BRL: A Toolkit for Learning How an Agent Performs Belief Revision

Belief revision occurs when an agent receives new information that may conflict with their current beliefs. This process can be modelled by a formal belief revision operator. However, in a practical scenario, simply defining abstract revision operators is not sufficient. A truly intelligent agent must be able to observe how others have revised their beliefs in the past, and use this information to predict how they will revise their beliefs in the future. In other words, an agent must be able to learn the mental model that is used by other agents. This process involves combining two traditionally distinct areas of Artificial Intelligence to produce a general reasoning system. In this paper, we discuss challenges faced in using various learning approaches to learn belief revision operators. We then present the BRL toolkit: software can learn the revision operator an agent is using based on past revisions. This is a tool that bridges formal reasoning and machine learning to address a common problem in practical reasoning. Accuracy and efficiency of the approach are discussed.