Incremental dynamic case-based reasoning-MAS: From static to dynamic cycle

In this paper we present our approach in the field of Case-Based Reasoning (CBR). This approach is based on the reuse of previous traces that are similar to the current situation in a dynamic way. Several approaches have been used in this area, but they suffer from some limitations in automated real-time management dynamic parameters (the designer should be defined beforehand the different situations possible). We propose a multi-agent multi-layer architecture based on Incremental Dynamic Case-Based Reasoning (IDCBR) able to study dynamic situations (recognition, prediction, and learning situations). We propose a generic approach able to learn automatically from their experiences in order to acquire the knowledge automatically. Based on the Case-Based Reasoning and multi-agent paradigm, we propose a modification of the static CBR cycle in order to introduce a dynamic process of Case-Based Reasoning based on a dynamic similarity measure able to evaluate in real time the similarity between a dynamic situation (target case) and past experiences stored in the memory (sources case) in order to predict the target case in the future.