Sense-making software for crime investigation: how to
combine stories and arguments?
Floris Bex
Faculty of Law, University of Groningen
Susan van den Braak & Herre van Oostendorp
Department of Information and Computing Sciences, Utrecht University
Henry Prakken
Department of Information and Computing Sciences, Utrecht University& Faculty of Law, University of Groningen
Bart Verheij
Department of Artificial Intelligence, University of Groningen
Gerard Vreeswijk
Department of Information and Computing Sciences, Utrecht University
March 5, 2007
Abstract
Sense-making software for crime investigation should be based on a model of reasoning
about evidence that is both natural and rationally well-founded. A formal model is proposedthat combines AI formalisms for abductive inference to the best explanation and for defeasible argumentation. Stories about what might have happened in a case are represented as
causal networks and possible hypotheses can be inferred by abductive reasoning. Links between stories and the available evidence are expressed with evidential generalisations that
express how observations can be inferred from evidential sources with defeasible argumentation. It is argued that this approach unifies two well-known accounts of reasoning about
evidence, namely, anchored narratives theory and new evidence theory. After the reasoning
model is defined, a design is presented for sense-making software that allows crime investigators to visualise their thinking about a case in terms of the reasoning model.

Keywords: crime investigation, sense-making, explanation, stories, abduction, argumentation.

1

Introduction

Crime investigation is a difficult and laborious process, and the costs of mistakes can be high.
Especially in large crime investigations, investigators are faced with a mass of unstructured evidence of which they have to make sense. They have to map out the possible hypotheses about
what happened and assess the potential relevance of the available evidence to each of thesehypotheses. Because of the difficulty of this task and the high costs of mistakes, it is worth investigating how crime investigators could benefit from support tools. It has been suggested thatsoftware could offer such tools by supporting crime investigators in expressing their reasoning
about a case in terms of arguments on the relevance of evidence to the various hypotheses using
common knowledge [26]. With such a programme the user could visualise his reasoning invarious ways (graphs, tables, forms) and explore its consequences. Thus the programme would

1

support investigators in seeing patterns, discovering new relationships or inconsistencies, andin identifying missing evidence. Such software would also facilitate transfer of the case files toothers by increasing the transparency of the files, so that subsequent investigators, prosecutors
and fact finders could gain a quicker and better understanding of the case.
In the current practice of crime investigation and similar fact finding processes, software
for managing and visualising evidence is already being used. Well-known examples are Analyst’s Notebook [2] and HOLMES 2 [1]. In the Netherlands, the BRAINS system has been
experimentally used in crime investigation with some success [25]. However, a limitation ofsuch software is that it does not allow for expressing the reasons why certain pieces of evidence support or attack a certain hypothesis. Thus the ’core business’ of structuring evidence
and assessing the relation between hypotheses and evidence is still, in fact, wholly dependent
on human reasoning, and the structures resulting from such reasoning cannot be recorded andanalysed by the software.
This article reports on an ongoing research project on software that could add these so-called‘sense-making’ capabilities to current evidence management software. The aim of this projectis to develop proof-of-concept software in which a human crime investigator can visualise possible stories about what happened and can link these stories through arguments with available
supporting or attacking evidence. We anticipate that to make such software useful in practice, it
should be combined with systems like HOLMES 2, Analyst’s Notebook and Brains, so that thevisualisations are linked with the documents containing the available evidence and thus makethese documents more manageable. To this end, our software already supports links between
components of the visualisations and pieces of text within source documents. However, a full
integration with current professional document management software is outside the scope of
this project.
In this article we focus on one aspect of our project, namely, the design of the reasoning
model underlying our system should. We will investigate this aspect from both a theoreticaland a software perspective, and we will especially focus on the role of causal information inreasoning about evidence. It has been argued that the only viable manner in which police
investigators can structure the information that they gather is through constructing stories about
what might have happened [18]. The stories should explain the available evidence by explaining
what might have caused the evidence to be available. The same has been argued for triers of fact
and others involved in criminal proceedings [35]. However, others have advocated a method ofbuilding arguments from the available evidence to the hypotheses by applying commonsense
generalisations [3, 26]. In this method causal statements (for instance, the shooting has caused
the appearance of gun powder) are often inverted to evidential statements (gun powder indicatesthat a shooting took place). In this paper we want to investigate to which extent these two
reasoning models (stories and arguments) can be used for our purposes. Very briefly, we willclaim that there are reasons to combine storytelling in the form of abductive inference to thebest explanation with defeasible argumentation for linking the stories to the available evidence.
After having outlined a formal model that combines both forms of reasoning, we will thendescribe our current design of software that visualises reasoning based on this combined model.

2

Current theoretical approaches

In this section we sketch the main current theoretical approaches to modelling legal reasoning
about evidence. To be usable in practice, our software design should be based on concepts thatare actually used in such reasoning, while to improve the quality of crime investigations, thedesign should be based on a rationally founded theory of these concepts. Methods founded on
probability theory (such as probabilistic networks) satisfy the second criterion but arguably not
the first since in the vast majority of legal cases reliable statistics are not available, while humanexperts are often unable or reluctant to provide quantitative estimates. This project therefore2takes its starting point in three important concepts of current legal evidence theory, viz. arguments, generalisations and stories (also called ‘narratives’). Two legal-theoretical approaches
that take these notions seriously are New Evidence Theory and Anchored Narratives Theory.
In Artificial Intelligence (AI) mainly models of abduction and inference to the best explanation
have been applied and sometimes also logics for default-style nonmonotonic reasoning.

