I am affiliated with the Algorithmics group at Delft University of Technology in the Netherlands. This website contains information on my research, publications, activities and other news you may be interested in.
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.
Mar 2018
A paper describing our approximate algorithms for Constrained POMDPs will appear in the Journal of Artificial Intelligence Research (JAIR).
Jan 2018
Our work on improving exact algorithms for multi-objective and partially observable MDPs (with Diederik Roijers) will be presented at ICAPS.
Jan 2017
We released SolvePOMDP, a software toolbox containing exact and approximate algorithms for POMDPs.
Nov 2016
Our work on accelerated POMDP solvers and resource-constrained MDP planning will be presented at AAAI-17.
Jun 2016
The ECAI conference has accepted our paper about planning under uncertainty for electric vehicle charging.
My research interests lie in the field of planning, scheduling, decision making under uncertainty, machine learning and artificial intelligence. Below you can find an overview of recent projects.
Planning and scheduling in smart grids
My work focuses on planning and scheduling algorithms to control flexible loads in distribution grids, exploiting stochastic information on power generation and consumption. My research is part of the GCP project and the NWO URSES program. More info:
Smoover
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.
Activities
Conferences - I am part of the local organizing committee of ICAPS 2018 in Delft.
MSc courses - Algorithms for Planning and Scheduling, Advanced Algorithms, Randomized Algorithms.
Our 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:
I am affiliated with the Algorithmics group at Delft University of Technology in the Netherlands. This website contains information on my research, publications, activities and other news you may be interested in.
© Erwin Walraven 2019