Towards Adversarial Reasoning in Statistical Relational Domains

Statistical relational artificial intelligence combines first-order logic and probability in order to handle the complexity and uncertainty present in many real-world domains. However, many real-world domains also include multiple agents that cooperate or compete according to their diverse goals. In order to handle such domains, an autonomous agent must also consider the actions of other agents. In this paper, we show that existing statistical relational modeling and inference techniques can be readily adapted to certain adversarial or non-cooperative scenarios. We also discuss how learning methods can be adapted to be robust to the behavior of adversaries. Extending and applying these methods to real-world problems will extend the scope and impact of statistical relational artificial intelligence.