2.1

New Evidence Theory and Anchored Narratives Theory

The importance of the concepts of arguments, stories and generalisations has been emphasisedin two recent research strands in legal theory, ‘New Evidence Theory’ (NET, e.g.[3, 26] and‘Anchored Narratives Theory’ (ANT, [35]). Both approaches stress the importance of empiricalgeneralisations in reasoning about evidence but they differ on their precise role. While NET
regards generalisations as the ‘glue’ in evidential arguments from evidence to hypotheses, ANT
regards them as the ‘anchors’ of evidential stories in the available evidence.
NET takes its inspiration in Wigmores [37] charting method, in which alternative argumentsfrom evidence to hypotheses can be graphically displayed. Schum & Tillers take Wigmore’s
method as the basis of their ideas on evidential visualisation software. As observed by [19, 4],Wigmore charts are similar to AI models of argumentation [16, 12, 33, 22], and the notion ofa generalisation (or anchor) is essentially the same as AI’s notion of a default [24]. In consequence, the logical aspects of NET are now well understood. However, the relation between
arguments and stories still needs to be clarified since, although NET acknowledges the importance of stories in fact finding and evidence analysis (see e.g. [30]), it does not incorporate thisin its use of Wigmore charts.
ANT stresses that the only viable way in which crime investigation and judicial proof canproceed is by constructing alternative stories about what happened in a case, by comparing their
quality as stories, and by comparing how well they are “anchored” in commonsense generalisations. A story can be anchored in two ways.1 The first is internal anchoring. Stories atleast contain a sequence of events on a time line and stories become stronger if the connections
between the events it contains are not just temporal but also causal (for example, shooting a gun
causes a sound) or intentional (a man possessing a gun who is assaulted will shoot the attacker).
The second type of anchoring is external anchoring: elements of a story can be anchored in theavailable evidence by sources of information, such as observation, memory or testimony. This
also involves commonsense generalisations. For instance, a witness testimony supports a belief
only by virtue of the common knowledge that witnesses usually tell the truth. Clearly, the general knowledge involved in anchoring stories can have exceptions and therefore anchors must
be critically examined and refined when the facts indicate a possible exception. For instance, if
two witnesses know each other, they may have influenced each other’s testimonies: to discard
this possibility a refined anchor may be needed, such as that if two witnesses agree but did notconfer, they usually tell the truth.
Although ANT is more explicit than NET about the role of stories in reasoning about evidence, it does not give a detailed account of how stories can be connected to the available
evidence. In the present paper we aim to provide such an account in terms of the notion ofargumentation as used in NET. This is motivated by the fact that, although the proponents in
ANT focus on the story-based perspective in their choice both of wording and of research background, several of their central claims have a more argumentative than story-based flavour (cf.
[31, 6]). Especially the role of generalisations (or anchors), exceptions to these generalisations and of the dynamics of developing and refining an analysis of the evidence in a case arecharacteristic for an argumentative approach.
1 This distinction is not explicit in ANT but is made in [5].

3

2.2

Causation in reasoning about evidence

Reasoning with causal information clearly is an important aspect of reasoning about evidence.
Basically, it can take two forms. Using familiar AI terminology, in prediction one observes orassumes a certain event and tries to predict what will happen as a consequence of this event,while in explanation one observes an event or state of affairs and tries to explain how it could
have been caused by other events. Both forms of reasoning are, of course, of prime importancein reasoning about evidence, whether story- or argument-based. Often an attempted proof thata certain crime took place is constructed by saying that an observed fact (the evidence) holds
since something else (the crime) happened which caused it. Such an explanation can then be
tested by predicting what else must have been caused by the crime if it has taken place and by
trying to find evidence concerning the predicted facts. In this paper, we will as far as stories
are concerned, only model explanation and leave its combination with prediction for future occasions. As remarked above, the causation involved in reasoning about evidence may both bephysical (fire causes smoke) or mental (wanting to be rich makes one steal). Clearly, reasoning
with causal information is defeasible in several ways: causal generalisations may have exceptions (striking a match will cause fire except if the match is wet) and observed evidence may
be explained by several alternatives (the grass is wet since it rained or since the sprinkler was
turned on).

2.3

Current approaches in AI

In AI it is well-known that causal knowledge can be represented in two alternative ways, namelyfrom cause to effect (fire causes smoke) or from effect to cause (smoke means fire) (see e.g.
[14, 17]). Generally, a choice is made for one of these two representation methods. For instance, the well-known MYCIN medical expert system [7] expressed empirical associations
between symptoms and diseases from effect to cause (if symptoms then disease) while later,model-based approaches to medical diagnosis such as CASNET [28] represented the relevant
knowledge from cause to effect (if disease then symptom). As for terminology, in this paper wecall cause-to-effect statements causal generalisations and effect-to-cause statements evidential
generalisations. With evidential generalisations explanatory reasoning can be modus-ponens
style: if the antecedent (the symptom) is known, the consequent (the disease) can be inferred.
When thus alternative explanations can be derived, a choice should be made with some priority mechanism: MYCIN used certainty factors for this. With causal generalisations prediction
can also be modus-ponens-style but explanation must be abductive: given the consequent (thesymptom) the antecedent (the disease) is inferred since if true it would imply the symptom bymodus ponens on the causal generalisation. Of course, in the same way alternative explanations may be found. If so, then the best explanation must be determined either by trying tofind evidence concerning predicted facts or (if gathering further evidence is impossible) withsome priority mechanism on abductively inferred causes (such as a probability distribution, asin [17]).
In AI the most popular way to model explanatory reasoning is with causal generalisations
and abduction (see e.g. [13, 17, 11]). Logics for defeasible argumentation and other nonmonotonic logics, which only model modus-ponens-style reasoning, are less popular, presumably
since they require the use of evidential generalisations, which arguably is cognitively less natural [14]. Moreover, these logics abstract from the nature of the relation between antecedentand consequent of a generalisation, which creates the danger of a careless mixing of causal andevidential generalisations. Pearl [14] showed that this may give rise to counterintuitive consequences. For instance, if we know that the smoke machine was on and that turning the smoke
machine on causes smoke, we should not go on to infer from ‘there is smoke’ and the evidential
generalisation that smoke means fire that there is a fire. Pearl proposed his ’c-e-constraints’ fordefault logics, which if respected, prevent such unwanted inferences. In this paper we will allow for mixed reasoning with evidential and causal generalisations in a way that respects these4constraints.

3

Our approach

As observed in the previous section, in AI usually one single approach to explanatory reasoning
is taken. In most approaches all causal knowledge is represented from cause to effect andthe reasoning is abductive, and in some approaches all causal knowledge is represented from
effect to cause and the reasoning is modus-ponens-style. However, we will argue that there arereasons to combine these two approaches. We will propose a model in which stories about what
happened are represented as networks of causal generalisations while the relation between theavailable evidence and events in the causal network is represented as evidential generalisations.
The reasons for this approach are as follows.
A reason not to represent all causal information from effect to cause has to do with the factthat crime investigators very often draw time lines and causal-network-like structures. Since wewant to build software for supporting crime investigators, we want to support this habit. This
explains why for our purposes representing all causal information from effect to cause is lessdesirable.
However, there are also reasons not to represent all causal information from cause to effect.
The first has to do with the treatment of witness testimonies. In an abductive approach as
taken in e.g. [17] and as further explained below in Section 5.2, the relation between a witness
testimony and its content must be represented as causal generalisations, in which the witness
testimony is regarded as caused by something else. One possible cause of a witness testimony
is, of course, the truth of the event to which the witness testifies. Schematically:g1 :p ⇒ w said “p”(To be truly realistic this generalisation should have some auxiliary conditions like “the witness
was interrogated” and so on, but for simplicity we will leave such conditions implicit.) However,there may be other possible causes of the witness testimony. for instance:
g2 :
g3 :

w hallucinated and thought he saw p ⇒ w said “p”
w has reason to lie about p ⇒ w said “p”

