Optimization of dialectical outcomes in dialogical argumentation

Abstract

When informal arguments are presented, there may be imprecision in the language used, and so the audience may be uncertain as to the structure of the argument graph as intended by the presenter of the arguments. For a presenter of arguments, it is useful to know the audience's argument graph, but the presenter may be uncertain as to the structure of it. To model the uncertainty as to the structure of the argument graph in situations such as these, we can use probabilistic argument graphs. The set of subgraphs of an argument graph is a sample space. A probability value is assigned to each subgraph such that the sum is 1, thereby reflecting the uncertainty over which is the actual subgraph. We can then determine the probability that a particular set of arguments is included or excluded from an extension according to a particular Dung semantics. We represent and reason with extensions from a graph and from its subgraphs, using a logic of dialectical outcomes that we present. We harness this to define the notion of an argumentation lottery, which can be used by the audience to determine the expected utility of a debate, and can be used by the presenter to decide which arguments to present by choosing those that maximize expected utility. We investigate some of the options for using argumentation lotteries, and provide a computational evaluation.

Comfort or safety? Gathering and using the concerns of a participant for better persuasion

“…In our previous work on developing APSs, we have primarily focussed on beliefs in arguments as being the key aspect of a user model for making good choices of move in a dialogue. To represent and reason with beliefs in arguments, we can use the epistemic approach to probabilistic argumentation [7,41,48,49,51,63,72] which has been supported by experiments with participants [62]. In applying this approach to modelling a persuadee's beliefs in arguments, we have developed methods for: (1) updating beliefs during a dialogue [43,45,50]; (2) efficiently representing and reasoning with the probabilistic user model [34]; (3) modelling uncertainty in the modelling of persuadee beliefs [36,46]; (4) harnessing decision rules for optimizing the choice of argument based on the user model [35,37]; (5) crowdsourcing the acquisition of user models [47]; and (6) modelling a domain in a way that supports the use of the epistemic approach [18].…”

“…There are some proposals for strategies using probability theory to, for instance, select a move based on what an agent believes the other is aware of [70], or, to approximately predict the argument an opponent might put forward based on data about the moves made by the opponent in previous dialogues [32]. Using the constellations approach to probabilistic argumentation, a decision-theoretic lottery can be constructed for each possible move [51]. Other works represent the problem as a probabilistic finite state machine with a restricted protocol [42], and generalize it to POMDPs when there is uncertainty on the internal state of the opponent [33].…”

Towards Computational Persuasion via Natural Language Argumentation Dialogues

“…We can use crowdsourcing for the acquisition of user models based on concerns [28] and beliefs [36]. To represent and reason with beliefs in arguments, we can use the epistemic approach to probabilistic argumentation [56,32,4,40,48] which has been supported by experiments with participants [47]. In applying the epistemic approach to user modelling, we have developed methods for: (1) updating beliefs during a dialogue [33,34,39]; (2) efficiently representing and reasoning with a probabilistic user model [25]; and (3) modelling uncertainty in the modelling of persuadee beliefs [35,27].…”

Towards a framework for computational persuasion with applications in behaviour change1

“…We can update the model with each argument/attack presented, and we can use expected utility to identify best choice of argument/attack to present [81,83].…”