Investigating Stories in a Formal Dialogue
Game
Floris BEX a, Henry PRAKKEN a, bCentre for Law & ICT, Faculty of Law, University of Groningenb
Department of Information and Computing Sciences, Utrecht UniversityaAbstract In this paper we propose a formal dialogue game in which two players
aim to determine the best explanation for a set of observations. By assuming anadversarial setting, we force the players to advance and improve their own
explanations as well as criticize their opponent’s explanations, thus hopefully
preventing the well-known problem of ‘tunnel vision’. A main novelty of ourapproach is that the game supports the combination of argumentation with
abductive inference to the best explanation.

1. Introduction
In the literature, two main approaches to reasoning about factual issues in criminalcases are the story-based and the argument-based approach. Some authors [1] argue
that legal reasoning about evidence is mainly done by constructing and analysing
evidential arguments from the sources evidence to the events that are to be proven.
Other authors [13] argue that legal reasoning with evidence is instead done byconstructing different stories around the evidence and then analysing and comparing
these stories. In previous work ([5], [6]), we have shown that these approaches can be
combined in one formal framework, namely as a combination of the AI formalisms of
abductive inference to the best explanation (IBE) and defeasible argumentation. Stories
about what might have happened in a case are represented as hypothetical explanations
and links between stories and the available evidence are expressed with evidential
generalizations that express how parts of the explanations can be inferred fromevidential sources with defeasible argumentation. Combining the argumentative and
IBE approach in this way solves some of the problems of the separate approaches
A limitation of our previous work is that it discusses only a static viewpoint: aframework is provided for the current status of an argumentative and story-based
analysis of a case, and the dynamics of developing and refining such an analysis are notdiscussed. In this paper, we aim to model these dynamics in a formal dialogue game. In
this dialogue game, it should be possible to build, critically analyse and change stories
and their supporting arguments.
The game we propose is for dialogues in which crime analysts aim to determine
the best explanation for a set of observations. Despite this cooperative goal of the
dialogue participants, we still assume an adversarial setting in which the protocol isdesigned to motivate the players to ‘win’. Thus we hope to prevent the well-known
problem of ‘tunnel vision’ or confirmation bias, by forcing the participants to look at
all sides of a case. A main novelty of our approach, motivated by our previous work in[5] on the static aspects of crime investigation, is that the game supports the
combination of argumentation with IBE.
The rest of this paper is organized as follows. In section 2, we summarize our
combined framework as developed in [5]. In section 3 we present our formal dialogue
game and in section 4 we apply it to a simple example. Finally, in section 5 weconclude with a discussion and some ideas for future research.
2. Argumentative Story-based Analysis of Reasoning with Evidence
In this section, the framework for argumentative story-based analysis of reasoning withevidence as proposed in [5] will be summarized. This formal framework combines alogic for defeasible argumentation with a logical model of abductive inference to thebest explanation (for an overview of abductive reasoning see [10]). We first discuss our
combined logical framework, followed by a short example. Then we will argue that thiscombined approach solves some of the problems of purely argumentative and purely
IBE approaches to evidential reasoning.
The basic idea of the combined approach is as follows. A logical model ofabductive IBE takes as input a causal theory and a set of propositions that has to be
explained, the explananda, and produces as output a set of hypotheses that explain the
explananda in terms of the causal theory. The combination of hypotheses and causal
theory can be seen as a story about what might have happened. These hypotheticalstories can then be compared according to the extent to which they conform to theevidence in a case. This evidence is connected to the stories by defeasible argumentsfrom evidential sources (e.g. witness testimonies). Defeasible arguments are also used
to reason about the plausibility of a story: the causal rules of the causal theory are notjust given but their applicability can become the subject of an argumentation process.
This definition of stories as causal networks is not entirely new: Pennington and Hastie[13] also defined stories as causal networks, following earlier influential research by
Schank and Abelson [18]. Note that we use a naïve interpretation of causality;
sometimes a causal link does not represent a much stronger relation than temporal
precedence. This allows us to model a story as a simple, chronologically orderedsequence of events.
A framework for evidential reasoning ER = (C, A) is a combination of a causalabductive framework C and an evidential argumentation framework A. The underlying
logic L of this framework consists of the inference rules of classical logic combinedwith a defeasible modus ponens rule for a conditional operator
for defeasible
generalizations. The generalizations used in C and A (see below) are formalized withthis connective: gi: p1 ∧...∧ pn q. Here gi is the name of the generalisation and p1…pn
and q are literals. The type of generalisation is indicated with a subscript: E denotes
an evidential generalisation andC denotes a causal generalisation. For example,
Smoke E Fire says that smoke is evidence of fire, while Fire C Smoke says that firecauses smoke.
The argumentation framework is a pair A = (G, I), where G is a set of evidential
generalizations and I is a set of input facts, where IE ⊆ I is the set of sources ofevidence in a case. This set of evidence IE is different from other input facts in that thesources of evidence in IE cannot be attacked by arguments. The other elements in I arepropositions that denote ‘general knowledge’, opinions or ideas which may be open todiscussion and which are not generalizations of the form ‘if…then…’ (e.g. ‘Hillary
Clinton will probably be the next U.S. president’ or ‘the idea that people from
Suriname rob supermarkets more often than Dutch people is based on prejudice’).
The logic for this framework is very similar to the logic underlying the ASPIC
inference engine [1], which in turn combines Pollock’s [14] ideas on a tree structure ofarguments and two notions of rebutting and undercutting defeat with Prakken &Sartor’s [17] rule language and their argument game for Dung’s [8] grounded
semantics. The set I and the evidential generalizations from G allow us to build
