Towards explainable decision support using hybrid neural models for logistic terminal automation
This work addresses the need for explainable and causally reliable decision support systems in transportation logistics, particularly for multimodal terminal automation, though it appears incremental by building on existing interpretability methods.
The paper tackles the problem of integrating Deep Learning into System Dynamics for logistics automation, which improves scalability and predictive accuracy but sacrifices explainability and causal reliability. It presents a hybrid neural framework that combines DL with interpretability techniques to create models with semantically meaningful variables, aiming to bridge the gap between black-box predictions and critical decision support in complex environments.
The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, these gains are often offset by the loss of explainability and causal reliability $-$ key requirements in critical decision-making systems. This paper presents a novel framework for interpretable-by-design neural system dynamics modeling that synergizes DL with techniques from Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. The proposed hybrid approach enables the construction of neural network models that operate on semantically meaningful and actionable variables, while retaining the causal grounding and transparency typical of traditional SD models. The framework is conceived to be applied to real-world case-studies from the EU-funded project AutoMoTIF, focusing on data-driven decision support, automation, and optimization of multimodal logistic terminals. We aim at showing how neuro-symbolic methods can bridge the gap between black-box predictive models and the need for critical decision support in complex dynamical environments within cyber-physical systems enabled by the industrial Internet-of-Things.