As regards g3 , one reason to lie about p could be that the witness wants to protect the offender,another reason could be that by speaking the truth he would compromise himself (such as when
speaking the truth would reveal a visit to the red light district), and so on.
However, this creates the following problem. In a causal theory with the above three generalisations, the observation that w said “p” can be explained in three ways: it can be explainedin the ‘normal’ way by supposing that the witness speaks the truth, i.e. by p, but it can also
be explained by supposing that w hallucinated and thought he saw p and by supposingthat w has reason to lie aboutp. Therefore, in the approach applied here some measure ofstrength is needed to discriminate between them, or if further investigation is possible, further
evidence should be gathered to discriminate between the two explanations. However, intuitively
it seems that in the absence of further evidence for these alternative explanations, they are not
worth considering. They are exceptions to the general default statement that witnesses usually
speak the truth and they should therefore be assumed false as long as there is no evidence to thecontrary.
Thagard [29] speaks in this connection of a “dual pathway model” of reasoning with testimonial evidence. He distinguishes a “default pathway” in which people almost automatically
accept a testimony and a “reflective pathway” in which people build a model of the relevant
knowledge and decide whether to believe the testimony by inference to the best explanation(IBE). People shift from the default to the reflective pathway when the content of the testimony is inconsistent with their current beliefs or when there is reason to doubt the credibilityof the source. The problem with a ’cause-to-effect’ approach as described here is that it forces5crime investigators to always take the reflective pathway, since it forces to consider all alternative explanations, even if they are not supported by any further evidence. Clearly, this will inmany cases induce unnecessary cognitive overload. More precisely, by leaving the evidential
generalisationg4 :w said “p” ⇒ p

out of the problem description, this approach fails to capture that witness testimonies are usuallytrue . Statements like g4 express the empirical regularity that the usual cause of a testimony is
the truth of its content and that any other cause is an exceptional cause.
(It should be noted that our criticism directly applies only to the logical models of abduction
as described below. It may be argued that Thagard’s own connectionist model of IBE does not
suffer from this problem since his connections between statements have no direction.)
For these reasons we propose a mixed approach: while the construction of stories to explain
the available evidence is modelled as abductive reasoning with networks of causal generalisations, source-based reasoning about evidence is modelled as modus-ponens-style reasoning
with evidential generalisations. Since these two aspects will be clearly separated, our proposal
will respect Pearl’s above-mentioned c-e constraints. Sources of evidence include, of course,
testimonies but also, for instance, memory and perception. As for testimonies, a shift from thedefault to the reflective pathway will in our approach be modelled as the construction of counterarguments based on exceptions to the evidential generalisation triggered by available evidence.
The crucial difference with the above purely abductive approach to reasoning with testimonies
is that in our argumentation approach such counterarguments can only be constructed if there isevidence for a possible exceptional cause; in the criticised approach such an exceptional cause
also counts as a possible explanation without further evidence.

4

An example

Let us illustrate our approach with an example discussed in [9, pp. 460–463], a relatively simple
case about an alleged burglary. (We take our analysis of the example from [5]). The prosecution
presents the following story.
On the 18th of November, Andrew King climbs over the fence of the backyardof the Zomerdijk family with the intention to look if there is something interestingfor him in the family’s house. Through this yard he walks to the door that offers
entry into the bedroom of the 5-year-old son of the family. The door is not closed,
so King opens it and enters the bedroom to see if there is anything of interest in
the house. Because it is dark, King does not see the toy lying on the floor. Kinghits the toy, causing it to make a sound which causes the dog to give tongue. King
hears the dog and runs outside, closing the door behind him. Mr. Zomerdijk hears
the toy and the dog. He goes to the bedroom and sees King running away throughthe closed garden door. He shouts “there is a burglar, come and help me!” andruns into the garden after King. King, who wants to pretend he is lost, does not run
away. In spite of this, Zomerdijk jumps on King and, aided by his brother, who isvisiting the Zomerdijk family, molests King.
The prosecution’s story is depicted in the following figure, together with the witness testimonies
on which some of the elements of the story are based.
The part of the figure within the large rounded box represents the causal network corresponding to the prosecution’s story. The four small grey boxes outside the causal network arepieces of testimonial evidence. With the evidential generalisation ’a witness usually speaks thetruth’ they can be used to build arguments to support nodes inside the causal network. In thiscase the arguments are very simple, directly supporting a node.

6

Figure 1: The prosecution’s story in the King case
King has his own explanation for the fact that the toy made a sound; he claims that the wind
may have blown open the door, hit the toy (which caused it to make a sound) and then blown
the door shut again. This story predicts a loud bang as the consequence of the fact that the windblew the door shut again. However, the members of the Zomerdijk family reported that they
heard no loud bang.
The two stories can be depicted together in the following figure (where part of the prosecution’s story is left implicit for readability):

Figure 2: The prosecution’s and defence’s stories in the King case
Besides new causal links within the large rounded box (capturing King’s story) also a newargument from witness testimonies is added on the right. This argument in fact concludes to thenegation of a network node, namely, that there was no loud bang. Note that so far none of theevidential arguments has a counterargument; below we will add such counterarguments.
Recall that we want to model how the evidence can be explained. In our approach the
explanation task is to explain the positive facts supported by evidential arguments while notcontradicting the negative fact supported by evidential arguments. (When evidential arguments
have counterarguments this is restricted to those facts that are supported by arguments thatsurvive the competition). Let us say we are interested in explaining why witnesses w1 andw2 grabbed (i.e., molested) King. If we focus on minimal sets of causes, then the following
explanations correspond to the stories of the prosecution, respectively, King:H1 = {King has bad intentions, others in living room}
H2 = {wind opens door}
Hypothesis H1 explains all the observations that we made while H2 does not explain that King
climbed into the backyard. Also, H2 contradicts the observation that there was no loud bang.
Intuitively, this clearly makes H1 the better explanation.
The reader may find some of the causal or evidential generalisations in this example weak or
far-fetched. However, this is not a problem for our approach. The very idea of our sense-making
system (which it shares with, for example, Wigmore’s charting method) is that it is the user ofthe system who is responsible for carefully testing the quality of his stories and arguments.
7

The software should support the user in this critical process; it should not itself automaticallygenerate sensible stories and arguments. Ideally, the software should also inform the user about
the dangers involved in relying on stories and generalisations (cf. [30]). How this can be done
is part of our research project but falls outside the scope of the present paper.

5

The formalisms

In this section we outline our combined formal theory.