evidential arguments by taking elements from I and the generalizations as premises andchaining applications of defeasible modus ponens into tree-structured arguments. Suchan evidential argument is a finite sequence of lines of argument, where a line is either a
proposition from I, a generalization from G or the result of an application of thedefeasible modus ponens to one or more previous lines. Args(A) is the set of all wellformed arguments in A.
An argument can defeat another argument by rebutting or undercutting the otherargument. Two arguments rebut each other if they have the opposite conclusion. An
argument AR1 undercuts another argument AR2 if there is a line ¬gi in argument AR1
and a line in argument AR2, which is obtained from some previous lines in AR2 by theapplication of defeasible modus ponens to gi.
For a collection of arguments and their binary defeat relations, the dialectical statusof the arguments can be determined: arguments can be either justified, which meansthat they are not attacked by other justified arguments that are stronger, or overruled,which means that they are attacked by one or more other stronger arguments that are
justified, or defensible, which means that they are neither justified nor overruled. Notethat in the present paper, we will not discuss the relative strength between arguments.
The abductive framework is a tuple C = (H, T, F). Here, T is the causal theory
which contains all the causal generalizations from the different stories. H is a set of
hypotheses, propositions with which we want to explain the explananda. F is the set of
explananda, propositions that have to be explained. A set of hypotheses H and a causal
theory T can be used to explain propositions:(explaining) Hi ∪ Ti, where Hi ⊆ H and T i ⊆ T, explains a set of propositions E iff
1. ∀e : If e ∈ E then:- Hi ∪ Ti
e; and- Hi ∪ Ti is consistent.
2. There is no justified argument in Args(A) for the conclusion ¬ g, where g ∈ Ti.
Here stands for logical consequence according to the set of all deductive inference
rules extended with modus ponens for . Condition (1) of this definition is standard in
logical models of abduction but condition (2) is new and makes it possible to attack
causal generalizations of dubious quality in the explanation with an argument: Hi ∪ Ti
does not explain E if one of the generalizations in T is attacked by a justified argument.
The idea of our dialogue game is that during a dialogue the players jointly andincrementally build a framework, which can contain several alternative explanations
for the explananda. Moreover, these explanations can be extended during the dialogue,
for instance, by giving a further explanation for a hypothesis. Therefore we must beable to identify at each step of the dialogue the explanations for F.
(explanation) Given a framework ER, an explanation S = Hi ∪ Ti is an explanation for
the explananda F iff:
1. S explains F; and2. Hi ⊆ H contains only initial causes; and3. Ti is a minimal subset of T.
Initial causes are propositions that are not a conclusion of a causal rule in T. This
ensures that an explanation is considered from beginning to end. The condition that Ti
is a minimal subset of T ensures that two explanations for F are really seen as two
different explanations. The set of all explanations for F in a framework is denoted asExpl(ER). If there is more than one explanation for the explananda, they must becompared according to their plausibility and their conformity to the evidence in a case.
The plausibility of a story is often judged by looking at the plausibility of its
underlying generalizations (cf. [19]). Two kinds of generalizations are important toconsider: the causal generalizations in the explanation and the evidential
generalizations in the arguments linking the evidence to the story. The plausibility of
the causal generalizations is ensured by point 2 of the definition of explaining on theprevious page. In the same way, the plausibility of the evidential generalizations in thearguments is ensured by allowing arguments to be attacked and defeated.
As to an explanation’s conformity to the evidence in a case, we recognize threecriteria. The first of these is evidential coverage, which stands for the number ofsources of evidence covered by an explanation. The second is evidential contradiction,
which stands for the number of events in the explanation contradicted by evidential
arguments and the third is evidential support, which stands for the number of events ina story supported by evidential arguments. Evidential coverage was first mentioned in[13] and the other criteria were mentioned in [6]. Because in this paper the focus is on
the dialogue game, we only formally define evidential coverage.
(evidential coverage) The evidential coverage of an explanation S, denoted as ecS, isthe total number of sources of evidence that are covered by an explanation, where:• a source of evidence p ∈ IE is covered by an explanation S if a proposition in S
follows from a non-overruled argument in Args(A) which has p as its premise.
Thus, if a proposition in the explanation follows from a source of evidence theexplanation covers that source of evidence. So if, for example, an explanation S covers
five pieces of evidence, then ecS = 5. Note that here we do not intend to define an
objective probabilistic measure for the quality of stories; instead the notion ofevidential coverage aids us in comparing explanations, viz.: an explanation S is better
than an explanation S′ if its evidential coverage is higher. Note how the above
definition ensures the plausibility of the evidential generalizations: if an argument thatlinks a certain piece of evidence to an explanation is overruled, that piece of evidence
does not count towards the evidential coverage of an explanation.
The explananda, while they also follow from evidence and are also events in an
explanation, are treated differently from other events in an explanation; explananda
cannot be attacked and providing arguments from evidence for an explanandum does
not increase an explanation’s evidential coverage. This is because we do not want toreason about what should be explained but instead we want to reason about how certain
events are explained. In section 3.2 this point will be made clearer.
Let us illustrate the combined framework with a simple example, adapted from
Wagenaar et al. ([19], page 35). The example concerns the Haaknat case, in which asupermarket was robbed. The police conducted a search operation in a park near thesupermarket, hoping to find the robber. Haaknat was found hiding in a moat in the park
and the police, believing that Haaknat was the robber, apprehended him. Haaknat,
however, argued that he was hiding in the moat because earlier that day, he had anargument with a man called Benny over some money. According to Haaknat, Bennydrew a knife so Haaknat fled and hid himself in the moat where the police found him.
The explanandum in this case is ‘Haaknat is found by the police’. In figure 1 the two
explanations for this explanandum are represented in a simple graph.

