Story schemes for argumentation about the facts of a crime
Floris J. Bex1 and Bart Verheij2
1

Argumentation Research Group, School of Computing, University of Dundee
Dundee DD1 4HN, United Kingdom
florisbex@computing.dundee.ac.uk
2

Department of Artificial Intelligence, University of Groningen
P.O. Box 407, 9700 AK Groningen, The Netherlandsb.verheij@ai.rug.nl

Abstract
In the literature on reasoning on the basis of evidence, two
traditions exist: one argument-based, and one based on
narratives. Recently, we have proposed a hybrid perspectivein which argumentation and narratives are combined. This
formalized hybrid theory has been tested in a sense-making
software prototype for criminal investigators and decision
makers. In the present paper, we elaborate on the role ofcommonsense knowledge. We argue that two kinds ofknowledge are essential: argumentation schemes and story
schemes. We discuss some of the research issues that need
to be addressed.

Introduction: complex reasoning with facts
and evidence
Reasoning with evidence in a criminal case is a difficultand laborious process. In a large case, investigators, judges
and jurors are faced with a mass of unstructured evidenceof which they have to make sense. They are expected,
often without any prior formal training, to map out
complex scenarios about what might have happened in thecase and to assess the potential relevance of a vast amountof evidence to each of these hypothetical scenarios.
Humans can only process a limited amount of information
at once and various cognitive and social biases such as
tunnel vision, groupthink and confirmation bias may leadto unwanted situations and mistakes. Such mistakes, which
seem almost unavoidable given the difficult nature of thetask, can have a large impact on those involved in the case,as evidenced by some (well-publicized) miscarriages ofjustice in the previous century, such as the US Sacco and
Vanzetti case or the British Sally Clark case.

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

In the theoretical literature on reasoning about the factsof criminal cases, two perspectives are prevalent. The firstgoes back to Wigmore's work (Wigmore 1931) and has
been the basis for much work in the field of legal theory(Anderson et al. 2005). In this argument-based tradition,evidence is treated as giving reasons for and against thefacts of the case. In argumentative reasoning, one performs
consecutive reasoning steps from the evidence to the facts
in issue, using commonsense knowledge to warrant theinference steps. By evaluating the justificatory force of
reasons (which depends on the warranting generalizations)and by resolving conflicts between reasons (e.g. byweighing), it is determined which account of the facts canbe believed and why.
The second tradition is based on narratives (or stories).
Different stories – coherent sequences of events – about
what might have happened (e.g. as presented in court or as
hypothesized in the investigation) are evaluated andcompared. The belief in a story is then determined bylooking at how well it fits both the available evidence andour commonsense knowledge. This story-based perspective
originates from legal psychologists such as Pennington andHastie (1993), which has been followed up in the anchorednarratives approach by Wagenaar and colleagues (1993),
who have used their approach to analyse possible
miscarriages of justice.
Recently, Bex and colleagues (Bex 2009c; Bex et al.
2007) have argued for a hybrid approach, in whicharguments and narratives can be used in conjunction as
well as interchangeably. This hybrid approach was shown
to be a natural way of modelling the process of proof, theiterative process of constructing, testing and justifying
hypotheses in crime investigation and decision- making.
The hybrid theory was used as the basis of a sense-making
tool, AVERS (van den Braak 2010), which allows the
mapping and visualization of one‟s knowledge about a caseusing both stories and arguments (cf. Reed et al. 2007, whotalk about similar tools in which arguments can bemapped).

