Performance boundary identification method for autonomous decision-making systems based on neighbor boundary degree

The ability of an autonomous decision-making system to withstand disruptions is reflected in its performance boundary, which is a crucial indicator of its resilience. A performance boundary identification approach based on neighbor boundary degrees is proposed for autonomous decision-making systems, taking into account the features of multi-space distribution and incremental creation of performance boundary data. In order to solve the absolute scale measurement problem, we first design the neighbor boundary degree index. Then, we propose a performance boundary identification process based on neighbor boundary degree, which addresses the challenges of a complex performance boundary search space and non-uniform scale throughout the space. Secondly, the incremental performance boundary Identification method based on neighbor boundary degree is proposed by combining incremental data with the original identification results in order to accurately describe the performance boundary and to achieve efficient incremental data processing. An approximate nearest neighbor search optimization technique that enhances local sensitive hashing is then suggested in order to address the efficiency issue of nearest neighbor search and reverse nearest neighbor search that arose in the incremental phase. Finally, benchmark systems and path planning systems are used as autonomous decision-making systems to carry out verification and analysis of theoretical research work. Experimental results show that the performance boundary identification method based on neighbor boundary degree has good generalization ability of algorithm parameters. In the experiment on the path planning system, this method has a 13.68% higher boundary recognition accuracy and a 91.57% shorter running time compared to the comparison method.