Sepeedeh Shahbeigi

Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi et al.

Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task planning. However, the quality and reliability of these explanations heavily depend on effective prompt engineering. The lack of a systematic understanding of how diverse stakeholder groups formulate and refine prompts hinders the development of tools that can automate this process. We introduce COMPASS (COgnitive Modelling for Prompt Automated SynthesiS), a proof-of-concept self-adaptive approach that formalises prompt engineering as a cognitive and probabilistic decision-making process. COMPASS models unobservable users' latent cognitive states, such as attention and comprehension, uncertainty, and observable interaction cues as a POMDP, whose synthesised policy enables adaptive generation of explanations and prompt refinements. We evaluate COMPASS using two diverse cyber-physical system case studies to assess the adaptive explanation generation and their qualities, both quantitatively and qualitatively. Our results demonstrate the feasibility of COMPASS integrating human cognition and user profile's feedback into automated prompt synthesis in complex task planning systems.

Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi et al.

Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challenge by introducing an approach for adapting probabilistic system models to handle out-of-ODD scenarios while, in parallel, providing quantitative guarantees. Our approach dynamically extends the coverage of existing system situation capabilities, supporting the verification and adaptation of the system's behaviour under unanticipated situations. Preliminary results demonstrate that our approach effectively increases system reliability by adapting its behaviour and providing formal guarantees even under unforeseen out-of-ODD situations.

Calum Corrie Imrie, Ioannis Stefanakos, Sepeedeh Shahbeigi et al.

The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities, incorporating ML components introduces significant safety and assurance challenges. Among ML techniques, reinforcement learning (RL) is particularly suited for CPS due to its capacity to handle complex, dynamic environments where explicit models of interaction between system and environment are unavailable or difficult to construct. However, in safety-critical applications, this learning process must not only be effective but demonstrably safe. Safe-RL methods aim to address this by incorporating safety constraints during learning, yet they fall short in providing systematic assurance across the RL lifecycle. The AMLAS methodology offers structured guidance for assuring the safety of supervised learning components, but it does not directly apply to the unique challenges posed by RL. In this paper, we adapt AMLAS to provide a framework for generating assurance arguments for an RL-enabled system through an iterative process; AMLAS-RL. We demonstrate AMLAS-RL using a running example of a wheeled vehicle tasked with reaching a target goal without collision.