PhotoI am affiliated with the Algorithmics group at Delft University of Technology in the Netherlands. This website contains information on my previous projects, publications and software.

News

May 2019
I successfully defended my PhD dissertation entitled 'Planning under Uncertainty in Constrained and Partially Observable Environments'.

Apr 2019
Our paper about point-based planning for finite-horizon POMDPs got accepted to JAIR.

Projects

I have been working on several research projects related to planning, scheduling, decision making under uncertainty, machine learning and artificial intelligence. In these projects I used algorithms to solve problems in the areas of mobility, transportation and smart energy systems. An overview can be found below.

Wind power

Planning and scheduling in smart grids

As part of my PhD research I contributed to a project focusing on planning and scheduling for flexible loads in smart distribution grids. My research was part of the GCP project and the NWO URSES program. More info:

Smoover app

Machine learning for traffic flow control

We developed reinforcement learning algorithms to compute speed limits for highways. These algorithms have been used to support the development of Smoover, a new Intelligent Transportation System in the Netherlands which aims to reduce traffic congestion. More info:

Publications

Point-Based Value Iteration for Finite-Horizon POMDPs

Erwin Walraven and Matthijs T. J. Spaan
Journal of Artificial Intelligence Research, vol. 65, pp. 307–341, 2019.
Planning under Uncertainty in Constrained and Partially Observable Environments

PhD dissertation, Delft University of Technology, 2019.
Column Generation Algorithms for Constrained POMDPs

Erwin Walraven and Matthijs T. J. Spaan
Journal of Artificial Intelligence Research, vol. 62, pp. 489–533, 2018.
Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs

Diederik M. Roijers, Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 28th Int. Conference on Automated Planning and Scheduling, pp. 218–226, 2018.
Accelerated Vector Pruning for Optimal POMDP Solvers

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3672–3678, 2017.
Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs

Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt and Matthijs T. J. Spaan
Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3562–3568, 2017.
Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 22nd European Conference on Artificial Intelligence, pp. 904–912, 2016.
Traffic flow optimization: A reinforcement learning approach

Erwin Walraven, Matthijs T. J. Spaan and Bram Bakker
Engineering Applications of Artificial Intelligence, vol. 52, pp. 203–212, 2016.
Planning under Uncertainty for Aggregated Electric Vehicle Charging using Markov Decision Processes

Erwin Walraven and Matthijs T. J. Spaan
International Workshop on Artificial Intelligence for Smart Grids and Smart Buildings, 2016.
Planning under Uncertainty with Weighted State Scenarios

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, pp. 912–921, 2015.
A Scenario State Representation for Scheduling Deferrable Loads under Wind Uncertainty

Erwin Walraven and Matthijs T. J. Spaan
The 10th Annual Workshop on Multiagent Sequential Decision Making Under Uncertainty, 2015.
Traffic Flow Optimization using Reinforcement Learning (abstract)

Proceedings of the 26th Benelux Conference on Artificial Intelligence, pp. 211–212, 2014.
Traffic Flow Optimization using Reinforcement Learning

Master's thesis, Delft University of Technology, 2014.
Enhancing SAT Based Planning with Landmark Knowledge

Jan Elffers, Dyan Konijnenberg, Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 25th Benelux Conference on Artificial Intelligence, pp. 64–71, 2013.

Software

My accelerated version of incremental pruning for POMDPs is available in SolvePOMDP, which is an open source software toolbox containing exact and approximate algorithms for POMDPs.

Contact

If you would like to contact me you can send me an email at:

© Erwin Walraven 2019