When reasoning in the hybrid approach, two forms ofcommonsense knowledge are used. The first is what werefer to as (inferential) generalizations, statements of theform „a therefore (presumably) b‟, which in argumentative
reasoning allow us to infer conclusions from premises (e.g.
Nicole stated Julius shot Peter therefore Julius shot Peter).
The second form of commonsense knowledge concerns
knowledge of general scenarios, clusters of events andtheir mutual (temporal or causal) relations. Such generalscenarios are basis of the particular explanatory stories that
explain observations in the case. For example, theobservation that „Peter is dead‟ can be explained byassuming a particular instance of either a „murderscenario‟, „suicide scenario‟ or „accident scenario‟.
In this paper, we show that generalizations can be seen
as argumentation schemes (Walton 1996, Walton et al.
2008), general patterns of argument that act backgroundfor particular instantiated arguments, and scenarios can beseen as story schemes (Bex 2009a), standard general eventpatterns that act as a background for particular instantiatedstories. Argumentation schemes, which originate frominformal argumentation theory, have been adopted indifferent types of research in AI and Law (Bex et al. 2003)(including reasoning with evidence) and computational
argumentation (Atkinson et al. 2006, Bex et al. 2003,
Prakken 2010, Verheij 2003). Story schemes have also
been used in AI, for example, as they the basis of natural
language understanding systems (Schank and Abelson
1977). Furthermore, the hybrid theory has made a start inshowing how stories and story schemes might be used not
only to (formally) describe but also to improve complex
reasoning processes, such as reasoning with evidence.
Important work remains to be done in order to fully
integrate stories and story schemes in computational
argumentation. Building on knowledge gained in theformalization of informal argumentation schemes, we want
to determine how story schemes can be schematized,
classified and formalized so that they can be stored for
future reference and further (automatic) reasoning. Thus,
story schemes can be further integrated in support toolsthat can be used to map and analyse complex reasoning.
In sum, this paper is a combination of old and new; it
reports on work done by the authors and their collaboratorsin the last five years and at the same time it tries to look at
possible interesting avenues for future research. The rest of
this paper is organized as follows. First, we first provide abrief overview of the hybrid approach. Then we elaborateon the idea of schemes for commonsense knowledge,
looking at the similarities and differences between
argumentation and story schemes. We then elaborate on
the formalization of story schemes, emphasizing a numberof research issues important for reasoning with legal
evidence and complex argumentation in general.

Different approaches to evidential reasoning
The Argumentative Approach
The study of proof was put on centre-stage in the first halfof the twentieth century by John Henry Wigmore (1931),
who argued for the formulation of principles for reasoning
with evidence and proof independent of the rules of law.
He set out to develop a charting method that involves
reasoning from evidence to the so-called probanda, facts tobe proven. Wigmore‟s ideas on proof gained influencemuch later, when they were elaborated upon by the New
Evidence Theorists (Anderson et al. 2005).
Wigmore argued for a basic logic underlying his chartsand his views can be seen as a preliminary take on
defeasible reasoning with arguments, as was shown by Bex
and colleagues (2003), who have modelled Wigmorean
argument charts in an argumentation logic. In such logics(see Prakken and Vreeswijk 2002 for an overview), rulesof classical logic are augmented with rules for defeasible
inference. Arguments are constructed by chaining
applications of inferences and thus one ends up with anargument tree akin to a Wigmore chart. Each argument tree
has as its premises (leaves) the pieces of evidence and asits ultimate conclusion (root) one of the probanda.
In figure 1, an example of an argument tree is given. The
arrows between the premises and (intermediate)conclusions denote (defeasible) inferences. Associatedwith a defeasible inference is an underlying generalizationthat acts as a warrant (Toulmin 1958). For example, theinference from evidence NT to conclusion 1 is justified by
the generalization „witnesses (presumably) speak thetruth‟, which can be rephrased as „witness w testifies that p,therefore (presumably) p‟.
P

1

NT

2
3

4

PR

FR

P: Julius shot and killed Peter
1: Julius shot Peter
NT: Nicole’s testimony to 1
2: Peter was killed by Julius shooting him
3: Julius had a gun
PR: Police report
4: The chemical trace left behind by the bullet that killedPeter is similar to the one left behind a bullet of the typefound in Julius’ gun
FR: Forensics expert report

Figure 1: an argument based on evidence

Arguments can also be attacked. They can be rebuttedwith an argument for the opposite conclusion and they canbe undercut with an argument for why an inference is notallowed (usually because a generalization does not apply in
the given circumstances). In the example, an argument for„Julius did not shoot (and kill) Peter‟ rebuts the conclusion
P and an argument for „Nicole is a liar‟ undercuts theinference from NT to 1, because it provides an exception to
the generalization that „witnesses speak the truth‟. These
attacking arguments can also be attacked and thus thestatus of arguments (e.g. “justified”, “overruled”) can be
determined dialectically.

