Persuasive Precedents
Floris Bexa, Trevor Bench-Caponb and Bart VerheijcaSchool of Computing, University of Dundee, UK
Department of Computer Science, University of Liverpool, UKc
Department of Artificial Intelligence, University of Groningen, The Netherlands
E-mail: florisbex@computing.dundee.ac.uk, tbc@liverpool.ac.uk, b.verheij@ai.rug.nlbAbstract
Stories can be a powerful vehicle of persuasion. We typically use stories to link known events into coherent wholes. One way toestablish coherence is to appeal to past examples, real or fictitious. These examples can be chosen and critiqued using legal case-based
reasoning (CBR) techniques. In this paper, we apply these techniques to factual stories, assessing a story about the facts using
precedents. We thus show how legal reasoning in a CBR model is equally applicable to reasoning with factual stories.
Keywords: Stories, precedents, case-based reasoning, argumentation

1.

Introduction

Stories can be a powerful vehicle of persuasion. They canbe used, for example, to present evidence about “whathappened” in a particular case in a coherent andbelievable way (Bex et al. 2010), or to convince others tofollow a particular course of action (Bex andBench-Capon 2010). A story does not persuade by
imparting explicit rules like an argument does, but insteadby exposing a coherent and plausible sequence of events.
Thus, for example, we more readily believe a set ofevidence if we can structure it using some coherent story(Bennett and Feldman 1981).
One way to establish the coherence of a story is to appeal
to examples, real or fictitious. In previous work (Bex 2011,
Bex and Verheij 2011), we argued that a story is coherent
if it fits a story scheme, a generalised pattern of events
akin to a script (Schank and Abelson 1977). Story
schemes model the way things tend to happen in the world;
for instance, the restaurant script lists the roles (customer,
waiter) and sequence of events (ordering, eating, paying)for a typical restaurant visit. These abstract story schemes
depend on precedent stories: the restaurant scheme we use
is based on our experiences of restaurants.
In realistic argumentative contexts people will usuallyfind it more effective to cite a precedent story rather thanan abstract story scheme. As an example, suppose two
people who know each other meet on a train: on one story
it is a chance encounter, in another it is an arranged
meeting. If both regularly use the train at similar times achance meeting is entirely plausible. If they rarely use thetrain, or live elsewhere, it is less so. But citing a particularstory can help, particularly a personal one: you remember
when you met Bill on the Rialto bridge? Neither of you
knew the other was in Venice, but these coincidences do
happen. The object here is to establish from personal
experience that the improbable actually does occur fromtime to time, so the coincidence is at least possible. An
appeal to personal experience or an appeal to awell-known story is much more powerful than citing astory scheme for chance encounters: A is at location L forreason RA - B is at location L for reason RB - RA and RB
are unrelated - A and B meet. The real story provides aunity to elements which would remain entirely
disconnected in the abstract scheme.
Citing a similar story thus helps establishing coherence.
Here, it is important that the current story and the
supporting example be relevantly similar (Walton 2010).
In AI, this similarity is usually enforced by requiring thateach item in the precedent matches exactly one item in thetarget and vice-versa, and that if there is a correspondence
between two statements, then there must also becorrespondences between its arguments.
If we cannot find a precedent story which matches on
enough facts, we can attempt to find a more generalprecedent (e.g. citing a story which contains a coincidence
but says nothing about chance meetings). However, insuch a case it is easier to reduce the force of the example
by pointing to relevant differences. These distinctions canthen be emphasised and downplayed.
The work in AI and cognitive science (see Gentner andForbus for a comprehensive overview) has so far mainly
focused on retrieving precedents, matching the current
story to the precedent and calculating the degree in which
a story and its precedent match according to somehard-coded principles. Work in legal case-based
reasoning (CBR) (Aleven 1997, Ashley 1990) presents a
more fluid approach, in which identifying relevant
similarities and differences between legal cases becomes
the subject of argumentation.
In this paper, we show how techniques for mapping
common in AI can be combined with argumentativetechniques for citing, emphasising and downplaying
stories can be applied to factual (i.e. non-legal) stories.

2.

