Explainable AI for delivery route optimization using Reinforcement Learning
Year: 2025
Type: article
Abstract: Artificial intelligence (AI) techniques are being applied across an expanding range of fields and industries. However, many AI models operate as “black boxes”, making it challenging to understand the reasoning behind their outputs. This lack of transparency can lead to bias, discrimination, errors, and defects. Explainable AI (XAI) techniques are a possible solution to this issue. In this work, Reinforcement Learning (RL) is used to solve a delivery route optimization problem, while an intrinsic interpretability XAI method (Rule-based modelling) and two post-hoc analysis methods (Shapley values and Local Interpretable Model-agnostic Explanations (LIME)) are applied to explain and compare predictions of the RL agent.
Source: IFAC-PapersOnLine
Institution: SINTEF Manufacturing
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414567915