5.1

Generalisations

General knowledge is in our approach expressed with two sets GC and GE of causal and evidential generalisations. Logically, we formalise both types of generalisations in the same way,with a special conditional connective ⇒ which only satisfies the modus ponens inference rule:g: p1 & . . . & pn ⇒ q
Here c1 . . . cn and q are predicate-logic literals and g, the name of the generalisation, is a
first-order term. Below we will indicate whether a generalisation is causal or evidential with
subscripts, writing ⇒C and ⇒E .2

5.2

An abductive framework

We now specify the notion of an abductive framework. In AI many formal and computational
accounts of abduction are available. The following simple account captures their essence andsuffices for present purposes.
An abductive framework is a tuple AC = (GC , O, F, X) were the elements of the tuple aredefined as follows.
• GC , the causal theory, is a set of causal generalisations.
• O, the observations, is a set of ground first-order literals. (We will explain below why O
does not have to be consistent.)• F ⊆ O, the explananda, is a consistent set of first-order literals. They are the observations
which have to be explained (as further explained below, the observations not in F do notstrictly have to be explained but explaining them does make an explanation better).
• X, the explanantia, is the set of all ground literals occurring in the antecedent of somecausal generalisation in GC and instantiated with some term in GC ∪ O.
Now an explanation in terms of AC is a set H 0 ⊆ X of hypotheses such that for each f ∈ F it
holds that• H ` f ; and• H 6` ⊥.
Here ` stands for logical implication according to the set of all deductive inference rules extended with modus ponens for ⇒. In AI it is usually also required that a hypothesis explainsor is at least consistent with all observations in O but these conditions are unrealistic if the correctness and completeness of the causal theory is not guaranteed, (cf. [21]), as is often the casein legal cases.
2 As usual in AI, a conditional expressed with ⇒ which contains variables is a scheme for all its ground instances.

8

When F can be explained in more than one way, the alternative explanations have to becompared. Sometimes, e.g. in [17], this is done in terms of probability distributions over H.
However, in our purely qualitative approach this method is, of course, not applicable. For now
we work with the following simple ordering on explanations. (As not uncommon in AI, we will
only consider subset-minimal sets of hypotheses.) For any explanation H the sets Hp and Hncontain the observations in O that are explained, respectively, contradicted by H.
• If Hp ⊂ Hp0 and Hn ⊇ Hn0 then H < H 0 .
• If Hp ⊆ Hp0 and Hn ⊃ Hn0 then H < H 0 .
• If Hp = Hp0 and Hn = Hn0 then H ≈ H 0 .
• In all other cases H and H 0 are incomparable.
In words, if H 0 is better than H on the observations explained and not worse on the observations contradicted, or if H 0 is better on the observations contradicted and not worse on theobservations explained, then H 0 is better than H. If they are equal on both criteria, then they
are equally good overall. In all other cases they are incomparable.
Below we will see that combining abduction with argumentation allows a refinement of
this preference relation. In future research we want to investigate whether the criteria can befurther refined with content-based criteria from, for instance, the legal-psychological literatureon storytelling, e.g. [15, 35].
Let us illustrate these definitions with our King example. The set GC consists all causal
generalisations corresponding to a link withn in the large rounded box in Figure 4. To be brief
and for simplicity, we give just five of them and make some of the literals propositional):
g1
g2
g3
g4
g5

x has bad intentions ⇒C x climbs into backyard
x has bad intentions & x climbs into backyard &others in living room ⇒C x opens door
x steps on toy ⇒C toy soundtoy sound ⇒C x hears toy soundwind closes door ⇒C loud bang

The set O contains all nodes that are supported by witness statements:
O={
King climbs into backyard,
w1 is in living room,
others in living room,
w1 hears toy sound,
door is closed,
w1 goes to bedroom,
w1 sees King in backyard,
w1 runs after King,
w1 and w2 grab King,¬ loud bang}
The set F of observations that have to be explained consists of just w1 and w2 grab King.
The explanantia X consist of all ground literals that can be formed from any node with an
outgoing causal link and instantiated with King, w1 or w2 . To be brief, we do not list the set X.
Now it is easy to see that the two explanations listed in Section 4 can be formally generated
by the following hypotheses:
H1 = {King has bad intentions, others in living room}
H2 = {wind opens door}
9

To make our earlier observations about their relative strength formal, we have:
H1p =

H2p =

{King climbs into backyard, w1 hears toy sound,
door is closed, w1 goes to bedroom, w1 sees King in backyard,
w1 runs after King, w1 and w2 grab King}{ w1 hears toy sound, door is closed,
w1 goes to bedroom, w1 sees King in backyard,
w1 runs after King, w1 and w2 grab King}so H2p ⊂ H1p . Also, we have:
H1n =
H2n =

∅
{¬ loud bang}so H1n ⊂ H2n . Then we have that H1 > H2 , so our formal preference relation captures ourintuitions of Section 4 that H1 is the better explanation.

5.3

A logic for defeasible argumentation

We now specify the logic for reasoning with evidential generalisations. Since such generalisations allow for exceptions, this logic must be nonmonotonic. Following our earlier workin [4, 19], its design is very much inspired by Pollock’s OSCAR system [16]. In this system
the inference rules of classical logic are extended with defeasible inference rules that give rise
to “prima facie reasons”. Reasoning proceeds by constructing, attacking and comparing arguments. Arguments can be constructed by chaining deductive and/or defeasible inference rules
into trees. The leaves of such a tree are the input information (in our case the evidence) andthe root is its conclusion. A special kind of inference rule is an undercutter, which says that incertain circumstances some defeasible inference rule does not apply. Accordingly, defeasible
arguments can be defeated in two ways: they can be rebutted with an argument for the opposite
conclusion and they can be undercut with an argument that applies the relevant undercutting
reason. Arguments can be directly defeated on their final conclusion or inference step but also
indirectly on intermediate ones. If appropriate, rebutting conflicts between arguments can be
adjudicated with a suitable preference relation on arguments, possibly defined in terms of apreference relation on its premises.
In our case the classical inference rules are those of standard first-order logic while the only
defeasible inference rule is the modus ponens rule for the ⇒ connective. Undercutters to thisdefeasible version of modus ponens are formalised as arguments for the conclusion ¬ valid(g)where g is the name of the generalisation to which modus ponens is applied.3
Summarising, we have:An evidential framework is a tuple AE = (GE , E), where• GE is a set of evidential generalisations;• E, a consistent set of literals, contains the evidence.
An evidential framework induces an argumentation framework in the sense of [10], i.e., a set
Args of arguments ordered by a binary relation of defeat. We say that an argument A defeatsan argument B if A rebuts or undercuts either B itself or one of its subarguments and (in caseof rebutting) B is not preferred over A. The dialectical status of the arguments in Arg can
3 The condition valid(g) is included to support reasoning about whether a generalisation is acceptable at all. Strictly
speaking, g is a function expression g(t1 , . . . , tn ) where t1 , . . . , tn are all terms occurring in a generalisation. Thusa generalisation can be invalidated for only some of its ground instances, since the general name of a generalisation
could be g(x) (for variable x) while a statement ¬ valid(g(a)) asserts its invalidity for a specific individual a. Below
we will leave such subtleties implicit and write names of generalisations as constants. Also, for simplicity we make no
distinction between ‘full’ invalidity (inapplicable in all cases) and ‘partial’ invalidity (inapplicable in only some cases).
In [6] this distinction is made, by distinguishing between the validity of and exceptions to a generalisation.

