updated news and papers

Author: Issa Hanou

_bibliography/papers.bib

@@ -54,3 +54,24 @@ @mastersthesis{Hanou2022Applying
   pdf              = {masterthesis.pdf},
   html             = {https://repository.tudelft.nl/islandora/object/uuid:2ca12cd2-315e-4a31-9a2a-67a77d8988bf},
 }
+@preprint{Hanou2025Revisiting,
+  title={Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances}, 
+  author={Issa Hanou and Sebastijan Duman\v{c}i\'{c} and Mathijs De Weerdt},
+  year={2025},
+  eprint={2508.21564},
+  archivePrefix={arXiv},
+  primaryClass={cs.AI},
+  html={https://arxiv.org/abs/2508.21564},
+  abbr={arXiv},
+  abstract={We propose a new framework for discovering landmarks that automatically generalize across a domain. These generalized landmarks are learned from a set of solved instances and describe intermediate goals for planning problems where traditional landmark extraction algorithms fall short. Our generalized landmarks extend beyond the predicates of a domain by using state functions that are independent of the objects of a specific problem and apply to all similar objects, thus capturing repetition. Based on these functions, we construct a directed generalized landmark graph that defines the landmark progression, including loop possibilities for repetitive subplans. We show how to use this graph in a heuristic to solve new problem instances of the same domain. Our results show that the generalized landmark graphs learned from a few small instances are also effective for larger instances in the same domain. If a loop that indicates repetition is identified, we see a significant improvement in heuristic performance over the baseline. Generalized landmarks capture domain information that is interpretable and useful to an automated planner. This information can be discovered from a small set of plans for the same domain.},
+}
+@misc{Hanou2025MultiAgent,
+  author={Issa Hanou and Mathijs De Weerdt},
+  title={Multi-Agent Pathfinding for Railway Routing},
+  year={2025},
+  abbr={RailDresden},
+  venue={RailDresden 2025: 11th International Conference on Railway Operations Modelling and Analysis - Technische Universität Dresden, Dresden, Germany},
+  html={https://research.tudelft.nl/en/publications/multi-agent-pathfinding-for-railway-routing},
+  abstract={Research in railway operations has mostly focused on operations research methods. However, these real-world problems have a state-based nature, which makes them very suitable for AI models, such as the Multi-Agent Pathfinding problem, where agents move in a grid and need to be routed from their start to their goal location without colliding with each other. The core aspect of problems like train shunting and train dispatching is routing, which is often not the main focus of current mathematical formulations. Therefore, we apply the state-of-the-art algorithms to the railway problems of shunting and dispatching and study their usability for routing trains. The Multi-Agent Pathfinding problem is often solved with one of two algorithms: conflict-based search (a two-stage algorithm detecting conflicts between individual paths and using A* search to find new conflict-free paths), and branch-cut-and-price (a linear program adding cuts (row generation) based on problem-specific constraints, and finding new paths to be selected that satisfy all constraints using a pricer). We modify these algorithms to include more railway details. First, we allow for the matching of train units (i.e., ensure the necessary train units of a certain type are available for departure) by specifying goals for agent (type) groups instead of single agent goals. Moreover, we add goal sequences for servicing stations and agents of different sizes, and we study specific aspects of the railway infrastructure to exploit in the algorithm. Finally, we show the use of Multi-Agent Pathfinding solvers in different railway settings and analyze the conditions for success.},
+  poster={poster_raildresden25.pdf}
+}

