Demo Data from EDBT 2026 / reviewers in the wild / expert
Kim van den Houten
dblp:339/5633
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0001-7540-980XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
constraint programming |
0.9 | 1 | 2025 | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags · AAAI 2025 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
scheduling |
0.9 | 1 | 2025 | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags · AAAI 2025 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
temporal network |
0.9 | 1 | 2025 | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags · AAAI 2025 |
Methods — techniques the papers use, named apart from their topics
simple temporal networks with uncertainty · 0.9online rescheduling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-LagsabstractThis study investigates scheduling strategies for the stochastic resource-constrained project scheduling problem with maximal time lags (SRCPSP/max). Recent advances in Constraint Programming (CP) and Temporal Networks have re-invoked interest in evaluating the advantages and drawbacks of various proactive and reactive scheduling methods. First, we present a new, CP-based fully proactive method. Second, we show how a reactive approach can be constructed using an online rescheduling procedure. A third contribution is based on partial order schedules and uses Simple Temporal Networks with Uncertainty (STNUs). Our statistical analysis shows that the STNU-based algorithm performs best in terms of solution quality, while also showing good relative offline and online computation time Kim van den Houten, Léon Planken, Esteban Freydell, David M. J. Tax, Mathijs de Weerdt |
AAAI | 1 |
| 2024 | Learning from Scenarios for Repairable Stochastic Scheduling
Kim van den Houten, David M. J. Tax, Esteban Freydell, Mathijs de Weerdt |
CPAIOR (2) | 1 |