Figure 1: two explanations for the fact that Haaknat was found in the moat

In the figure, the causal theories combined with the hypotheses are represented as whiteboxes, where the variables in the causal theories have been instantiated with theconstants from the hypotheses. Causal relations are rendered as arrows with an open
head. A piece of evidence, namely, that the owner of the supermarket testified that itwas Haaknat who robbed his shop, is represented as a grey box and the evidential
generalization is represented as a grey rounded box; evidential relations are rendered asarrows with a closed head. In the example ‘Haaknat robs a supermarket’ follows from a
non-overruled evidential argument. This can be seen in the figure, where events that are
not supported by evidence are in a dotted box and events that are supported byevidence in a box with a solid line. The explanandum of course also follows fromevidence (in this case a police report and Haaknat’s own testimony). However, thelinks between this evidence and the explanandum have not been rendered in the above
figure, because we want to focus on whether the explanation is supported by evidence
and not on whether the explanandum is supported by evidence. The bottom explanation(that Haaknat robbed the supermarket) can be regarded as the best explanation because
it has an evidential coverage of 1 while the other explanation (that Haaknat had anargument with Benny) has an evidential coverage of 0.
Both the argumentative and the IBE approach have been used separately to model
evidential reasoning (see [4] for an example of a purely argumentative approach and[19] for an example of an IBE approach). We now briefly explain why we combine thetwo approaches instead of adopting just one of them.
A disadvantage of an argumentative approach is that it does not provide a complete
overview of the case, as the original stories about ‘what happened’ are cut into pieces
to become conclusions of different arguments and counter-arguments. An approach
where stories are represented as causal networks and thus the overview of the case isretained is closer to how legal decision makers and investigators actually think about acase ([13],[15]). This was informally confirmed in our contacts with police detectives
and lecturers of the Dutch police academy, in which we learned that crime investigators
often visualise time lines and causal structures to make sense of a body of evidence.
However, a problem of a purely IBE approach is that sources of evidence such as
testimonies are modelled as effects caused by the event for which they serve as
evidence. The advantage of adding evidential arguments to the IBE approach is that in
the combined theory, reasoning with sources of evidence is arguably more natural:
events are inferred from evidence using evidential generalizations. In our informal
contacts with the Dutch police we found that this is how crime analysts usually connect
the available evidence with their temporal and causal models of a case.
Another problem of the IBE approach as it is usually modelled is that it is
impossible to reason about the causal generalizations used in an explanation. In legal
settings this is a limitation since it is well-known that in criminal cases the quality of

