Can computers deliberately deceive? - A simulation tool and its application to Turing's imitation game
In this paper, we describe how agents can deceive within a probabilistic framework for representing their mental state: in doing so, we challenge the so-called sincerity assumption in Human-Computer interaction (HCI) and multi-agent systems (MAS). We distinguish deception from its special case of lie and characterize different forms of deception, by identifying several criteria for distinguishing among them. In particular, we propose a model of information impact on the Receiver's mind. As the message Sender must plan its strategy by considering the Receiver's criteria for believing, we also discuss some of these criteria, like content plausibility, source informativity, and information safety. We apply this model to a simplified version of Turing's Imitation Game and describe how we implemented a Simulator of deceptive strategies that we called Mouth of Truth. We conclude the paper by describing an evaluation study that enabled us to verify the validity of our method and to revise it in part.
deception modeling belief networks belief ascription uncertain reasoning