Photo of Dr Frans Oliehoek

Dr Frans Oliehoek PhD

Lecturer Computer Science

Publications

2017

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

Claes, D., Oliehoek, F., Baier, H., & Tuyls, K. (2017). Decentralized Online Planning for Multi-Robot Warehouse Commissioning. In 16th International Conference on Autonomous Agents and Multiagent Systems. Sao Paulo, Brazil.

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

Satsangi, Y., Whiteson, S., Oliehoek, F. A., & Spaan, M. T. J. (n.d.). Exploiting submodular value functions for scaling up active perception. Autonomous Robots. doi:10.1007/s10514-017-9666-5

DOI: 10.1007/s10514-017-9666-5

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. doi:10.1007/978-3-319-28929-8

DOI: 10.1007/978-3-319-28929-8

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. Retrieved from http://gateway.webofknowledge.com/

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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