Ken Satoh (National Institute of Informatics, Japan)
Translating German traffic cases into logical rules
This is a joint work with May Myo Zin at my center and Georg Borgess at University of Saarland. In this talk, I will report the work on extracting normative sentences from German traffic cases and translating them into logical rules. The development of autonomous vehicles (AVs) requires a comprehensive understanding of both explicit and implicit traffic rules to ensure legal compliance and safety. While explicit traffic laws are well-defined in statutes and regulations, implicit rules derived from judicial interpretations and case law are more nuanced and challenging to extract. This research firstly investigates the potential of Large Language Models (LLMs), particularly GPT-4o, in automating the extraction of implicit traffic normative sentences from judicial decisions. Then we investigate how to translate these normative sentences into a logical form. We explore to use large language models (LLMs) to automate the translation of traffic rules into PROLOG, a declarative programming language ideal for encoding logical rules and relationships. The proposed methodology consists of three key phases: extracting traffic rules from diverse textual sources, structuring them into Logical English (LE) for clarity and consistency, and translating them into PROLOG representations using advanced natural language processing (NLP) techniques, including in-context learning and fine-tuning. The experimental results demonstrate the effectiveness of LLMs in automating this process, achieving high accuracy in translation.