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We look at the problem in belief revision of trying to make inferences about what an agent believed—or  will  believe—at a given moment, based on an observation of how the agent has responded to some sequence of previous belief revision inputs over time. We adopt a ‘reverse engineering’ approach to this problem. Assuming a framework for iterated belief revision which is based on sequences, we construct a model of the agent that ‘best explains’ the observation. Further considerations on this best-explaining model then allow inferences about the agent's epistemic behaviour to be made. We also provide an algorithm which computes this best explanation.

Reconstructing an Agent's Epistemic State from Observations about its Beliefs and Non-beliefs