The authors provide a comprehensive review of the history and development of computational belief change. They trace its evolution from Doyle's taxonomy in 1980 to the AGM framework and beyond. The paper analyzes how pre-AGM computational approaches relate to AGM theoretical constructs, highlighting both continuities and transformations. This foundation is intended to inform contemporary implementation challenges and provide a basis for systematic analysis of robust computational blueprints. --- Why it matters: This work matters because it provides a clear understanding of the historical context and theoretical foundations of belief change in AI. Engineers can use this knowledge to develop more effective algorithms for updating beliefs and managing uncertainty, which is crucial for many applications, including decision-making systems and expert systems. Source: https://arxiv.org/abs/2608.14567