Legal Case Based Reasoning

The leading legal CBR systems are HYPO (Ashley 1990)
and CATO (Aleven 1997). We will base our approach to
CBR on CATO. The key idea of CATO is that cases canbe described as collections of factors, stereotypical factsituations that have legal relevance (e.g. in cases
concerning trade secrets we have information_disclosed_in_negotiations, plaintiff_took_security_measures). The
facts of the case determine whether particular factors arepresent or absent from a case. Typically a case willcontain a number of factors, some favouring thedefendant and some favouring the plaintiff, and the court
will need to decide which set of reasons prevail.
Guidance on the relative strengths of sets of factors can beobtained from the precedent cases. If the combination
presented in a case under consideration (the current case)has been found before, then it would be expected that thedecision in the past case would be the decision in thecurrent case. Normally, however, there will be no exact
match and missing and additional factors will serve to
distinguish the current case from the precedent. Equallysome precedents may point one way and other the other,so providing counter examples.
CATO supports a three ply form of argument:
1. One side cites a precedent case (a case with factors incommon with the current case) decided for their side;
2. Other side presents counter examples (cases withfactors in common decided for the other side) anddistinguishes the cited cases;
3. Original side may distinguish the counter example,
and cite any additional reasons to support their side.
CATO recognises the following argument moves: Citing acase to a past case with a favourable outcome (Ply 1);
Distinguishing a case with an unfavourable outcome (Ply2); Emphasising the significance of a distinction (Ply 2);
Downplaying the significance of a distinction (Ply 3);
Citing a favourable case to emphasise strengths (Ply 3);
Citing a favourable case to argue that weaknesses are notfatal (Ply 3); Citing a counterexample (Ply 2). In section 4
we further discuss these moves when we relate them tostories about the facts.

3.

Stories and story schemes

Stories are finite sequences of facts, events or states ofaffairs that are assumed, at least for the moment, to have
happened or existed. Stories are specific rather than
general. Consider a simple example story about Tony (T),
who killed Gordon (G) in a knife fight: stabs(T,G) –stabbing_injured(G) – died(G). Story schemes are abstract
scenarios, the structure of which is close to that of stories.
Basically, a story scheme is a sequence containing
narrative units (Propp 1968), which represent (sets of)generalized facts or types of facts: has_motive(x) –stabs(x,y) – stabbing_injures(y) – dies(y). The narrative
units thus represent what we call story roles, general roles
that facts in a story can take.
A story can be matched to a story scheme by assigning thefacts to their respective story roles, that is, matching thefacts in the story to the relevant narrative units in thescheme (Bex 2011, Bex and Verheij 2011). This matchingis similar to what in existing work in AI and cognitive
science (Gentner and Forbus 2011) is called mapping:given a base case and a target case, a mapping consists ofa set of correspondences, each linking a particular item inthe base with a particular item in the target. Here, both thebase and target are usually specific (instantiated)structures. We follow Schank (1986) in that we match
specific stories to story schemes.
After matching, the coherence of the story is determinedby checking whether the story has no “loose ends” (there
are facts in the story that do not match a narrative unit in
the scheme) and whether the story “has all its parts” (allthe narrative units in the relevant scheme are matched by afact in the story) (Bex 2010). For example, our example
story does not complete the example scheme, as there isno fact that matches the narrative unit has_motive(x).

4.

Argument Moves and Precedents

CATO’s cases are very similar to story schemes. Story
schemes are clusters of abstract facts (narrative units) andcases are clusters of legally qualified abstract facts(factors). Hence, the three ply form of argument and theargument moves from CBR can be used in a factual
situation as well as a legal situation.
To determine the coherence of a story, a precedent story iscited as the basis for the construction of a story scheme.
After a precedent has been cited, variants of CATO’s
argument moves can then be used to argue about thedifferences and similarities between the precedent and thecurrent story. Effectively, these argument moves are about
whether the current story relevantly matches the story
scheme based on the precedent.
Citing a precedent story: This move establishes a story
scheme based on the precedent story, and then (implicitly)
argues that the current story matches that scheme.
Distinguishing a precedent story: The precedent story andthe current story will each contain elements beyond thoserequired for matching a common story scheme. For
example, the current story and the precedent may have thecentral action in common (stabbing), but may well differ
as to the type of people involved. Such differences can beoffered as reasons to argue that the current story does notmatch the scheme established by the precedent.
We can identify different kinds of distinction between
stories (cf. Wyner and Bench-Capon 2007), depending onwhether the current story is missing a fact required tomake the story coherent, or has an additional fact (that theprecedent lacks) which jeopardises the coherence of thestory. In the first case, there is an assumption satisfied in
the precedent which is not satisfied in the current case:this means that the current story is not complete, it doesnot “have all its parts”. In the second case, there is a fact in
the current story which supplies an exception to the story
scheme.
Emphasising the significance of a distinction: this moveaccompanies a distinction and attempts to pre-empt any
attempt to downplay; it seems as much rhetorical as
logical.
Downplaying the significance of a distinction:
Downplaying a distinction has variants according to thenature of the distinction. If the distinction is an unsatisfied
assumption, it is necessary to point to some fact in thecurrent story which can play a similar role, thus having
the current story complete the story scheme after all. If thecurrent story has what appears to be an exception,
downplaying involves finding a fact in the current story
that provides an exception to that exception.
Citing a case to emphasise strengths and citing a case toargue that weaknesses are not fatal: These two moves
respond to a distinction and involve citing other stories
which can serve as precedent stories. When it is argued
that the current story misses an assumption, newprecedent stories can help to show that this assumption is
not vital to the coherence of the story. Against the second
type of distinction one can cite further precedent stories
matching the current story and containing the alleged
exceptions, showing that it is possible to have thisadditional fact in a coherent story. These moves are
essentially attempts to shift the story scheme relied onslightly. The difference between emphasise strengths andweakness not fatal seems to be largely rhetorical, focusing
on the strengths or alleged weaknesses of the current
story, respectively.
Citing a counterexample: This move involves citing a
new precedent story that argues for a different story
scheme. Counterexamples are used to demonstrate that
there are alternatives, and so avoid tunnel vision.

