Contribution of Case Based Reasoning (CBR) in the Exploitation of Return of Experience. Application to Accident Scenarii in Railroad Transport
This addresses safety improvement in railroad transport by creating a knowledge-sharing tool for domain experts, though it appears incremental in applying existing AI methods to this specific domain.
The study developed a software tool using Case Based Reasoning (CBR) to analyze accident scenarios in railroad transport, aiming to prevent future incidents by proposing prevention measures and improving safety.
The study is from a base of accident scenarii in rail transport (feedback) in order to develop a tool to share build and sustain knowledge and safety and secondly to exploit the knowledge stored to prevent the reproduction of accidents / incidents. This tool should ultimately lead to the proposal of prevention and protection measures to minimize the risk level of a new transport system and thus to improve safety. The approach to achieving this goal largely depends on the use of artificial intelligence techniques and rarely the use of a method of automatic learning in order to develop a feasibility model of a software tool based on case based reasoning (CBR) to exploit stored knowledge in order to create know-how that can help stimulate domain experts in the task of analysis, evaluation and certification of a new system.