The Narrative Approach
In the 1980‟s and 90‟s, the psychologists Pennington andHastie (1993) performed a number of tests to determine
exactly how (prospective) jurors in the US reasoned with alarge amount of evidence in a criminal case. They foundthat people tend to organize the evidence by building
stories about what might have happened. Such stories are
essentially networks of events which are causally linked.
The idea is that hypothesised stories explain theobservations (i.e. the evidence) in the case throughabductive inference to the best explanation (IBE). The
basic idea of abductive inference is that if we have ageneral rule cause → effect and we observe effect, we areallowed to infer cause as a possible explanation of theeffect. This cause which is used to explain the effect can bea single state or event, but it can also be a sequence of
events, a story.
Taken by itself the abductive scheme is nothing but thefallacy of affirming the consequent. However, in a setting
where alternative abductive explanations are generated andcompared, it can still be rational to accept an explanation if
no better other explanation is available. Clearly, suchreasoning is defeasible, since additional facts might giverise to new explanations. In sum, the idea of abductive
inference to the best explanation is that there are different
explanations which have to be compared. As an example,
consider figure 2, which renders two alternative stories
about why Peter died.
Story 1

1

5

A1

A2

4
E

ChoiceStory 2

B1

B2

1

B3

B4

4
5

1: Julius and Peter were having a fightA1: Julius had a gun
B1: Ellen had a gun
A2: Julius shot Peter
B2: Ellen pointed gun at Julius
B3: Julius pushed Ellen
B4: Ellen’s gun went off
4: Peter was hit by a gunshot
5: Gunshot soundE (explanandum): Peter died

Figure 2: two stories in the narrative approach

In the example case, the observation that Peter died isthe explanandum (fact to be explained). In story 1, it ishypothesized that this was caused by Julius shooting Peter,
which was itself caused by a fight between Julius andPeter. Story 2 tells an alternative tale: Ellen, Peter‟s wife,
was also present during the fight. She produced a gun,seemingly to protect her husband. Julius felt threatened bythis so he pushed Ellen, which made the gun go off,
accidentally hitting Peter. Notice that, in addition to the
explanandum, the two stories also explain the observationthat there was a gunshot.
Naturally, the choice between alternative explanations
depends on how much of the observed evidence is
explained: the more observations are explained, the betterthe story conforms to the evidence. Furthermore, thecoherence of the hypothetical stories also plays a big rolein choosing between them. This coherence depends onwhether the story conforms to our general commonsense
knowledge and on whether the story conforms to certain
case-specific assumptions. For example, the coherence ofstory 1 partly depends on whether we deem the causal link
between (1, A1) and A2 to be plausible. In other words, do
we find it plausible that someone gets out a gun and shoots
someone when he is in a fight? Similarly, with regards tostory 2, is it credible that someone like Ellen has a gun?
And how likely is a gun to accidentally fire?
Both the argumentative and the narrative approach havetheir own advantages. The argumentative approach, whichbuilds on a significant academic tradition of research on
informal and formal argumentation, is well suited for athorough analysis of the individual pieces of evidence andthe direct inferences that can be drawn from them. The
empirically tested narrative approach, on the other hand, ismainly appreciated for its natural account of crime
scenarios and causal reasoning (Heuer 1999). Bothapproaches also have their own disadvantages. The more
atomistic nature of arguments makes them unsuitable forgiving an overview of the various hypothetical scenarios
about what happened in the case and not all aspects of
causal reasoning can be found in the argumentative
approach. In the narrative approach, the individual
evidence does not have a clear place and its credibility andrelevance with regards to the facts at issue cannot bechecked easily. Furthermore, it is not always clear how one
should reason about the coherence of a story and how
stories should be compared.

The Hybrid Approach: combing stories andarguments
In his dissertation, Bex (2009) shows that when dealing
with the complex reasoning involved in large criminalcases, arguments and stories need to be combined into one
hybrid theory, where stories are used to causally explain
the explananda and arguments based on evidence are used
to support and attack these stories and each other. In the
hybrid theory, stories such as those in figure 2 areanchored in evidence using arguments such as the one infigure 1. Figure 3 shows this anchoring. The main story,

which is a general, slightly summary version of story 1(figure 2), is anchored in a “ground” of solid evidence
through sub-stories, which go into the events in the main
story in greater detail.
Main story
Julius and Peter got into a fight and Julius shotand killed Peter

