Photo of Dr Frans Oliehoek

Dr Frans Oliehoek PhD

Senior Lecturer Computer Science

Publications

2017

Decentralised Online Planning for Multi-Robot Warehouse Commissioning. (Conference Paper)

Claes, D., Oliehoek, F. A., Baier, H., & Tuyls, K. (2017). Decentralised Online Planning for Multi-Robot Warehouse Commissioning.. In K. Larson, M. Winikoff, S. Das, & E. H. Durfee (Eds.), AAMAS (pp. 492-500). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=3091125

Exploiting submodular value functions for scaling up active perception (Journal article)

Satsangi, Y., Whiteson, S., Oliehoek, F. A., & Spaan, M. T. J. (2018). Exploiting submodular value functions for scaling up active perception. AUTONOMOUS ROBOTS, 42(2), 209-233. doi:10.1007/s10514-017-9666-5

DOI: 10.1007/s10514-017-9666-5

GANGs: Generative Adversarial Network Games (Journal article)

Oliehoek, F. A., Savani, R., Gallego-Posada, J., Pol, E. V. D., Jong, E. D. D., & Gross, R. (n.d.). GANGs: Generative Adversarial Network Games. Retrieved from http://arxiv.org/abs/1712.00679v2

Learning in POMDPs with Monte Carlo Tree Search. (Conference Paper)

Katt, S., Oliehoek, F. A., & Amato, C. (2017). Learning in POMDPs with Monte Carlo Tree Search.. In D. Precup, & Y. W. Teh (Eds.), ICML Vol. 70 (pp. 1819-1827). PMLR. Retrieved from http://jmlr.org/proceedings/papers/v70/

LiftUpp: Support to develop learner performance (Conference Paper)

Oliehoek, F. A., Savani, R., Adderton, E., Cui, X., Jackson, D., Jimmieson, P., . . . Dawson, L. (n.d.). LiftUpp: Support to develop learner performance. Retrieved from http://arxiv.org/abs/1704.06549v1

Maximizing the probability of arriving on time: A practical q-learning method (Conference Paper)

Cao, Z., Guo, H., Zhang, J., Oliehoek, F., & Fastenrath, U. (2017). Maximizing the probability of arriving on time: A practical q-learning method. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 4481-4487).

Real-time resource allocation for tracking systems (Conference Paper)

Satsangi, Y., Whiteson, S., Oliehoek, F. A., & Bouma, H. (2017). Real-time resource allocation for tracking systems. In Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017.

The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems (Conference Paper)

Oliehoek, F. A., Spaan, M. T. J., Terwijn, B., Robbel, P., & Messias, J. V. (2017). The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems. In JOURNAL OF MACHINE LEARNING RESEARCH Vol. 18. Retrieved from http://gateway.webofknowledge.com/

The MADP toolbox: An open source library for planning and learning in (multi-)agent systems (Journal article)

Oliehoek, F. A., Spaan, M. T. J., Terwijn, B., Robbel, P., & Messias, J. V. (2017). The MADP toolbox: An open source library for planning and learning in (multi-)agent systems. Journal of Machine Learning Research, 18, 1-5.

2016

A Concise Introduction to Decentralized POMDPs (Book)

Oliehoek, F. A., & Amato, C. (2016). A Concise Introduction to Decentralized POMDPs. Springer.

A scalable framework to choose sellers in E-marketplaces using POMDPs (Conference Paper)

Irissappane, A. A., Oliehoek, F. A., & Zhang, J. (2016). A scalable framework to choose sellers in E-marketplaces using POMDPs. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 158-164).

Energy- and cost-efficient pumping station control (Conference Paper)

Kanters, T. V., Oliehoek, F. A., Kaisers, M., Van Den Bosch, S. R., Grispen, J., & Hermans, J. (2016). Energy- and cost-efficient pumping station control. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 3842-3848).

Exploiting anonymity in approximate linear programming: Scaling to large multiagent MDPs (Conference Paper)

Robbel, P., Oliehoek, F. A., & Kochenderfer, M. J. (2016). Exploiting anonymity in approximate linear programming: Scaling to large multiagent MDPs. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2537-2543).

PAC greedy maximization with efficient bounds on information gain for sensor selection (Conference Paper)

Satsangi, Y., Whiteson, S., & Oliehoek, F. A. (2016). PAC greedy maximization with efficient bounds on information gain for sensor selection. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2016-January (pp. 3220-3227).

Probably Approximately Correct Greedy Maximization (Journal article)

Satsangi, Y., Whiteson, S., & Oliehoek, F. A. (n.d.). Probably Approximately Correct Greedy Maximization. Retrieved from http://arxiv.org/abs/1602.07860v1

Probably Approximately Correct Greedy Maximization (Conference Paper)

Satsangi, Y., Whiteson, S., & Oliehoek, F. A. (2016). Probably Approximately Correct Greedy Maximization. In Proceedings of the Fifteenth International Conference on Autonomous Agents and Multiagent Systems.

