Rômulo Meira-Góes

Shih-Jie Shih, Jonghan Lim, Ilya Kovalenko et al.

Supervisory control of discrete-event systems provides formal guarantees of correctness with respect to a plant model and specification. However, these guarantees heavily rely on the plant model, which could deviate from nominal behavior due to modeling errors or faults. Recent notions of discrete robustness model deviations as a set of additional transitions that are added to the plant. The discrete robustness is defined as all sets of extra transitions for which the supervised system still guarantees a desired specification. However, this notion suffers from scalability due to the large solution space and conservatism since most deviations are infeasible in practice. This paper proposes to address these two issues using a neurosymbolic computing framework for discrete robustness analysis of safety properties. First, a neural reasoning layer based on Large Language Models infers a set of feasible deviation transitions from system models, specifications, and domain knowledge. Next, a symbolic layer computes the discrete robustness guarantees over the inferred deviation set. We evaluate our framework on three case studies, demonstrating that our method identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition-based analysis.

Jonghan Lim, Mostafa Tavakkoli Anbarani, Rômulo Meira-Góes et al.

Manufacturing industries are facing increasing product variability due to the growing demand for personalized products. Under these conditions, ensuring safety becomes challenging as frequent reconfigurations can lead to unintended hazardous behaviors. Multi-agent control architectures have been proposed to improve flexibility through decentralized decision-making and coordination. However, these architectures are based on predefined task models, which limit their ability to adapt task planning to new product requirements while preserving safety. Recently, large language models have been introduced into manufacturing systems to enhance adaptability, but reliability remains a key challenge. To address this issue, we propose a control architecture that leverages the flexibility of large language models while preserving safety on the manufacturing shop floor. Specifically, the proposed framework verifies large language model-enabled task allocations by using temporal logic and discrete event systems. The effectiveness of the proposed framework is demonstrated through a case study that involves a multi-robot assembly scenario, showing that unsafe tasks can be allocated safely before task execution.