10

be defined in various ways [10]. For present purposes their differences do not matter so we
confine ourselves to an intuitive sketch. An argument is justified if it survives the competition
with all its counterarguments, it is overruled if it is defeated by a justified argument and it isdefensible otherwise, i.e., if at least one conflict with a counterargument cannot be resolved. It is
important to note that the dialectical status of an argument depends on the interactions between
all arguments in Arg: although arguments can make themselves justified by defeating all theircounterarguments, sometimes they do not, in case they need the help of other arguments, which
reinstate them by defeating their defeaters. Suppose, for instance, that we generally preferpolice witness statements over statements of other witnesses. Then if A is based on a police
statement and B on another witness, we have that A defeats B but B may still be justified bya finding a reinstater. For example, if we find out that the policeman lied, this gives rise to anargument C that defeats A and so reinstates B.
As for the content of GE , we are inspired by the notion of an argument scheme from argumentation theory (see e.g. [36]). Such schemes express stereotypical patterns of defeasible
reasoning. Their defeasibility is captured by a list of critical questions, which all have to beanswered before the scheme can be applied. In [4] it was argued that argument schemes can beformalised as defeasible inference rules and that negative answers to critical questions correspond to undercutters of such rules (see also [32, 19]). In [4, 19] it was argued that for reasoning
about evidence a combination of the witness testimony scheme (adapted from [36]) and the perception, memory and temporal-persistence schemes of [16] comes close to David Schum’s [27]well-known decomposition of witness credibility into the aspects of veracity, objectivity andobservational sensitivity. In this paper we adopt the approach of [4] but we slightly change its
formalisation: instead of formalising the argument schemes as particular defeasible inference
rules, we assume that they are present in GE as generalisations and that they are applied withthe modus-ponens inference rule for the → connective. Note, finally, that GE may also containknowledge that is relevant for making inferences from the evidence or for assessing the validityof causal generalisations in GC .

5.4

The combination

We now describe the combination of the above abductive and evidential frameworks. The mainidea is that evidential argumentation influences story-based explanation in two ways. It influences the content of the set GC of causal generalisations with arguments about the validity of
causal generalisations, and it influences the content of the set O of observations with evidential
arguments about whether the observations can be derived from the evidential sources. In thispaper we illustrate both types of influences with an example. In [6] the insights drawn fromthese examples are fully formalised.
To illustrate the first situation, let us assume that the prosecution wants to strengthen its
story by adding a causal statement
g6 :x was broke & x uses drugs ⇒C x has bad intentions

Suppose the police testified that King uses drugs. Then in explanation H1 the assumption Kinghas bad intentions can be replaced by King was broke. This arguably makes H1 moreconvincing since the new assumption can be evidentially supported by documentary evidence.
However, suppose that GE contains the generalisations
g7 :
g8 :

g is based on prejudice ⇒E invalid(g)g looks at drug use & suspect x is fromethnic minority ⇒E g is based on prejudiceand suppose that E contains
e1 :
e2 :

g6 looks at drug use
suspect King is from ethnic minority.
11Then on the basis of AE the following argument can be constructed.
1.
2.
3.
4.

g6 looks at drug use
suspect King is from ethnic minority
g6 is based on prejudice¬ valid(g6 )(from E)
(from E)
(from 1,2 with g8 )(from 4 with g7 )Suppose for the moment that this argument has no defeaters. Then clearly our new version of
H1 should not count as an explanation, since it uses a generalisation that is declared invalid
by a justified argument. So the prosecution should either return to its original version of H1
or find another causal generalisation for explaining King’s bad intentions. However, things canbe more subtle since the just-given argument may have defeaters and may be only defensible.
(We leave it to the reader to think of plausible counterarguments.) In that case it seems that
explanation H1 is still constructible but has lost some of its quality.
Let us now consider disputes about witness credibility, affecting the content of the observations O. Suppose GE contains the following generalisation, formalising the witness testimony
scheme for any combination of n witnesses:4
g9 :witness w1 said “p” & . . . & witness wn said “p” ⇒E p

The argument that gave rise to the observation ¬ loud bang then looks as follows.
1.
2.
3.
4.

witness w1 said “no loudbang”
witness w2 said “no loudbang”
witness w3 said “no loudbang”¬ loud bang

(from E)
(from E)
(from E)
(from 1,2,3 with g9 )Without defeaters this argument is justified so its conclusion can safely be included in O. However, if this observation has no justified arguments, things are different. If it only has overruled
arguments, it should not be in O, while if it at best has a defensible argument, it can be includedin O but explaining it is less important than explaining a justified observation.
We now briefly illustrate this with the following generalisations in GE .
g10 :
g11 :

¬ witness w is truthful ⇒E ¬ valid(g9 )witness w has lied before ⇒E ¬ witness w is truthful

Clearly, if we add to E that
e3 :
e4 :
e5 :

witness w1 has lied before
witness w2 has lied before
witness w3 has lied before(for simplicity we leave the sources of these facts implicit) then for all witnesses an argumentthat they are not truthful can be constructed, so the argument that there was no loud bang isundercut (as well as the versions of g9 for one and two witnesses). In that case, this conclusion
only has overruled arguments and should not be included in O.
However, suppose that the undercutting arguments can be in turn attacked, and that in theend they remain defensible. Then the conclusion ¬ loud bang can be added to O but it becomesless important that they are explained and less serious that they are contradicted.
To conclude this section, the examples in this section were deliberately simple for ease of
explanation, which might have made them rather simplistic. It should be noted that in [4, 19, 20]
a number of hopefully more realistic examples of argumentation about witness testimonies are
discussed.
4 For accrual of evidence in argumentation see [20].

126Building sense-making software

As said in the introduction, the ultimate goal of the research project on which this paper reports
is to design (proof of concept) sense-making software for crime investigation. In this section
we briefly describe the visualisation component of this system as far as it has been designed
so far and we illustrate it with the King case. The system (called AVERs) is implemented as a
web front-end to an SQL database. A case can be represented visually through multiple views;
in this paper we will focus on the two graphical views, that is, the evidence view and the story
view. The supplementary views (including a node view, a report view, and an argument view)
are, among other things, useful for reporting purposes. The software design makes extensive useof colours, which naturally cannot be shown in this paper. Therefore, colour indications have insome cases been added to the screenshots by hand. A version with colour pictures has been put
online at http : //www.cs.uu.nl/research/projects/evidence/publications/lpr07submitted.pdf.
It should also be noted that the visualisation methods described here are meant to be usedin software and therefore some pictures may be less readable when displayed in print.

6.1

The evidence tab: drawing arguments and causal networks

The evidence tab (see Figure 3) can be used to draw evidential arguments and causal networks.
It consists of a split screen where the upper half displays a global overview of the case (thecombined argument and story graph containing nodes and links) and the lower half displays theattributes of a node that is selected by the user in the upper half of the screen. New nodes can be
added to the screen by clicking the desired node type. Two nodes can be connected by drawing
lines from node to node. If a node is clicked in the upper half of the screen, its attributes can beedited in the lower half of the screen. Thus, a case is built.