Probably Approximately Correct Greedy Maximization: (Extended Abstract). (Conference Paper)

Satsangi, Y., Whiteson, S., & Oliehoek, F. A. (2016). Probably Approximately Correct Greedy Maximization: (Extended Abstract).. In C. M. Jonker, S. Marsella, J. Thangarajah, & K. Tuyls (Eds.), AAMAS (pp. 1387-1388). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2936924

Reports of the AAAI 2016 Spring Symposium Series (Journal article)

Amato, C., Amir, O., Bryson, J., Grosz, B., Indurkhya, B., Kiciman, E., . . . Takadama, K. (2016). Reports of the AAAI 2016 Spring Symposium Series. AI MAGAZINE, 37(4), 83-88. Retrieved from http://gateway.webofknowledge.com/

Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version) (Journal article)

Scharpff, J., Roijers, D. M., Oliehoek, F. A., Spaan, M. T. J., & Weerdt, M. M. D. (n.d.). Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version). Retrieved from http://arxiv.org/abs/1511.09047v2

Solving transition-independent multi-agent mdps with sparse interactions (Conference Paper)

Scharpff, J., Roijers, D. M., Oliehoek, F. A., Spaan, M. T. J., & Deweerdt, M. M. (2016). Solving transition-independent multi-agent mdps with sparse interactions. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 3174-3180).

Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information (Conference Paper)

Wiggers, A. J., Oliehoek, F. A., & Roijers, D. M. (2016). Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information. In ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 285 (pp. 1628-1629). doi:10.3233/978-1-61499-672-9-1628

DOI: 10.3233/978-1-61499-672-9-1628

The 2015 AAAI Fall Symposium Series Reports (Journal article)

Ahmed, N., Bello, P., Bringsjord, S., Clark, M., Hayes, B., Kolobov, A., . . . Spaan, M. (2016). The 2015 AAAI Fall Symposium Series Reports. AI MAGAZINE, 37(2), 85-90. doi:10.1609/aimag.v37i2.2661

DOI: 10.1609/aimag.v37i2.2661

2015

Computing Convex Coverage Sets for Faster Multi-objective Coordination (Journal article)

Roijers, D. M., Whiteson, S., & Oliehoek, F. A. (2015). Computing Convex Coverage Sets for Faster Multi-objective Coordination. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 52, 399-443. Retrieved from http://gateway.webofknowledge.com/

Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks (Conference Paper)

Claes, D., Robbel, P., Oliehoek, F. A., Hennes, D., Tuyls, K., & Van der Hoek, W. (2015). Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks. In Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems (pp. 881-890).

Effective Approximations for Spatial Task Allocation Problems (Conference Paper)

Claes, D., Robbel, P., Oliehoek, F. A., Hennes, D., Tuyls, K., & Van der Hoek, W. (2015). Effective Approximations for Spatial Task Allocation Problems. In Proceedings of the Tenth AAMAS Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM).

Effective approximations for multi-robot coordination in spatially distributed tasks (Conference Paper)

Claes, D., Robbel, P., Oliehoek, F. A., Tuyls, K., Hennes, D., & Van Der Hoek, W. (2015). Effective approximations for multi-robot coordination in spatially distributed tasks. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2 (pp. 881-890).

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version) (Conference Paper)

Robbel, P., Oliehoek, F. A., & Kochenderfer, M. J. (n.d.). Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version). Retrieved from http://arxiv.org/abs/1511.09080v2

Exploiting submodular value functions for faster dynamic sensor selection (Conference Paper)

Satsangi, Y., Whiteson, S., & Oliehoek, F. A. (2015). Exploiting submodular value functions for faster dynamic sensor selection. In Proceedings of the National Conference on Artificial Intelligence Vol. 5 (pp. 3356-3363).

Factored upper bounds for multiagent planning problems under uncertainty with non-factored value functions (Journal article)

Oliehoek, F. A., Spaan, M. T. J., & Witwicki, S. J. (2015). Factored upper bounds for multiagent planning problems under uncertainty with non-factored value functions. IJCAI International Joint Conference on Artificial Intelligence, 2015-January, 1645-1651.

Influence-Optimistic Local Values for Multiagent Planning (Conference Paper)

Oliehoek, F. A., Spaan, M. T. J., & Witwicki, S. (2015). Influence-Optimistic Local Values for Multiagent Planning. In Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems (pp. 1703-1704).

Influence-Optimistic Local Values for Multiagent Planning --- Extended Version (Journal article)

Oliehoek, F. A., Spaan, M. T. J., & Witwicki, S. (n.d.). Influence-Optimistic Local Values for Multiagent Planning --- Extended Version. Retrieved from http://arxiv.org/abs/1502.05443v2

Influence-optimistic local values for multiagent planning (Conference Paper)

Oliehoek, F. A., Spaan, M. T. J., & Witwicki, S. J. (2015). Influence-optimistic local values for multiagent planning. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 3 (pp. 1703-1704).

Multi-source entity resolution for genealogical data (Chapter)

Efremova, J., Calders, T., & Weiss, G. (2015). Multi-source entity resolution for genealogical data. In Population Reconstruction (pp. 129-154). doi:10.1007/978-3-319-19884-2_7

DOI: 10.1007/978-3-319-19884-2_7

Point-based planning for multi-objective POMDPs (Conference Paper)

Roijers, D. M., Whiteson, S., & Oliehoek, F. A. (2015). Point-based planning for multi-objective POMDPs. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2015-January (pp. 1666-1672).