the generalisations used by crime investigators or lawyers to build explanations cannot
be taken for granted ([1], [19]).
3. The dialogue gameThe analysis of stories and evidence in a case is a process; exactly how this process
takes form depends on who performs the analysis and the specific legal context theanalysis is performed in. In a decision-making context, for example, the defence isconfronted with a complete story about what happened, namely the prosecution’s story.
Usually, this story is already supported by evidence and the defence will try to attack
the prosecutor’s evidential arguments (by arguing, for example, that a witness is nottrustworthy) or the defence gives an alternative explanation (for example, that analleged killer acted in self-defence). In an investigation context, however, things aredifferent. Often, a team of criminal investigators is faced with some initial evidence
and they construct several possible stories (or scenarios) and then try to find new
evidence that supports or discredits these scenarios. During the investigation there isconstant interaction between the scenarios and the evidence: a scenario provides aframe in which new evidence can be interpreted and, at the same time, new evidence isused to support or discredit a scenario or to extend a scenario [15].
In this paper, we aim to model the dynamics of the process of analysing stories andarguments in a formal dialogue game, with which it should be possible to build,critically analyse and change explanations and their supporting arguments.
Dialogue games formulate principles for coherent dialogue between two or more
players, and this coherence depends on the goal of a dialogue. In our previous work on
dialogue games [3], one of the players made a claim which he had to defend, while theother player’s goal was to dispute this claim. The goal of the dialogue game was toresolve this difference of opinion in a fair and effective way. By contrast, the current
dialogue game is meant to regulate a discussion between analysts in a criminal case. In
such a setting the players have identical roles since they both want to find a plausible
and evidentially well-supported explanation for the explananda. Moreover, none of theplayers really wants to win, since they have the joint goal to find the best explanationof the explananda. As explained in the introduction, our dialogue game is designed topromote this joint goal of the players by forcing them in an adversarial setting, where
they technically have the aim to ‘win’, so that all sides of a case are explored.
Accordingly, the game allows both players, given an initial body of evidence, topropose, criticise and defend alternative explanations for what happened. The idea
behind enforcing such an adversarial setting is to avoid the well-known problem of‘tunnel-vision’ or confirmation bias, where one explanation is taken as the right one
and the investigation focuses on finding evidence that supports this explanation while
dismissing evidence that contradicts this explanation. Note that while the game has twoplayers, extending the dialogue game to accommodate for more players is easy, thus
allowing our dialogue game to support discussions between groups of analysts.
Now, in a dialogue the players build a framework for evidential reasoning ER by
performing speech acts from a communication language Lc. With these speech acts,
explanations can be given for the explananda F, and arguments can be moved forsupporting explanations or for attacking explanations or other arguments, thus
continually updating the framework ER. One part of the dialogue game is a protocol,
which specifies the allowed moves at a certain point in the dialogue. Such a protocol isessentially a normative model for how the process of an analysis of evidence andexplanations should take place.
The dialogue game also has commitment rules, which specify the effects of aspeech act on the propositional commitments of the dialogue participants. For instance,
explaining the explananda with an explanation commits the speaker to the explanation
and retracting a previously moved argument removes this argument from the speaker’s
commitments. Commitments can be used to constrain the allowed moves, for example,
to disallow moves that make the speaker’s commitments inconsistent. They can also be
used to define termination and outcome of a dialogue. Recall that the objective of thegame is to find the best explanation for the explananda so the outcome of a dialogue isan explanation together with its supporting arguments and the dialogue terminates if
both players are committed to the best explanation. In addition, for nonterminateddialogues a notion of the current winner can be defined; this is the adversarial elementof the dialogue. The current winner is the player that is committed to the currently best
explanation for the explananda. The notion is used to control turn taking, with a rulethat a player is to move until he has succeeded in becoming the current winner (cf. [9]).
We now turn to the definitions of the elements of our dialogue game. Because of
space limitations, the definitions will in some places be semiformal. Below,
AR ∈ Args(A) and ϕ ∈ wff(L), where L is the underlying logic of the framework (seesection 2). Dialogues take place between two players, p1 and p2. The variable a ranges
over the players, so that if a is one player, then is the other player.
The communication language Lc consists of the following locutions or speech acts:••