Figure 3: Screenshot of AVERs’ evidence tab
Square-boxed nodes represent claims about a case and may be connected by directed links
to represent inferential relations between claims. To link claims to the real world, some ofthem are coupled to external source documents from which text is selected. Such selections are
made in much the same way as text in hard copy source documents is marked with highlighting
marker pencils.
Nodes can be of three different types and three different polarities (see Table 1 for an
overview and Figure 4 for an example). More precisely, nodes can be of the data, inference, or
scheme type, and they can either be positive, negative, or neutral. Data nodes, represented as
square boxes, can in turn be of two subtypes: interpretation nodes, either positive or negative,
represent contestable claims while quotation nodes represent quotes from textual sources. Inference nodes, depicted as small ellipses, are justifications for inferential links. This distinction
between data nodes and inference nodes is founded on the Argument Interchange Format [8].
Finally, scheme nodes represent argument schemes and justifications for inference nodes thatare not supported by other inferences. Scheme nodes are depicted as blue ellipses. Currently,
AVERs contains the argumentation schemes present in Araucaria [23]. Moreover, users are able
to modify schemes and add their own schemes.

13Type / Polarity
Positive(green)Negative (red)Neutral (blue)

Table 1: Node types
Data (box)Inference and Scheme
Interpretation node
Inference node
Claim PRO main thesis
Inference from positive nodesInterpretation node
Inference node
Claim CON main thesis
Inference from negative nodes
Quotation node
Scheme node
Quote from source document
Multiple witness
testimonies [blue scheme]

W1 [blue quote]W1 testimony

W2 [blue quote]W2 testimony

W3 [blue quote]lied before [green claim]
W1 has lied
beforenot truthful [green claim][green inference]W3 testimony[darkgreen inference]no bang [darkgreen observation]
No loud bang

W1 is nottruthful[green undercutter]lied before [green claim]
W2 has lied
beforenot truthful [green claim][green inference]lied before [green claim]
W3 has lied
before[green inference]

W2 is nottruthful[green undercutter]

not truthful [green claim][green undercutter]W3 is nottruthful

Figure 4: An argument with counterarguments in AVERs

Given these node types, inferences can be expressed by connecting two or more nodes
with inferential links, based either on causal or on evidential relations between states of affair.
Evidential links are black with triangle-shaped arrowheads while causal links are yellow andhave diamond-shaped arrowheads (see Figure 5). The polarities of linked nodes determine
the type of the link between them: two nodes of the same polarity support each other while
two nodes of opposing polarities attack each other (the polarity of a node is indicated by its
colour). Nodes can be attacked in two different ways. A rebuttal attacks a data node, while
an undercutter attacks another inference node. Figure 4 contains an argument with conclusion
noloudbang attacked by three undercutting attacks. A rebutting attack of the same argument
could be drawn by creating a node loud bang and linking it via a rebut ellipse to the node
no loud bang.
Generally, a case is built as follows (see the abstract example in Figure 5). Starting fromthe evidence, for instance, a quote from a witness testimony that P occurred, a user may use
the witness testimony scheme in order to infer P . While doing that, the user has to take thecritical questions that are attached to this scheme into account. A negative answer to any ofthese invalidates the instantiation of the scheme and is therefore added as an undercutter, while
a positive answer will result in a latent undercutter (as displayed in Figure 5 with the ellipse
containing ‘[lightgray CQ]’). The user may then use causal knowledge to connect the observed
fact P to another fact, say Q, and Q to another fact R. Thus a story is built. To complete astory, the user may also add events or facts that are not yet supported by evidence. If evidence
for such a proposition is found, the user may use an evidential argumentation scheme in order toconnect it to evidence, since schemes can expand nodes not only bottom up but also top-down.
In other words, a certain node can be marked as being (one of) the premise(s) of a scheme, but

14

Witness Testimony[blue scheme]

W [blue quote](Premise: Witness
W says P)

Q [darkgreen observation]

P [green observation][green inference]causes(Conclusion: P) (Premise:In this case, P occurs)(Conclusion: In this case, Q will
occur) (Premise: In thiscase, Q occurs)

causesR [darkgreen observation](Conclusion: In thiscase, R will occur)[lightgray CQ]

Figure 5: A simple story in AVERs
bad intentions [green claim]
King has bad
intentions

causesclimb backyard [green observation]
King climbs into
backyardcausesenter house [darkgreen observation]
King entershousecausesgrab King [darkgreen observation]
w1 and w2 grab
King

Figure 6: King case: preliminary story
also as the conclusion. In the first case the system automatically adds the conclusion of thescheme (together with the inferential link and the corresponding scheme node), while in thelatter case it automatically adds the premise(s). Moreover, the system provides functionality todefine links as being instantiations of a certain scheme. If two nodes are connected by dragging
the mouse, the chosen scheme is added automatically.
Based on inferential connections AVERs is able to evaluate the dialectical status of nodes,that is, whether they are supported by a justified argument or not, based on algorithms describedin [34]. For example, in Figure 4 the node no loud bang is defeated by three undercutters. In
the software both this node and the inference node on which it is based will therefore be coloured
red.

6.2

The story tab: investigating explanations

The story tab displays a selection of the nodes and links that were already added by the user
using the evidence tab. Put more precisely, it displays a graph that contains all and only those
nodes that are part of a causal network, that is, nodes that are linked to one or more othernodes through causal connections. The nodes that are part of the set O, which are those thatare supported by a justified or defensible argument, are displayed in a different colour, and thenodes in F are in addition displayed as encapsulated boxes.
The support the system should provide to the user in the story tab is twofold (currently
these features have not yet been implemented). Firstly, it should be able to determine, given
a set H of nodes marked by the user as hypotheses and the set of causal generalisations Gc
drawn by the user, which of the observations in O are explained by H ∪ Gc . When applicable
it should also tell which overruled facts are explained. Secondly, and reasoning the other way
around, the system should be able to infer the possible explanations for the observed facts Othat satisfy certain criteria. For instance, it could be told to infer minimal explanations of F that
only consist of initial causes, or it could be told to infer a minimal connected part of the causal
network that starts with initial causes and ends with all elements of F .

