Multi-Agent Case-Based Diagnosis in the Aircraft Domain Pascal Reuss 12 , Klaus-Dieter Althoff 12 , Alexander Hundt 1 , Wolfram Henkel 3 , and Matthias Pfeiffer 3 1 German Research Center for Artificial Intelligence Kaiserslautern, Germany http://www.dfki.de 2 Institute of Computer Science, Intelligent Information Systems Lab University of Hildesheim, Hildesheim, Germany http://www.uni-hildesheim.de 3 Airbus Kreetslag 10 21129 Hamburg, Germany Abstract. Aircraft diagnosis is a highly complex topic. Many knowl- edge sources are required and have to be integrated into a diagnosis system. This paper describes the instantiation of a multi-agent system for case-based aircraft diagnosis based on the SEASALT architecture. This system will extend a existing rule-based diagnosis system, to make use of the experience of occurred faults and their solutions. We describe the agents within our diagnosis system, the planned diagnosis workflow and the current status of the implementation. For the case-based agents, we give an overview of the initial case structures and similarity measures. In addition, we describe some challenges we have during the development of the multi-agent system, especially during the knowledge modeling. 1 Introduction An aircraft is a complex mechanism, consisting of many subsystems. Occurring faults cannot be easily tracked to their root cause. A fault can be caused by one system, by the interaction of several systems or by the communication line between systems. Finding the root cause can be very time and resource con- suming. Therefore the use of experience from successfully found and solved root causes can be very helpful for aircraft diagnosis. This paper describes the in- stantiation of a multi-agent system (MAS) based on the SEASALT architecture. The MAS contains several Case-Based Reasoning (CBR) systems to store the experience and provide this knowledge for diagnosis. In the next section, we give an overview of the OMAHA project and the SEASALT architecture. Sec- tion 2 contains related work with comparing our approach to other diagnosis and multi-agent approaches. In Section 3 we describe the instantiation of the SEASALT components for our MAS and describe the case-bases agents with the case structure and similarity measures of the underlying CBR systems in more detail. Section 4 gives a short summary of the paper and an outlook on future work. Copyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany. 43