Sub-story
about howfight started

Sub-story:
Julius got out his gun and shot Peter

Sub-story about
how the bullet
killed Peter

Julius’testimony

Nicole’stestimony

Forensic’sreportFigure 3: Anchoring stories in evidence in the Hybrid
Approach

The hybrid approach solves one of the most important
issues with the narrative approach as, for example,
described by Wagenaar and colleagues (1993), namely that
often the connection between the evidence and the stories
is not made clear. In the hybrid approach, stories can be
firmly anchored or, in other terms, evidentially supported.
In the example, the evidence NT1 from figure 1 supports
the event A2 in story 1. Arguments can be attacked, which
may break the “anchor‟s chain”, causing the story to be nolonger connected to the ground. If, for example, it turns out
Nicole is a liar, then the justificatory force of NT1 willdiminish.
Aside from anchoring stories in evidence, the hybrid
approach also makes it possible to reason about thecoherence of a story in a dialectical way. For example, we
could argue that „normally, people do not get out guns
when they are in a fight‟, effectively attacking the causal
relation between (1, A1) and A2 in story 1. This argument
can itself be attacked by saying, for example, that „Julius isan aggressive person who does not react to stress in thesame way other people do‟.
In the hybrid theory, there are essentially two types ofcriteria for determining the quality of a story: the extent towhich it conforms to the evidence and its coherence.
Anchoring is a criterion of the first type and the plausibilityof causal relations is a criterion of the second type. Bex(2009) gives these and other criteria as a list criticalquestions, typical sources of doubt when reasoning withthe hybrid theory. The questions that concern the extent towhich the story conforms to the evidence are as follows: Evidential support or anchoring: How much and whichof the available evidence supports the story?
 Evidential contradiction: How much and which of theavailable evidence contradicts the story?
 Evidential gaps: How many and which events in the
explanation are unsupported by evidential data?
The other three questions concern a story‟s coherence, theextent to which a story conforms to commonsense
knowledge: Plausibility: How plausible are the events and causal
relations in the story?
 Completeness: Does the story adhere to plausible story
scheme?
 Consistency: Are there elements of the story that
contradict each other?
Some of these criteria for the quality of stories, particularly
the ones pertaining to coherence, will be further discussed
below.
One of the lessons learned from the work on the hybrid
theory is that stories and arguments are essentially“communicating vessels”: when dealing with the complex
reasoning involved in large criminal cases, a narrative
approach works best for some points of a case, while inother instances an argumentative approach is the mostnatural. The hybrid approach allows for the flexibility ofthe separate argumentative and narrative approaches whilst
at the same it uses arguments and stories as complementary
tools for complex reasoning. An interesting topic for future
research is whether stories and arguments can be further
integrated. In the hybrid approach, arguments are used totalk about the individual elements of stories (i.e. events,
causal relations). How would one use arguments to reason
about stories as-a-whole? And can stories only be used ininference to the best explanation, or are there other ways ofreasoning for which stories are suitable? These arequestions that will have to be further explored; in the restof this paper, however, we will concentrate on the role ofschemes that express commonsense knowledge.

Commonsense knowledge in evidential
reasoning: argumentation schemes and story
schemes
Evidence is (quite obviously) the most important source ofknowledge in evidential reasoning; the knowledge gained
from sources of evidence provides the “ground” on which
all other reasoning about a case can be built (figure 3).
However, evidential reasoning also involves a large
amount of commonsense knowledge about the world
around us. This knowledge is not based on direct evidence,but rather it is stored in what is metaphorically called astock of knowledge, a repository of general commonsense
knowledge about the world that is “stocked” in thereasoner‟s mind (Cohen 1977).