6.3

The King case visualised in AVERs

We now visualise some aspects of the King case in AVERs. In doing so, we assume that the twostories were gradually built and refined by the police investigators.
Let us suppose that in this example case the investigation started with the observation that
King was molested by Mr. Zomerdijk and his brother in their backyard (see Figure 6, node w1
and w2 grab King). Subsequently, the police might have considered two possible explanations:15

bad intentions [green claim]
King has bad
intentions

causesclimbs backyard [green observation]
King climbs into
backyardcausesothers living room [green claim]causesopen door [darkgreen observation]
King opens
door

causesenter house [darkgreen observation]
King entershousecausesstep toy [darkgreen observation]
King steps on
toycausestoy sound [darkgreen observation]
Toy makes
soundOthers in livingroomhear toy [darkgreen observation]
toy sound [darkgreen observation]
Toy makes
soundcausesgo bedroom [darkgreen observation]
causesW1 hears toy

W1 goes tobedroomcausessee King [darkgreen observation]

causes

W1 sees King inbackyard

run King [darkgreen observation]
W1 runs afterKing

causescausesexit house [darkgreen observation]
King exitshousecausesclose door [darkgreen observation]
King closes
door

causesdoor closed [darkgreen observation]
Door is closed

Figure 7: King case: prosecution’s complete story• King was trying to burgle the Zomerdijks and was caught by Mr. Zomerdijk, who acted
in self-defence, or• the Zomerdijk family saw King in their backyard, because the toy made a sound, andmolested him, apparently for no reason.
The police opted for the first story, supported by the testimonies of the Zomerdijks. So as an
explanation for the fact that King was grabbed by Mr. Zomerdijk in his backyard, they assumed
that King had bad intentions, that is, he wanted to break into the Zomerdijks’ house, and climbed
into their backyard in order to enter the house. Their preliminary story looks like Figure 6.
Assume next that based on testimonies of the Zomerdijk family and the hypothesized events,
the police refined their story. For example, based on their causal assumption that King entering
the house caused him to step on the toy, they added a causal link from King enters house andpredicted the node King steps on toy, which in turn led to the toy making sound, and so on.
The complete prosecution’s story may be represented in AVERs as displayed in Figure 7 (forreadability purposes the causal network is split into two parts at node Toy makes sound).
W1 saw others [blue quote]W1 says that he saw
others in the livingroom

W2 saw others [blue quote]W2 says that he saw
others in the living
room

Others [green observation][green inference]

W3 saw others [blue quote]

There were others in
the living room...

W3 says that she saw
others in the living
room

Multiple witness
testimonies [blue scheme]

Figure 8: King case: observation supported by witness testimonies
We next describe how stories can be anchored in evidence. For this purpose witness testimonies are used. Consider for example, witnesses 1, 2, and 3 who declared that they werein the living room. By selecting text from the original source documents, quotation nodes canbe added to the graph. These evidence nodes can now be connected to the fact in the story by
selecting the witness testimony scheme for multiple witnesses (see Figure 8). Applying argumentation schemes to all testimonies results in an anchored story as displayed in Figure 9 (alsosplit at node Toy makes sound). In this figure all generalisations, inference nodes, and scheme
nodes are hidden for readability purposes. This illustrates how AVERs allows to collapse certain
nodes based on their type or status (these nodes are depicted in smaller form without text) or tohide them completely, so that the user maintains overview of complex graphs.

16

grab King [darkgreen observation]
W1 and W2 grab
KingNow that the prosecution has completed their story, it has to be checked whether the observations can be explained by it. For this purpose the story tab must be used. This tab
displays the prosecution’s story and colours the facts or events that are supported by justified or defensible arguments gray (see Figure 10). These nodes are part of the set of observations O. This set thus contains: King climbs into backyard, Others in livingroom,
w1 hears toy, w1 goes to bedroom, W1 sees King in backyard, w1 runs after King,
w1 and w2 grab King, and Door is closed. The node w1 and w2 grab King is selected
as the only member of F (displayed with encapsulated boxes). The hypothesis H should explain as many observations from O as possible but at least explain w1 and w2 grab King. The
prosecution’s hypothesis (H1 ) is as follows: King had bad intentions and wanted to burgle theZomerdijk family. The others were in the living room, so he had the opportunity to do so. H1thus contains the nodes King has bad intentions and Others in livingroom. After selecting the nodes that are part of the hypothesis (marked by an asterisk in Figure 11), the system
deduces which observations are explained by it and marks them by adding a black box.
As displayed in Figure 11, the prosecution’s hypothesis explains all observations in O andmore. After all, this figure shows how all observations (coloured gray) are surrounded by ablack box.
King offers an alternative story, displayed in Figure 12 (to keep the figure readable somepaths are compressed to single links). However, when taking into account that generally whena door is blown shut, there is a loud bang, the story has to be updated (see Figure 13, which also
contains the relevant argument from witness statements that support King’s story). Unfortunately for King, none of the witnesses heard a loud bang, so assuming that all witnesses told thetruth there was no loud bang. Figure 13 therefore contains an attack link from no loud bang
to loud bang. If we combine the prosecution’s story with King’s explanation, we obtain Figure 14. (Because of space limitations, several causal paths are in this figure compressed tolinks.) The prosecution’s hypothesis still explains all observations, since King’s alternative
did not add any new observations to O. Now, let us determine whether King’s alternative isequally good. King’s hypothesis (H2 ) is that the wind opened the door. H2 thus only contains
Wind opens door (marked by an asterisk). The story tab will now display which observations
are explained (see Figure 15). There are two major problems with King’s story. First of all, it
does not explain why he was in the backyard. This means that King• must provide an alternative explanation, or• has to admit that he had bad intentions.
Secondly, H2 explains that a loud bang must have occurred. However, this conflicts with thewitness testimonies and is therefore defeated by a justified argument. So, using the figures
displayed in the story tab of AVERs (see Figures 11 and 15), it is fairly easy to see that H1 isbetter than H2 , because it explains more (more observations are surrounded by a black box) anddoes not explain defeated propositions (no red observations are marked by a black box).
Let us finally discuss how the two invalidity arguments of Section 5.4 can be added to thesefigures. Figure 16 displays the argument attacking the causal link that was supposedly added by
the prosecution to explain why King had bad intentions, while Figure 4 above in fact contains
the argument attacking the presence of the node no loud bang in the story part of the graphs.

6.4

State of implementation

The AVERs system as described in this section has not yet been fully implemented. Functionality that has been implemented includes the ability to create nodes, the ability to create and apply
inference schemes and the possibility to compute the dialectical status of individual nodes. In
short, the evidence tab as described above has been fully implemented. AVERs currently lacks
functionality to perform explanatory or abductive reasoning. This aspect, that is, the story tab,is currently being developed and implemented.
17

bad intentions [green claim]
King has bad
intentions

climbs backyard [darkgreen observation]

causesKing climbs into
backyard

King [blue quote]others living room [darkgreen observation]

King testimony

Others in living
room

causesopen door [darkgreen observation]

causesKing opens
door

causesenter house [darkgreen observation]
King entershousecausesstep toy [darkgreen observation]
King steps on
toycausestoy sound [darkgreen observation]
Toy makessound

