Persuasive Stories for Multi-Agent Argumentation
Floris J. Bex1 and Trevor Bench-Capon2
1Argumentation Research Group, School of Computing, University of Dundee
Dundee DD1 4HN, United Kingdom
florisbex@computing.dundee.ac.uk
2

Department of Computer Science, University of Liverpool
Liverpool L69 7ZF, United Kingdom
tbc@csc.liv.ac.uk

Abstract
In this paper, we explore ideas regarding a formal logicalmodel which allows for the use of stories to persuadeautonomous software agents to take a particular course ofaction. This model will show how typical stories –sequences of events that form a meaningful whole – can beused to set an example for an agent and how the agent might
adapt his own values and choices according to the values
and choices made by the characters in the story.

Introduction
Practical reasoning, justifying a choice of action by aparticular agent in a particular situation, is central to thereasoning abilities of autonomous agents (Wooldridge
2000). But different agents may rationally disagree as tothe action to choose, since they may differ in their interests
and aspirations. One way to represent this computationallyis to differentiate agents according to how they order socialvalues: an argument to raises taxes may be accepted if
equality is preferred to enterprise and rejected with theopposite preference.
A philosophically and computationally feasible accountof practical reasoning with values has recently beendeveloped by (Atkinson et al. 2007). This approach uses
simple arguments of a syllogistic form to guide a choice ofaction: if faced with a set of circumstances, then the agent
should perform a certain action to get the consequences hedesires, that is, to achieve his goals and promote his values.
Such arguments can attack each other and thus an agent
can try to persuade itself or another agent on theappropriate action to take in the current circumstances. The
dialectical process of persuasion using arguments, thevarious forms of argument and the ways in which they canbe criticized have been extensively discussed in theliterature (Walton 1990; van Eemeren et al. 1996).
Furthermore, the computational properties of sucharguments are clearly defined (Dung 1995; Prakken andVreeswijk 2002).
However, in a context of persuasion people will often
tell a story (i.e. a sequence of events caused or experienced
by actors) rather than give an argument based on
conditional rules. For example, teaching a child not to lie iseasier done by telling the story of The Boy Who Cried
Wolf1 than by presenting an argument that one should nottell lies in circumstances where there is no gain in telling
the lie because this will demote the value of honesty andconsequently people will not believe you when you do tell
the truth. In a sense, stories can be thought of as more
psychologically feasible than arguments, because ourthoughts and memories are more often collections ofstories rather than arguments (Schank 1990).
A story does not persuade by imparting explicit rules orvalues, but instead by having an agent identify with thesituations or actors in a story. Understanding a story
involves identifying the situations in the story, the internalmotivations of the characters in the story, the choices madeby these characters and the consequences of these choices.
Persuasion then occurs through identification with one ofthe characters, through a combination of correspondence offacts and attitudes, and then recognising the consequencesof the attitude and adapting one’s own attitude accordingly.
Identification with a character can be situational (I am in asimilar situation, e.g. a child hearing the Boy who criedWolf) or attitudinal (that character has the most similarvalue preferences to me).

1

Copyright © 2010, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

This well-known fable tells about a shepherd boy who cries ‘Wolf!
Wolf!’ to amuse himself. At first the villagers come to his aid, but after a
few times they do not believe him anymore, even when there is a real
wolf who scattered his sheep.

Conclusions and future research
As indicated above, the main work on both persuasive andpractical reasoning has so far mainly concentrated onreasoning with arguments. Agent-based persuasion andnegotiation uses a variety of techniques (see e.g. Rahwan
et al. 2004), but stories are not one of them. The use ofstories by agents has mainly been studied in the context ofcase-based reasoning (Ram 1989), where agents use
specific knowledge of previous situations and courses ofevents (i.e. stories) to solve a problem. However, we are
not necessarily interested in problem-solving, but rather in
how other agents may be persuaded. Consequently, we aim
to explore the role of stories in persuasion, particularly inthe context of value-based practical reasoning with agents.
Important questions in this respect are:
1. What is the structure of a story?
What are the important elements (e.g. values, types ofsituations, types of characters) in understanding a story?
What is the point of a story and how can this point bederived from a story?
2. When is a story persuasive for a particular agent?
Which elements in a story (e.g. values, goals, types ofsituations, types of characters) serve as an example for theaudience? How does an agent compare his own beliefs andknowledge to the elements in a story or the point of astory? When and to what extent does an agent identify witha story or a character?
3. Given an agent and his knowledge of different stories,
how does an agent choose an action?
Which conclusions does an agent draw from its
identification with a particular story or character? How
does an agent choose between multiple plausible stories?
4. What is the relation between arguments and stories in
the context of practical reasoning?
The aim is to answer each of these questions by proposing
a model for agent-based practical reasoning that uses
stories. Important is that this model is not just)computationally feasible, but that it also draws insprirationfrom psychology and sociology (e.g. Macy and Willer2002). Question 1 has been discussed in much of theresearch on story structures in literary theory, cognitive
psychology and AI (see Bex 2009 and Kwiat 2009 for acomprehensive overview). It would be interesting to see
which story-structure is needed for current purposes. Also,a small corpus typical stories represented using thestructure of choice will be needed. Question 2 is largely an
open question, and also depends on the Multi-Agentarchitecture used. Question 3 and 4 overlap in that they areboth about the combination of argumentation and stories.
For example, drawing a conclusion from a story (or fromone’s identification with an actor in the story) can be seen
as argumentative inference. Furthermore, question 4 can beseen more broadly: what is the relation between arguments

and stories in any reasoning context? In his previous work,
Bex (2009) discusses reasoning with stories but the focus
is on how a story can be supported by arguments based onevidence and not directly on the persuasiveness of stories.

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