Commonsense knowledge allows us to assume or infer
new information in a way that is as reliable as is needed in
the context. This type of knowledge, whilst often accepted
in a wide community, has varying degrees of reliability andfor every general scenario or generalization, there is atleast one example of a situation in which it does not hold.
The reliability of the commonsense knowledge we use also
depends on context. For instance, in the beginning of theinvestigative phase, it may be useful to accept someslightly implausible scenarios and generalizations in order
not to constrain the investigation too much by setting highstandards. Later on, in the actual decision-making process,more reliable commonsense knowledge corresponding to astricter burden of proof ('beyond a reasonable doubt' as
opposed to 'possibly') will be used. As such, commonsense
knowledge can have exceptions, its use involves the risk ofgetting it wrong and depends on context.
In the hybrid approach, commonsense knowledge isused in two pivotal ways. First, good and effective
reasoning with evidence requires knowledge of pragmatic(not necessarily formal) rules of inference, generalizations
which warrant inferences from premises to conclusions.
Second, knowledge of relevant scenarios or clusters ofevents is needed. Generalizations and scenarios can beseen as argumentation schemes and story schemes
respectively, schemes that encode general patterns ofreasoning. For example, a witness testimony argumentation
scheme („witness w testifies that p, therefore (presumably)p‟) is a general scheme for particular arguments based on
witness statements („witness Nicole testifies that Julius
shot Peter, therefore presumably Julius shot Peter‟).
Similarly, a “murder” story scheme (motive – murderous
action – deadly consequence) is a general scheme forparticular murder stories (fight between Julius and Peter –Julius shot Peter – Peter was hit).
In this section, the two types of schemes will be
discussed. First, argumentation schemes will be discussed
and it will be shown how researchers in AI have used andformally modelled them and then story schemes will be
introduced.

Argumentation schemes
The first kind of commonsense knowledge that is neededin our hybrid approach to reasoning with evidence
concerns the inferential generalizations underlying
reasoning steps. In recent research on informal andcomputational argumentation theory, these are referred toas argumentation schemes (Walton 1996) Argumentation
schemes can be thought of as a semi-formal generalizationof the rules of inference found in formal logic. Schemes
can be abstract or specific. For example, the standard
Modus Ponens inference rule can be seen as an abstract
argumentation scheme. An example of a specificargumentation scheme is that for Argument from Witness
Testimony (Bex et al. 2003):
Witness W is in a position to know whether A is true or not.
Witness W asserts that A is true (false).

Therefore, A may plausibly be taken to be true (false).
For example, from the evidence that Nicole, who was there
when Peter got shot, asserts it was Julius who shot him it
can be inferred that Julius shot Peter.
Prakken (2010, see also Verheij 2003 and Bex 2009c on
abstract and contextual inference rules) has argued that inlogics for argumentation, a scheme can essentially bemodelled in two ways, namely as a conditional premise
and as a metalinguistic rule of inference; in the first case,an additional (defeasible) modus ponens inference rule isneeded to infer the conclusion from the minor and major(conditional) premise. Verheij (2009) distinguishes threestatements which are important here, viz.
 Witnesses can usually be believed
 If witness w asserts that a is true/false then a is true/false If witness Nicole asserts that Julius shot Peter thenJulius shot Peter.
The first is the ordinary language expression of a scheme, ageneralization. The second is a conditional scheme, arewrite of the first statement which is necessary if we want
to use a scheme of the first type in an argumentation logic.
The third is an instantiation of the second rule. Now, therelation between the natural-language expression and theformal rule can be rendered as in figure 4.
Julius shot Peter
Witnesses canusually be believed
Witness Nicole saysthat Julius shot Peter

Figure 4: an argument with its scheme
expressed as a generalization
Note how the inferential relation in the argument is linked
to the scheme as expressed by the generalization (theinstantiation-step is not shown in the figure). This picture
shows that there is a strong analogy between
argumentation schemes and Toulmin‟s (1958) notion of
warrants, as figure 4 bears a strong resemblance Toulmin‟sargument diagrams.
In addition to the generalization expressed by anargumentation scheme, each scheme also has associatedcritical questions, typical sources of doubt for thegeneralization. For the Witness Testimony scheme, thesecan be the following:
1. Did w really perceive a?
2. Does w accurately remember a?
3. Does w have a reason for lying?
4. Did w really assert a?
5. Is a consistent with what other witnesses say?

