Issa Hanou

2026

1. ICAPS

   Revisiting Landmarks: How To Learn from Previous Plans to Generalize over Problem Instances

   Issa Hanou, Sebastijan Dumančić, and Mathijs De Weerdt

   In 9th Workshop on Generalization in Planning (GenPlan) at ICAPS’26, Jun 2026

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   Landmarks have greatly improved AI planning by identifying the must-reach states for any plan that solves an instance. However, as landmarks are ground atoms, they are instance- and object-specific, and thus do not capture any general knowledge about the problem domain. Moreover, landmark graphs often contain repetitive chains of landmarks, thereby increasing graph size. We propose generalized landmarks that describe general intermediate goals for an entire problem domain. Generalized landmarks extend beyond the predicates of a domain by using state functions, which are independent of a specific object and capture repetition. A directed generalized landmark graph is constructed that includes loops to identify repetitive subplans. We give examples of hand-constructed graphs and discuss our approach to learning them from a small set of solved instances. To use generalized landmarks for planning, we present a simple counting heuristic and show how it exploits loop behavior and local landmark guides to find plans more efficiently.

2. SoCS

   Precomputing Multi-Agent Path Replanning Using Temporal Flexibility

   Issa Hanou, Eric Kemmeren, Devin Wild Thomas, and Mathijs De Weerdt

   In Proceedings of the Nineteenth International Symposium on Combinatorial Search, Jun 2026

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   Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not yield an efficient plan, and sometimes cannot even yield a feasible one. On the other hand, replanning other agents may lead to a cascade of changes and delays, and it is computationally expensive. We show how to efficiently replan a single delayed agent by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay that the agent can take without changing the order with agents other than the initially delayed agent, or further delaying other agents. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent and returns the changes to the other agents within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network and in the MovingAI MAPF benchmark set. Our experiments show that FlexSIPP provides effective solutions relevant to real-world adjustments, and within a reasonable timeframe.

2024

1. ICAPS

   Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub

   Issa Hanou*, Devin Wild Thomas*, Wheeler Ruml, and Mathijs De Weerdt

   In Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling, May 2024

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   Train routing is sensitive to delays that occur in the network. When a train is delayed, it is imperative that a new plan be found quickly, or else other trains may need to be stopped to ensure safety, potentially causing cascading delays. In this paper, we consider this class of multi-agent planning problems, which we call Multi-Agent Execution Delay Replanning. We show that these can be solved by reducing the problem to an any-start-time safe interval path planning problem. When an agent has an any-start-time plan, it can react to a delay by simply looking up the precomputed plan for the delayed start time. We identify crucial real-world problem characteristics like the agent’s speed, size, and safety envelope, and extend the any-start-time planning to account for them. Experimental results on real-world train networks show that any-start-time plans are compact and can be computed in reasonable time while enabling agents to instantly recover a safe plan.