arXiv subjects

Issa Hanou

Publications and source records attributed to Issa Hanou.

3 recordsLinked to original sources

arXiv2026

Precomputing Multi-Agent Path Replanning Using Temporal Flexibility

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.

cs.AI↗

arXiv2025

Herb.jl: A Unifying Program Synthesis Library

Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmers. Numerous synthesizers have been developed for program synthesis, offering different approaches to the exponentially growing program space. Although such state-of-the-art tools exist, reusing and adapting them remains tedious and time-consuming. We propose Herb.jl, a unifying program synthesis library written in Julia, to address these issues. Since current methods share similar building blocks, we aim to break down the underlying algorithms into extendable, reusable subcomponents. To demonstrate the benefits of using Herb.jl, we show how to implement a simple problem and grammar, and how to solve it with just a few lines of code.

cs.PL↗

arXiv2025

Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances

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.

cs.AI↗