These critical questions give pointers on how and where an
Argument from Witness Testimony might be attacked. For
example, the fifth question points to a possible rebuttal of a(i.e. not a). But not all critical questions are related to
rebuttals. For instance, the fourth question raises doubt
about one of the premises of the scheme. Thus, the criticalquestions fulfil various roles.
In 1996, Walton‟s focus was not on computational
applications of his argumentation schemes. As a result, theuse of argumentation schemes cannot always be modelled
as single, one-step inferences. For instance, some are small
chains of inference steps or small dialogues. The idea tofurther systematize argumentation schemes has been taken
up in the field of computational argumentation. Recently,
Walton and colleagues (2008) gave an updated collectionof argumentation schemes. Bex and colleagues (2003) havereconstructed Wigmore‟s evidence charts usingargumentation schemes. Verheij (2003) has proposed asystematic specification of argumentation schemes inspiredby knowledge engineering technology and Atkinson andcolleagues (2006) have modelled arguments based on aparticular scheme (that for Practical Reasoning) and its
critical questions in an abstract formal argumentation
framework.
Argumentation schemes have also been used for morepractical applications. Argumentation schemes have been
incorporated in the Araucaria argument diagramming tool
(Reed and Rowe 2004) and its online variant OVA.1
Furthermore, Moens and colleagues (2007) have usedargumentation schemes for the automatic detection ofarguments in legal texts.

Story schemes
In the 1970‟s, the fields of cognitive science and artificial
intelligence also took an active interest in stories. This
research mainly focused on developing formal grammars
for describing the structure of a typical story (e.g.
Rumelhart 1975). These story grammars divide stories into
episodes, which consist of a beginning, development andconsequences. In later research, (Schank and Abelson
1977), more specific story-patterns called scripts or
explanation patterns are given, which help in story
understanding. For example, the „restaurant-script‟, whichcontains information about the standard sequence(s) of
events that take place when somebody goes to dine in a
restaurant, helps us understand a simple story about
someone who goes to dine in a restaurant because it fills
the gaps, the events which are not explicitly mentioned in
the story (e.g. the person reading the menu). Scripts andexplanation patterns can also be used to explain an event(Schank 1986), as they connect the event with an
explanation that has been used to explain the event before.
Episodes, scripts and explanation patterns can be seen as
instances of something which we call story schemes,
general patterns of events that can serve as a background to1

free to use at http://ova.computing.dundee.ac.uk/

particular stories. Like generalizations and argumentation
schemes, story schemes range from abstract to specific. For
example, a scheme beginning – middle – end is a very
abstract scheme for stories. Pennington and Hastie‟s (1993)
episode scheme for intentional actions, a causal pattern of
the form motive  goal  action  consequences, ismore specific. Even more specific schemes mention not
only a sequence of events, bit also other information
important in a story of that particular type. Take, forexample, the story scheme for „murder‟:1. Anomaly that the scheme explains: person y is dead.
2. Central action of the scheme: person x kills person y.
3. Other relevant information: the motive m, the time of
the killing t, the place of the killing p, the weapon w.
4. Pattern of actions: person x has a motive m to kill
person y  person x kills person y (at time t) (at placep) (with weapon w)  person y is dead.
5. More specific kinds of murder: assassination (e.g.
liquidation), felony murder (e.g. robbery murder),killing of one’s spouse.
In addition to a pattern of actions, this scheme also
contains other information, such as what the central action
is and which other, more specific schemes might beapplicable.
Story schemes are hierarchical in that specific schemes
can be seen as instances of abstract schemes if the elementsof the specific story scheme correspond to elements of themore abstract story scheme. For example, a murder is an
instance of an intentional action: motive from theintentional action scheme corresponds to person x has amotive m to kill person y from the murder scheme and thecausal link action  consequence from the intentional
action scheme corresponds to person x kills person y 
person y is dead from the murder scheme. Because theelements of the scheme are often abstract, it is also
possible for a single element of a scheme to correspond toa whole sub-scheme. For example, the motive of an
intentional action may be another intentional action with its
own motive. More specifically, in a “revenge murder”
scheme the motive m corresponds to a general murder
scheme. Thus, stories that match a scheme higher up in thehierarchy can be composed of sub-stories that correspondto other schemes lower in the hierarchy, as was alreadyshown in figure 3.
An argumentation scheme denotes a single (inferential)
relation between two propositions. A story scheme denotes
multiple temporal and causal relations between
propositions (expressing events or event types). Thismeans that in principle, argumentation schemes and story
schemes can be modelled in a similar way. Recall that
there are two ways to represent a general argumentation
scheme: in ordinary language and more formally, as aschematic conditional (the first and second statement in theprevious section). Similarly, the murder scheme can bephrased in ordinary language: „Sometimes, a person‟smotive to kill might actually induce him to kill anotherperson, ending the other person‟s life‟. The variables instory schemes can be instantiated in the same way as thevariables a general argumentation scheme can beinstantiated. Hence, a particular story and its corresponding
scheme can be rendered in the same way as a particularargument and its corresponding scheme. As an example,
see figure 5. Here, the original story is rendered at thebottom; this story matches a sub-scheme of the murder
scheme in which the motive m is a fight and the weapon w
a gun. Through the hierarchical structure of schemes, thisoriginal story corresponds to a story that matches the
Murder scheme. The causal relations in the Murder
Scheme are expressed in natural language in the same way
as the inferential relation in the argument in figure 4 isexpressed in natural language.
Sometimes, a person’s
motive induces him to kill..

