pallabi thesis

Author: Issa Hanou

_data/supervision.yml

@@ -38,22 +38,24 @@
   TUSS is a well-studied problem, and various approaches have been proposed. The first approach capable of solving real-world, complete TUSS instances is a local search method introduced by van den Broek et al. In this thesis, we explore an alternative approach using PDDL models. PDDL is the standard language for describing Automated Planning problems. Automated Planning is a well-established field within artificial intelligence, and new, improved algorithms are continually developed to solve PDDL models for problems similar to TUSS.
   In this thesis, we design a detailed model in PDDL and propose several methods to simplify the model so that planning algorithms perform more efficiently compared to the detailed model. When solving simplified models, a post-processing routine is employed to generate detailed shunting plans. The performance of several model-independent PDDL planners was analysed, and the best-performing planner was identified as Temporal FastDownward.
   By analysing plans obtained from experiments, we identified areas for improvement. Based on this knowledge, we developed a new TUSS-specific planner called Train Order Preserving Search (TOPS). TOPS employs a search algorithm with effective pruning of symmetrical states and a custom heuristic that guides the search towards states where the order of trains aligns with the departure order. TOPS significantly outperformed Temporal FastDownward in these experiments."
-- topic: Refactoring for AI planning
+- topic: "Macro-Actions for PDDL: A Dynamic Approach"
   cosupervisors: Sebastijan Dumancic
   name: Pallabi Sree Sarker
   id: Sarker2025
   type: MSc theses
   start: 2024-09-24
+  end: 2025-08-21
   year: 2025
-  status: inprogress
-  post_name: False
+  status: finished
+  post_name: True
+  link: https://resolver.tudelft.nl/uuid:a0a94ac8-8a8f-43a7-92a1-f625e4292578
   keywords:
-    - refactoring
-    - program synthesis
     - AI planning
-    - train dispatching
+    - macro-actions
     - forgetting
-    - inductive logic programming
+    - 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"
   name: Eric Kemmeren
   type: MSc theses

_news/msc-pallabi.md

@@ -0,0 +1,9 @@
+---
+layout: post
+date: 2025-08-21 16:11:00-0400
+year: 2025
+inline: true
+related_posts: false
+---
+
+Pallabi Sree Sarker defended her MSc thesis on [Dynamic Macro-Actions in PDDL](/education#Sarker2025).