Training Strategy Model for BDAR Based on Case-Based Reasoning

Abstract

Traditional battlefield damage assessment and repair training system focuses on the damage assessment. It ignores the battlefield environment. And the training task is selected randomly. The efficient of this training system is poor. Especially, it doesn't suit to the emergency training for BDAR. A new method based on CBR for BDAR training task generating is proposed. The frame technology is used on the BDAR case representation. It analysis the characteristic factor which effecting calculating of the similarity between attributes and cases. Both trainee's ability vector and case's ability vector are used in the similarity's solving, and a reverse mapping translation is promoted between two vector's evaluating linguistics. A retrieval strategy which consists of three steps is presented. This strategy can find a more suitable case for certain trainee, the efficient of training can be improved.