5.

An example of reasoning with precedents

Having looked at the individual moves, let us consider an
example to show them in action. In our example, theobservation to explain is that Tony killed Gordon in aknife fight. That Tony killed Gordon is not at issue: there
were plenty of witnesses as it took place quite openly in a
Glasgow street. But it is important to get a story
establishing Tony’s motive, as this will affect the sentence.
Wilma and Bert are discussing the matter. Note how byciting a precedent (top) for the current story (bottom),they are establishing a possible story scheme (middle).
Wilma: ‘Tony and Gordon were youths from the sameneighbourhood; perhaps it was a gang thing, like WestSide Story. 1 ’ In the precedent, Bernardo (B) of the
Sharks gang kills Riff (R) of the Jets gang in a knife
fight. Here, citing a precedent story attempts toestablish that the motive was gang feud:
same_area(B, R)
feud(B, R)
kills(B,R)
match

same_area(x, y)
match

match

feud(x, y)
match

match

kills(x,y)
match

same_area(T, G)
feud(T, G)
kills(T,G)
Bert: ‘But Tony and Gordon are middle class kids, andwhoever heard of middle class kids being in gangs?’
Here Bert distinguishes by mentioning an exception:
the story has an additional fact, that Tony and Gordon
are middle class, that the precedent story lacks – Jets

and Sharks are lower class immigrant gangs. Thus,
Bert argues that the current story does in fact notmatch the scheme established by the West Side Story
precedent, because being middle class is an exception
to the rule that people from the same neighbourhood
may be involved in a feud:
same_area(T, G) middle_class(T) middle_class(G) sofeud(T, G)This rule means that there can be no match between
the current story and scheme 1.
Wilma: ‘Maybe it was a family feud like in Romeoand Juliet, they were middle class.’ This is an example
of weaknesses not fatal, citing a precedent story witha similar motive that does include the alleged
exception: Romeo Montague (R) kills Tybalt Capulet(T) with a knife and the Capulets and Montagues weremiddle class:
R T middle_class, feud(R, T),
kills(R, T)
match

match

x

y middle_class, feud(x, y),match

kills(x, y)

match

match

T

G middle_class, feud(T, G),

kills(T, G)

match

Bert: ‘The Capulets and Montagues were Italian, andvendettas are very Mediterranean, but this was
Scotland.’ Bert distinguishes by mentioning a missing
assumption, that only in Italy do middle class people
get into feuds:
R T middle_class, R T Italian,feud(R, T)
match

x

Most of our precedents will be taken from fiction. Everyone knows
real examples of these schemes, but they know different examples:classic fiction provides a common cultural repository of stories.

match

y prone_to_feuding, feud(x, y)??no match
match
match
T G middle_classfeud(T, G)
Wilma: ‘But Tony and Gordon’s families were
supporters of football clubs involved in a notorious
feud, Glasgow Rangers and Celtic FC’ Wilma
downplays the distinction by providing facts that cantake the place of x and y are Italian:x y prone_to_feuding
match

different_clubs(T, G)
Bert: ‘But Tony was estranged from his family, as he
was in a relationship with Gordon’s sister.’ Bert again
distinguishes with exception: once disowned andallied to the other family it is unlikely that Tony would
continue the football feud.
different_clubs(T, G)
involved_with_family(T, G) sofeud(T, G)
Wilma: ‘But Romeo still killed Tybalt in Romeo andJuliet, even though he was involved with Tybalt’s
cousin Julia.’ Wilma downplays by denying theexception, pointing to her previous precedent.
R T Italian, involved_with_family(R, T), feud(R, T)
match

x
1

match

y middle_class, x

match

match

y prone_to_feuding, involved_with_family(x,y), feud(x,y)
match

match

match

different_clubs(T,G),involved_with_family(T,G),feud(T, G)

