Automated Planning of Simple Persuasion Dialogues

We take a simple form of non-adversarial persuasion dialogue in which one participant (the persuader) aims to convince the other (the responder) to accept the topic of the dialogue by asserting sets of beliefs. The responder replies honestly to indicate whether it finds the topic to be acceptable (we make no prescription as to what formalism and semantics must be used for this, only assuming some function for determining acceptable beliefs from a logical knowledge base). Our persuader has a model of the responder, which assigns probabilities to sets of beliefs, representing the likelihood that each set is the responder’s actual beliefs. The beliefs the persuader chooses to assert and the order in which it asserts them (i.e. its strategy) can impact on the success of the dialogue and the success of a particular strategy cannot generally be guaranteed (because of the uncertainty over the responder’s beliefs). We define our persuasion dialogue as a classical planning problem, which can then be solved by an automated planner to generate a strategy that maximises the chance of success given the persuader’s model of the responder; this allows us to exploit the power of existing automated planners, which have been shown to be efficient in many complex domains. We provide preliminary results that demonstrate how the efficiency of our approach scales with the number of beliefs.