Representing Deception

It is easy to see that social robots will need the ability to detect and evaluate deceptive speech; otherwise they will be vulnerable to manipulation by malevolent humans. More surprisingly, we argue that effective social robots must also be able to produce deceptive speech.

We argue that, although belief revision mechanisms surely operate at the level of single agents, we must also consider the need to lift an agent’s understanding of the belief revision process to the knowledge level in order to intentionally guide other agents’ revision processes with whom it socially interacts.

We argue that distinguishing between types of deception is required to generate successful action. We introduce a Framework for Identifying Deceptive Entities and demonstrate that it has the representational power to discriminate categories of deception.

We explore the representational and inferential requirements for supporting a rich notion of belief revision. More to the point, we argue that although belief revision mechanisms surely operate at the level of single agents, we must also consider the need to lift an agent’s understanding of the belief revision process to the knowledge level in order to intentionally guide other agents’ revision processes with whom it socially interacts.

We introduce a practical, computational framework that enables socially aware inference. We then demonstrate the framework’s ability to model a common, complex, and under-investigated aspect of human social behavior: deception.