Belief Revision

Belief revision studies how rational agents update their beliefs when encountering new, potentially conflicting information. Current research focuses on developing computationally feasible revision operators, particularly for complex logics and large language models (LLMs), and exploring the relationship between belief revision and various rationality norms, including coherence and explanation-based approaches. This work is crucial for improving the reliability and robustness of AI systems, as well as providing a deeper understanding of human cognition and reasoning processes. Furthermore, investigations into the efficiency and accuracy of belief revision in LLMs are driving the development of improved knowledge representation and reasoning techniques.