Updating Probabilistic Epistemic States in Persuasion Dialogues

Abstract

Abstract. In persuasion dialogues, the ability of the persuader to model the persuadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the persuadee's belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated at each step of the dialogue. However, there is a need to better understand these proposals, and moreover, to gain insights into the space of possible update functions. So in this paper, we present a general framework for update functions in which we consider existing and novel update functions.

Polynomial-Time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation

“…Given some new evidence represented as a set of linear atomic constraints (and possibly some existing constraints that we want to preserve), we want to update P . To this end, different update operators have been studied in [15,19,17]. Here, we are interested in update operators of the following type.…”

“…Several update operators in [19,17] are based on the idea of satisfying new evidence by changing the current epistemic state in a minimal way. The distance between two probability functions is determined by looking at the probabilities that they assign to possible worlds.…”

“…In stage 1, we identify all probability distributions that minimize an atomic distance measure. Instead of comparing probability functions elementwise on possible worlds, atomic distance measures compare probability functions only based on the probabilities that they assign to arguments [19]. We consider a quadratic variant here that will allow us to compute some updates in polynomial time.…”

Towards a framework for computational persuasion with applications in behaviour change1

“…The aim is to choose moves that will increase belief in positive persuasion goals or decrease belief in negative persuasion goals. We have proposed methods for updating beliefs during a dialogue [76,77,80], for efficient representation and reasoning with the probabilistic user model [61], for representing uncertainty in the user model [78], and for harnessing decision-theoretic decision rules for optimizing the choice of arguments based on the user model [62].…”

Empirical Methods for Modelling Persuadees in Dialogical Argumentation

“…To represent and reason with belief in arguments, we can use the epistemic approach to probabilistic argumentation [8]- [11]. Applying this approach to modelling persuadee's beliefs in arguments has produced methods for updating beliefs during a dialogue [12]- [14], for efficient representation and reasoning with the probabilistic user model [15], and for harnessing decision rules for optimizing the choice of arguments based on the user model [16]. These developments offer a wellunderstood theoretical and computationally viable framework for applications such as behaviour change.…”