An Information-Theoretic Approach to Model Identification in Interactive Influence Diagrams

Interactive influence diagrams (I-IDs) offer a transparent and semantically clear representation for the decision-making problem in multiagent settings. They ascribe procedural models such as IDs and I-IDs to the behavior of other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. However, as model spaces are often bounded, the true models of others may not be present in the model space. In addition to considering the case when the true model is within the model space, we investigate the realistic case when the true model may fall outside the space. We then seek to identify models that are relevant to the observed behaviors of others and show how the agent may learn to identify these models. We evaluate the performance of our method in two repeated games and provide results in support.