W1 [blue quote]W1 testimony

W2 [blue quote]W2 testimony

W3 [blue quote]W3 testimony
exit house [darkgreen observation]
toy sound [darkgreen observation]

causesToy makes
soundcausesKing exitshousecausesclose door [darkgreen observation]

causesKing closes
door

door closed [darkgreen observation]
Door is closed

hear toy [darkgreen observation]
W1 hears toy

causesgo bedroom [darkgreen observation]
W1 goes tobedroomcausessee King [darkgreen observation]

causes

W1 sees King inbackyard

run King [darkgreen observation]
W1 runs afterKing

causesgrab King [darkgreen observation]
W1 and W2 grabKing

W1 [blue quote]W1 testimony

W2 [blue quote]W2 testimony

Figure 9: King case: prosecution’s story supported by evidence

bad intentions [green claim]
King has bad
intentions

causescausesclimbs backyard [darkgreen observation]
King climbs into
backyardcausesopen door [darkgreen observation]
King opens
door

causescausesenter house [darkgreen observation]
King entershousecausesstep toy [darkgreen observation]
King steps on
toycausestoy sound [darkgreen observation]
Toy makes
soundothers living room [darkgreen observation]
Others in living
room

go bedroom [darkgreen observation]

hear toy [darkgreen observation]
toy sound [darkgreen observation]
Toy makes
soundcausescauses

W1 goes tobedroom

W1 hears toy

causessee King [darkgreen observation]

causes

W1 sees King inbackyard

run King [darkgreen observation]
W1 runs afterKing

grab King [darkgreen observation]
causes

W1 and W2 grab
King

causesexit house [darkgreen observation]
King exitshousecausesclose door [darkgreen observation]
King closes
door

causesdoor closed [darkgreen observation]
Door is closed

Figure 10: King case: prosecution’s story with highlighted observations

bad intentions* [green claim]
King has bad
intentions

causescausesclimbs backyard [darkgreen observation]

open door [darkgreen observation]
causesKing climbs into
backyard

King opens
door

causesenter house [darkgreen observation]
causesKing entershousestep toy [darkgreen observation]
causestoy sound [darkgreen observation]
causesKing steps on
toy

Toy makes
soundothers living room* [darkgreen observation]
Others in livingroomhear toy [darkgreen observation]
toy sound [darkgreen observation]
Toy makes
soundcausesgo bedroom [darkgreen observation]
causessee King [darkgreen observation]
causes

W1 goes tobedroom

W1 hears toy

W1 sees King inbackyard

grab King [darkgreen observation]

run King [darkgreen observation]
causesW1 runs afterKing

causes
W1 and W2 grab
King

causesexit house [darkgreen observation]
King exitshousecausesclose door [darkgreen observation]
causesKing closes
door

door closed [darkgreen observation]
Door is closed

Figure 11: King case: prosecution’s story with explained observations

toy sound [darkgreen observation]wind toy [green observation]wind open [green claim]causesWind opens
door

causescausesToy makes
soundWind hits toy

causes...shortcutted..

hear toy [darkgreen observation]
W1 hears toy

wind close [green observation]
causesWind closes
door

door closed [darkgreen observation]
Door is closed

Figure 12: King case: King’s story

18...shortcutted..

grab King [darkgreen observation]
W1 and W2 grab
King

7

Conclusion

This article has reported on a research project on the development of sense-making software
for crime investigation. We have discussed how the causal aspects of evidential storytelling
can best be modelled and visualised and linked to the available evidence. We claim to havemade two main contributions. Firstly, we have argued that in the context of sense-making
systems for crime investigation there are reasons to combine two AI approaches, abductive
explanatory reasoning and modus-ponens-style default reasoning, which are usually considered
as irreconcilable alternatives. We have outlined how this proposal can be formalised. Froma legal-theoretical perspective this amounts to an attempt to unify the Anchored Narratives
Theory and the New Evidence Theory. Secondly, we have described the current design of avisualisation software tool in which causal networks can be linked to the available evidencewith modus-ponens-style argumentation structures. This tool is still under construction. At
later stages of the project it will be empirically tested in user experiments to see whether using
such a tool indeed has the benefits it is often claimed to have. Among other things, this should
bring clarity on whether a combination of story-based and argument-based reasoning is indeed
natural to crime investigators.

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21

toy sound [darkgreen observation]
causeswind toy [green observation]

Toy makes
soundWind hits toy

causeshear toy [darkgreen observation]...shortcutted...

grab King [darkgreen observation]
W1 and W2 grab
King

W1 hears toy...shortcutted..

causesdoor closed [darkgreen observation]

W1 [blue quote]wind open [green claim]

W1 testimony

Wind opens
door

Door is closed

causescauseswind close [green observation]
Wind closes
door

W3 [blue quote]causesloud bang [red observation]
Loud bangno bang [darkred observation]

W3 testimony

No loud bang

W2 [blue quote]W2 testimony

Figure 13: King case: King’s story supported by evidence

bad intentions [green claim]causesKing has bad
intentions

climbs backyard [darkgreen observation]
King climbs into
backyardcausesothers living room [darkgreen observation]

causesopen door [darkgreen observation]...shortcutted...

King opens
door

toy sound [darkgreen observation]

causesToy makes
soundcausesgrab King [darkgreen observation]...shortcutted...

W1 and W2 grab
Kingwind toy [green observation]

exit house [darkgreen observation]

close door [darkgreen observation]

causesWind hits toy

King exitshousecauseswind close [green observation]

causeswind open [green claim]
Wind opens
door

hear toy [darkgreen observation]
W1 hears toy

Others in living
room

Wind closes
door

causesKing closes
door

causesdoor closed [darkgreen observation]
Door is closed

causescausesloud bang [red observation]
Loud bangno bang [darkred observation]
No loud bang

Figure 14: King case: the stories combined

bad intentions [green claim]
King has bad
intentions

causescausesclimbs backyard [darkgreen observation]

causesKing climbs into
backyard

others living room [darkgreen observation]
Others in livingroomhear toy [darkgreen observation]

King opens
door...shortcutted...

wind toy [green observation]

causescauses...shortcutted...
W1 and W2 grab
King

W1 hears toy

toy sound [darkgreen observation]
Toy makes
soundcausesexit house [darkgreen observation]
King exits
house

Wind hits toy
causeswind open* [green claim]
Wind opens
door

grab King [darkgreen observation]

open door [darkgreen observation]

causescausesclose door [darkgreen observation]
King closes
door

causesdoor closed [darkgreen observation]wind close [green observation]
causesDoor is closed

causesWind closes
door

causesloud bang [red observation]
Loud bangno bang [darkred observation]
No loud bang

Figure 15: King case: combined stories with explained observations

Broke [green claim]
King was broke

Drugs [green claim]

Suspect King is fromethnic minority

causesBad intentions [red observation]
King has bad
intentions

Prejudice [darkred claim][red inference]

Drug use [red claim][yellow causes]

King uses
drugs

Ethnic minority [red claim]

Generalization isbased on prejudice[darkred undercutter]

Generalization looks
at drug use

Figure 16: King case: a causal link attacked

22[yellow causes]

causesInto backyard [lightgray observation]
King climbs into
backyard