Quality Assessment of MORL Algorithms: A Utility-Based Approach (Journal article)

Zintgraf, L. M., Kanters, T. V., Roijers, D. M., Oliehoek, F., & Beau, P. (2015). Quality Assessment of MORL Algorithms: A Utility-Based Approach. Benelearn 2015: Proceedings of the 24th Annual Machine Learning Conference of Belgium and the Netherlands. Retrieved from http://www.benelearn2015.nl/

Scalable Planning and Learning for Multiagent POMDPs. (Conference Paper)

Amato, C., & Oliehoek, F. A. (2015). Scalable Planning and Learning for Multiagent POMDPs.. In B. Bonet, & S. Koenig (Eds.), AAAI (pp. 1995-2002). AAAI Press. Retrieved from http://www.aaai.org/Library/AAAI/aaai15contents.php

Scalable planning and learning for multiagent POMDPs (Conference Paper)

Amato, C., & Oliehoek, F. A. (2015). Scalable planning and learning for multiagent POMDPs. In Proceedings of the National Conference on Artificial Intelligence Vol. 3 (pp. 1995-2002).

Scaling POMDPs For Selecting Sellers in E-markets---Extended Version (Journal article)

Irissappane, A. A., Oliehoek, F. A., & Zhang, J. (n.d.). Scaling POMDPs For Selecting Sellers in E-markets-Extended Version. Retrieved from http://arxiv.org/abs/1511.09147v2

Secure routing in wireless sensor networks via POMDPs (Journal article)

Irissappane, A. A., Zhang, J., Oliehoek, F. A., & Dutta, P. S. (2015). Secure routing in wireless sensor networks via POMDPs. IJCAI International Joint Conference on Artificial Intelligence, 2015-January, 2617-2623.

Solving Multi-agent MDPs Optimally with Conditional Return Graphs (Conference Paper)

Scharpff, J., Roijers, D. M., Oliehoek, F., Spaan, M. T., & De Weerdt, M. (2015). Solving Multi-agent MDPs Optimally with Conditional Return Graphs. In The Tenth AAMAS Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM). Istanbul, Turkey. Retrieved from http://masplan.org/msdm2015:papers

Solving Multi-agent MDPs Optimally with Conditional Return Graphs (Conference Paper)

Scharpff, J., Roijers, D. M., Oliehoek, F. A., Spaan, M. T. J., & Weerdt, M. D. (2015). Solving Multi-agent MDPs Optimally with Conditional Return Graphs. In Proceedings of the Tenth AAMAS Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM).

Variational Multi-Objective Coordination (Conference Paper)

Roijers, D. M., Whiteson, S., Ihler, A., & Oliehoek, F. A. (2015). Variational Multi-Objective Coordination. In NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems.

2014

A POMDP based approach to optimally select sellers in electronic marketplaces (Conference Paper)

Irissappane, A. A., Oliehoek, F. A., & Zhang, J. (2014). A POMDP based approach to optimally select sellers in electronic marketplaces. In 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 Vol. 2 (pp. 1329-1336).

Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty (Conference Paper)

Oliehoek, F. A., & Amato, C. (2014). Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty. In Proceedings of the Ninth AAMAS Workshop on Multi-Agent Sequential Decision Making in Uncertain Domains (MSDM).

Bounded Approximations for Linear Multi-Objective Planning under Uncertainty (Extended Abstract) (Conference Paper)

Roijers, D., Scharpff, J., Spaan, M., Oliehoek, F. A., Weerdt, M. D., & Whiteson, S. (2014). Bounded Approximations for Linear Multi-Objective Planning under Uncertainty (Extended Abstract). In Proceedings of the 26th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2014) (pp. 168-169).

Bounded approximations for linear multi-objective planning under uncertainty (Conference Paper)

Roijers, D. M., Scharpff, J., Spaan, M. T. J., Oliehoek, F. A., De Weerdt, M., & Whiteson, S. (2014). Bounded approximations for linear multi-objective planning under uncertainty. In Proceedings International Conference on Automated Planning and Scheduling, ICAPS Vol. 2014-January (pp. 262-270).

Dec-POMDPs as Non-Observable MDPs (Report)

Oliehoek, F. A., & Amato, C. (2014). Dec-POMDPs as Non-Observable MDPs (IAS-UVA-14-01). Intelligent Systems Lab, University of Amsterdam.

Linear support for multi-objective coordination graphs (Conference Paper)

Roijers, D. M., Whiteson, S., & Oliehoek, F. A. (2014). Linear support for multi-objective coordination graphs. In 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 Vol. 2 (pp. 1297-1304).

Scalable Planning and Learning for Multiagent POMDPs: Extended Version (Journal article)

Amato, C., & Oliehoek, F. A. (n.d.). Scalable Planning and Learning for Multiagent POMDPs: Extended Version. Retrieved from http://arxiv.org/abs/1404.1140v2
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