Bert: ‘Maybe it was about Gordon’s sister. Perhaps
Gordon started the fight, like in Hamlet, so Tony acted
in self defence.’ Bert now changes main story schemeby citing a counter example, in which Hamlet (H)defends himself after Laertes (L) attacks him because
the latter blames Hamlet for the death of his sister,
Ophelia (O).
attacks(L,H) defends(H)matchattacks(x,y)

defends(y)matchattacks(G,T)

defends(T)Wilma: ‘But in Gordon’s case, his sister did not die so
he would have less incentive to attack Tony.’ Wilma
distinguishes by mentioning a missing assumption,that the attacker’s sister died.
sister(O, L) dies(O)attacks(L,H) defends(H)matchsister(z, y)match

dies(z)??no match

attacks(x,y)

defends(y)matchattacks(G,T) defends(T)Bert: ‘In Cavelleria Rustica, no-one died but Alfio (A)still attacked Turrido (T) to protect his honour’ Bertargues that weakness not fatal: and cites anotherprecedent that also does not include the missing
assumption (that the attacker’s sister died) but stillmatches the story scheme.
attacks(A,T) defends(T)matchattacks(x,y)

defends(y)matchattacks(G,T)

defends(T)Note that the debate is not just there to satisfy curiosity. It
matters legally which story is accepted. A fight mutuallyentered into (West Side Story and Romeo and Juliet)
would be manslaughter, but a gangland killing would geta heavier sentence than a family feud in the current
climate. Finally if we follow Cavelleria Rustica and theLaertes role of Hamlet, we can explain Tony’s role as self
defence and he might even be acquitted.

6.

Conclusions

In this paper, we have shown how reasoning with factual
stories and story schemes can be modelled in the style oflegal Case Based Reasoning models. It turns out that theprecedent cases of CBR have a natural counterpart infactual reasoning: story schemes. The facts of the story
can then be mapped to the elements of these story
schemes (narrative units) in the same way as facts of acase can be mapped to the elements of cases (factors).
This allows for the argumentative moves of CATO to be
applied to factual stories, enabling moves like citation anddistinction in discussions.
The link between CBR and stories allows for a more
realistic way of discussing story coherence: precedent
stories can be cited, obviating the need to explicitly model
abstract story schemes. The argument moves then enablea natural dialogue concerning the facts of the story.

The current model thus specifies Walton’s (2010) Scheme
for Argument from Analogy, which uses story schemes todetermine the similarity between precedents cases/stories
and the current case/story. This type of argument from
Analogy is not just useful when talking about past events(as is the case in this paper), but also when trying topersuade someone to a particular course of action. We are
more inclined to follow some course of action which has
proven successful in the past. Thus, citing precedents inwhich success was achieved might convince someone totake the same course of action, provided their current
situation is relevantly similar to the precedent.
Previous work in AI on general analogy (Gentner andForbus 2011) captures the logic of analogy: it tells us what
we require to state one and how to apply one. It does not,however, allow for argument moves about the analogicalmappings. In contrast, the work in AI and Law (Ashley
1990, Aleven 1997) captures precisely these argument
moves (e.g. analogizing and distinguishing) in a dynamic
argumentation setting, whilst leaving precise mappings
implicit. The framework for analogical case-based
reasoning sketched in this paper therefore aims to capture
both a precise matching and a possibility of argumentative
discussion about this mapping.
The framework not only allows for the matching offactual stories to other factual stories (as in Schank 1986)or legal cases to other legal cases (as in HYPO andCATO), but also provides a way of matching factual
stories to legal cases via legal rules (Bex and Verheij
2011). Furthermore, as discussed in (Bex 2011), thecorrespondences themselves, represented as legal orcommonsense rules, can also be subject of argumentation.
Thus, the framework is the first to capture all aspects ofboth factual and legal case-based reasoning in a single
defeasible framework.
In this paper the theoretical foundations for factual
precedent-based reasoning have been built. However, inorder to make practical implementations feasible, a
corpus of stories that can act as precedents is needed.
These stories are ideally expressed in some common
ontology to facilitate automatic processing. It is up to theyoung Computational Narratives field to tackle any
problems concerning such a corpus and an ontology
head-on, so as to provide an impetus not only to thisresearch but to the entire field. Once a corpus is available,
implementation of the CATO argumentation moves isrelatively straightforward. In addition to the original
version in (Aleven 1997), they have been implemented asa multi-agent system (Allen et al 2000) and usingargumentation schemes (Bench-Capon 2012).

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