_data/supervision.yml

@@ -56,15 +56,17 @@
     - refactoring
     - program synthesis
   abstract: "Automated Planning, also known as Artificial Intelligence (AI) planning is a branch of AI focused on automated decision-making and scheduling. A subproblem within AI Planning is domain-independent planning, where we want to develop methods that are generalizable for solving planning problems in many domains. A popular modelling language for domain-independent planning is PDDL. In PDDL, we model our problems as having some start state and some goal state; these states are defined by the truth-values of a set of defined predicates applied to a set of objects with corresponding types. In this work, we explore the concept of dynamic macro-actions for PDDL, which are macro-actions whose utility are re-evaluated as we solve more problems, and does not require prior training. We find that dynamic macro-actions are a promising method, showing average improvements in the number of nodes explored in the search space of up to 84% depending on the domain."
-- topic: "Replanning in advance for train scheduling"
+- topic: "Introducing Flexibility in Any-Start-Time Safe Interval Path Planning: A Case Study on the Dutch Railway Network"
   name: Eric Kemmeren
   type: MSc theses
+  cosupervisors: Mathijs de Weerdt
   id: Kemmeren2025
-  status: inprogress
+  status: finished
   start: 2025-01-16
   end: 2025-09-09
   year: 2025
-  post_name: False
+  post_name: True
+  link: https://resolver.tudelft.nl/uuid:04890fe5-d983-4b91-90bd-dc5da7129161
   keywords:
     - replanning
     - AI planning
@@ -73,6 +75,7 @@
     - flexibility
     - any-start-time planning
     - safe interval path planning
+  abstract: "During the daily operation of the railway network, ProRail is responsible for handling delays and planning ad hoc train movements. Train handling documents aid the traffic controllers in common situations. But when multiple trains are delayed, and these documents do not apply, they are left to their own expertise. In this thesis, we introduce FlexSIPP, an algorithm to plan or replan agents in an existing multi-agent plan. FlexSIPP builds upon the prior works of any-start-time safe interval path planning, where the current routes of the agents are seen as moving obstacles. FlexSIPP loosens this restriction by introducing flexibility: the ability for an agent to delay its plan while minimally impacting other agents. This algorithm is evaluated on the Dutch railway network. By finding tipping points, that is, the moment it is better to switch the order of two trains on the track to minimize the delay, we can recreate train handling documents. We show that FlexSIPP finds the same solutions within a minute in the case that no other trains are delayed. This implies that FlexSIPP is also able to aid traffic controllers in the case that other trains are delayed."
 - topic: "Landmarks in planning"
   cosupervisors: Sebastijan Dumancic
   type: BSc theses

_data/venues.yml

@@ -9,3 +9,11 @@
 "TRA":
   url: https://traconference.eu/
   color: "#17a756"
+
+"arXiv":
+  url: https://arxiv.org
+  color: "#b31b1b"
+
+"RailDresden":
+  url: https://tu-dresden.de/bu/verkehr/die-fakultaet/veranstaltungen/raildresden2025
+  color: "#00008c"

_layouts/bib.html

@@ -125,8 +125,10 @@
 			{%- capture entrytype -%}<em>In {{entry.booktitle}}</em> {%- endcapture -%}
 		{%- elsif thesis contains entry.type -%}
 			{%- capture entrytype -%}<em>{{entry.school}}</em> {%- endcapture -%}
+		{%- elsif entry.type == "preprint" -%}
+			{%- capture entrytype -%}<em>preprint {{entry.abbr}}:{{entry.eprint}}</em>{%-endcapture -%}
 		{%- else -%}
-			{%- capture entrytype -%}{%- endcapture -%}
+			{%- capture entrytype -%}<em>{{entry.venue}}</em>{%- endcapture -%}
 		{%- endif -%}
 
 		{%- if entry.month -%}

_news/ansya-25.md

@@ -0,0 +1,9 @@
+---
+layout: post
+date: 2025-09-01 13:31:00-0400
+year: 2025
+inline: true
+related_posts: false
+---
+
+I'm on the program committee for the [ANSyA](https://ansya-workshop.github.io/2025/) workshop at ECAI 2025.

_news/lecture-planning-25.md

@@ -0,0 +1,9 @@
+---
+layout: post
+date: 2025-09-10 17:30:00-0400
+year: 2025
+inline: true
+related_posts: false
+---
+
+I gave my lecture again on AI Planning for the Probabilistic AI and Reasoning MSc course.

_news/msc-eric.md

@@ -0,0 +1,9 @@
+---
+layout: post
+date: 2025-09-09 16:11:00-0400
+year: 2025
+inline: true
+related_posts: false
+---
+
+Eric Kemmeren defended his MSc thesis on [Flexibility for Safe Interval Path Planning in the Dutch Railway Networkd](/education#Kemmeren2025).