. Roijers, Shimon Whiteson:
Pareto Local Search for MOMDP Planning. ESANN 2015 - [c12]Mircea Traichioiu, Sander Bakkes, Diederik M. Roijers:
Grammar-based Procedural Content Generation from Designer-provided Difficulty Curves. FDG 2015 - [c11]Diederik Marijn Roijers, Shimon Whiteson, Frans A. Oliehoek:
Point-Based Planning for Multi-Objective POMDPs. IJCAI 2015: 1666-1672 - [c10]Diederik Marijn Roijers:
Efficient Methods for Multi-Objective Decision-Theoretic Planning. IJCAI 2015: 4389-4390 - [i2]Joris Scharpff, Diederik M. Roijers, Frans A. Oliehoek, Matthijs T. J. Spaan, Mathijs de Weerdt:
Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version). CoRR abs/1511.09047 (2015) - 2014
- [c9]Paris Mavromoustakos Blom, Sander Bakkes, Chek Tien Tan, Shimon Whiteson, Diederik M. Roijers, Roberto Valenti, Theo Gevers:
Towards Personalised Gaming via Facial Expression Recognition. AIIDE 2014 - [c8]Diederik Marijn Roijers, Joris Scharpff, Matthijs T. J. Spaan, Frans A. Oliehoek, Mathijs de Weerdt, Shimon Whiteson:
Bounded Approximations for Linear Multi-Objective Planning Under Uncertainty. ICAPS 2014 - [c7]Diederik M. Roijers, Shimon Whiteson, Frans A. Oliehoek:
Linear support for multi-objective coordination graphs. AAMAS 2014: 1297-1304 - [c6]Diederik M. Roijers:
Convex coverage set methods for multi-objective collaborative decision making. AAMAS 2014: 1727-1728 - [c5]Alex Rietveld, Sander Bakkes, Diederik M. Roijers:
Circuit-adaptive challenge balancing in racing games. GEM 2014: 1-8 - [c4]Maarten Inja, Chiel Kooijman, Maarten de Waard, Diederik M. Roijers
Queued Pareto Local Search for Multi-Objective Optimization. PPSN 2014: 589-599 - [i1]Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley:
A Survey of Multi-Objective Sequential Decision-Making. CoRR abs/1402.0590 (2014) - 2013
- [j1]Diederik M. Roijers
A Survey of Multi-Objective Sequential Decision-Making. J. Artif. Intell. Res. 48: 67-113 (2013) - [c3]Diederik M. Roijers
Computing Convex Coverage Sets for Multi-objective Coordination Graphs. ADT 2013: 309-323 - [c2]Diederik M. Roijers, Shimon Whiteson, Frans A. Oliehoek:
Multi-objective variable elimination for collaborative graphical games. AAMAS 2013: 1209-1210 - 2012
- [c1]Diederik M. Roijers, Johan Jeuring, Ad Feelders:
Probability estimation and a competence model for rule based e-tutoring systems. LAK 2012: 255-258
Coauthor Index
Pieter Libin
aka: Pieter J. K. Libin
aka: Pieter J. K. Libin
[j20] [c60] [c58] [c57] [i40] [i38] [i37] [j19] [j17] [c56] [c55] [c53] [c52] [c51] [i36] [i35] [i34] [i33] [j16] [j14] [j13] [c49] [i31] [i25] [i23] [i22] [i21] [j9] [j8] [j7] [c42] [c41] [c40] [c38] [i19] [i17] [i15] [c36] [c35] [c34] [c33] [c32] [c31] [i14] [i13] [i12] [i11] [i10] [c30] [c28] [c27] [c24] [c22] [i9] [i8] [i7] [c20] [c18] [i6] [i5]
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last updated on 2024-11-14 00:51 CET by the dblp team
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