The source code of some of our algorithms is available in software toolboxes. An overview of the current toolboxes and the corresponding papers is provided below.
- SolvePOMDP - A toolbox containing exact and approximate algorithms for Partially Observable Markov Decision Processes. In particular, it contains the software implementation of the accelerated vector pruning algorithm described in the following paper: Erwin Walraven and Matthijs T. J. Spaan. Accelerated Vector Pruning for Optimal POMDP Solvers. Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017. For feature requests or any other inquiries, please contact Erwin Walraven.
- MADP - The MultiAgent Decision Process toolbox contains several algorithms and tools for planning and learning in multiagent systems. For more details we refer to the dedicated webpage. Contact: Matthijs Spaan.
- STP and all-pairs shortest path algorithms and benchmark data - This page collects various benchmark problem sets to compute shortest paths on, for example to solve all-pairs short path problems such as useful for solving Simple Temporal Planning (STP) problems. In particular, it contains the code and data described in the following paper: L. R. Planken, M. M. de Weerdt and R. P.J. van der Krogt (2012) "Computing All-Pairs Shortest Paths by Leveraging Low Treewidth", Volume 43, pages 353-388.
- Multi-Machine Scheduling Lower Bounds Using Decision Diagrams - This is an implementation of decision diagrams in order to obtain lower bounds for a multi-machine scheduling problem, as described in van den Bogaerdt, P., de Weerdt, M.M.: Multi-machine scheduling lower bounds using decision diagrams. Operations Research Letters 46(6), 616–621 (2018). For the article and datasets, please see the "Downloads" tab here.