An assessment approach to assess the likelihood of rock burst in coal mines by integrating the Multi-Agent System with data fusion techniques is proposed in this paper. We discuss an optimal algorithm for multi-sensor data fusion to improve the accuracy and reliability of the source data. Some model-based situation quantization methods are described and a rock burst situation quantitative assessment model incorporating improved Dempster–Shafer theory is presented. The Auto-Regressive, Moving Average and Holt–Winters models are used to address indefinability and inaccuracy of the prediction. A case study demonstrates that the proposed situation assessment model is capable of producing relatively accurate forecasts, and thereby it can provide coal mine decision-makers with an overview of the development of rock bursts. 
A situation assessment method for rock burst based on multi-agent information fusion