...to kill, ending theother person’s life

Murderstory

Julius had amotive to kill
Peter

JuliuskilledPeterPeter isdead

Originalstory

Julius andPeter were
having a fightJulius
shot
Peter

Peter
dies

Figure 5: two versions of a story corresponding to aschemeStory schemes can be used in different ways inreasoning about evidential decision making and crime
investigation. In an investigation context, story schemes
are important in that they serve as possible templates for
hypotheses about what happened and they can guide thesearch for further evidence. In a murder case, for example,
one would look for evidence of the type of weapon used or
one could interrogate the suspect on his motives.
Furthermore, story schemes can also be used to criticallyanalyse stories: if a story does not match a particular
scheme (for example, the motive is missing in a murderstory or the causal link between action and supposed
consequence is missing) it‟s quality diminishes (see list of
criteria for a story in the section on the hybrid theory).
One issue which might be explored further is that ofcritical questions for story schemes, which would point totypical sources of doubt for stories that match a particular
scheme. Bex and colleagues (2009b) mention a list of such
critical questions for the intentional action scheme (e.g. „isthere another motivation which is a deterrent for doing theaction?‟ or „can the action have the stated consequences?‟).

opinion the multiple theoretical and practical uses ofargument schemes in computational argumentation show
the potential of such logical schemes for commonsense
reasoning.
We think that what has happened for inferential
generalizations by the rise of research on argumentation
schemes is also a possible direction for stories. In
particular, we consider the following issues to be urgent
topics of research:
1.
2.
3.
4.

5.

Repositories of argumentation and story schemes.
Which argumentation schemes and story schemes are
relevant in reasoning with evidence?
Embedding in software support systems. Can sets ofargumentation and story schemes form the backboneof useful software support systems?
Case studies. Are real cases usefully analyzable using
the hybrid theory? Are they an appropriate source ofargumentation and story schemes?
Elaboration of the notion of a story scheme. What are
the properties of story schemes that make them usefulin reasoning with evidence? What is the role ofabstraction of stories and story hierarchies? How are
actual facts to be matched to story schemes in a
repository?
Integration of argumentation and stories. Can
argumentation and stories be connected in a more
fundamental way than in the hybrid theory? Can theidea of 'communicating vessels' be fleshed out in a
genuine integrated approach?

Some ideas about these questions have already been
proposed by the current authors and their collaborators.
Bex and colleagues (2003) and Walton (2002) mention anumber of argumentation schemes which can be used
specifically in reasoning with evidence (question 1). Vanden Braak (2010) has already shown that a tool whichallows for “story-mapping” (cf. argument-mapping, Reedet al. 2007) can be of use in police investigations andpolice training (question 2).2 Bex and colleagues (2003)
analyse a part of the famous Sacco and Vanzetti case usingargumentation schemes and Bex (2009c) devotes a largepart of his dissertation on analysing a Dutch murder caseusing the hybrid theory (question 3). Questions 4 and 5
have also been explored in Bex‟ dissertation and in Bex(2009a) and Verheij and Bex (2009), respectively. We
think these first ideas are a fertile ground on which new
research can be built, further exploring the use of stories incomputational theories of complex reasoning.

Conclusions and future research
In this paper, we have shown how stories and arguments
can be used creatively and persuasively in the context ofreasoning with evidence in criminal cases. We have argued
that argumentation schemes and story schemes form themost relevant forms of commonsense knowledge in thecontext of reasoning with evidence. Furthermore, in our2

See also Bex’ recent blog post on
http://blog.law.cornell.edu/voxpop/2010/04/16/argumentmapping-and-storytelling-in-criminal-cases/

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