argue AR. The speaker states an argument.
explain (E, S). The speaker provides an abductive explanation S = H ∪ T for a setof propositions E.
• concede ϕ. The speaker admits that proposition ϕ is the case.
• retract ϕ. The speaker declares that he is not committed (any more) to ϕ.
The speech act explain is new while the other locutions are well-known from theliterature. A dialogue d is now a sequence of utterances of locutions from Lc, where d0
denotes the empty dialogue. Each utterance is called a move. The speaker of a move mis denoted by s(m).
3.1. Commitments

The players’ commitments are influenced by the moves they do during a dialogue. At
the start of a dialogue the commitments of both players consist of just the explananda
from F. The set Commss denoting the commitments of the speaker s is updated during a
dialogue as follows. When s moves an argue AR move, the premises of AR andconclusions of AR are added to Commss; when s moves an explain (E, S) move, theelements from E and S are added to Commss; when s moves a concede ϕ move, ϕ isadded to Commss and when s moves a retract ϕ move, ϕ is deleted from Commss.
3.2. The framework in the dialogue protocol
Recall that in our setup the dialogue participants jointly build a framework forevidential reasoning ER. In this framework, the set explananda F is given and assumed
nonempty and the players can update the framework by providing explanations orarguments using the speech acts. The set F does not change during the dialogue, so it

must be agreed upon before the dialogue starts. It is in theory possible to have an
argumentative dialogue about what the explananda are. However, the purpose of thecurrent dialogue is to find explanations for certain observations and to compare these
explanations; a dialogue about what should be explained is a different kind of dialogue
the details of which we leave for future research.
ER(d) = ((H(d), T(d), F(d)), (G(d), I(d))) stands for the evidential reasoning
framework after dialogue d. The elements of this framework also denote the elements
after a certain dialogue d; so H(d) is H after dialogue d, T(d) is T after dialogue d
etcetera. When the speaker s makes a move, the framework is updated as follows.
When s moves an argue AR move, the generalizations in the argument AR are added tothe set of evidential generalizations G and the other premises of AR are added to I.
When s moves an explain (E, (H′ ∪ T′)) move, the hypotheses in H′ are added to H andthe causal generalizations in T′ are added to T. When s moves a retract ϕ move andϕ ∉ Commss(d), then ϕ is removed from its corresponding element in the framework.
3.3. Turn taking and winning
Before the protocol itself is defined, two related notions need to be defined, namely
turn taking and winning. A player a is the current winner of a dialogue d if there is an
explanation S, S ∈ Expl(ER(d)) and S ⊆ Comms(a), and for each other explanation S′,
S′ ∈ Expl(ER(d)) and S′ ≠ S, it holds that ecS > ecS′. So if there is only one explanation
for the explananda in the current framework, a player is the current winner if he is the
only player committed to that explanation. If there are more explanations, a player isthe winner if he is the only player committed to the explanation that has the highest
evidential coverage. Note that this definition of the current winner also allows that
there is no current winner, namely when both players are committed to explanations
with equal evidential coverageWith the notion of a current winner, a turn taking rule can be defined as follows:
Turn is a function that for each dialogue returns the players-to-move, such thatTurn(d0) = p1, Turn(d, m) = if a currently wins d, else if there is no current winnerand Turn(d) = a then Turn(d, m) = a. Thus it is always the losing player’s turn and even
if he makes such a move that the other player is no longer the winner, he still has tobecome the winner himself. This situation ensures that both players try to advance anddefend their respective explanations as opposed to the situation where one player gives
an explanation and the other player constantly attacks this one explanation.
3.4. The protocol
The protocol P specifies the allowed moves at each stage of a dialogue. Its formal
definition is as follows. For all moves m and dialogues d it holds that m ∈ P(d) if andonly if all of the following conditions are satisfied:
1.
2.
3.
4.

Turn(d) = s(m)m was not already moved in d by the same player
Commss(d, m)
If m is an argue AR move (where ϕ is AR’s conclusion), then ϕ ∉ F and− either ϕ = ¬gei for some gei ∈ G(d) or ϕ is a negation of an elementin (I / IE)− either ϕ = ¬gci for some gci ∈ T(d)

5.
6.
7.
8.

− or ∃S, S ∈ Expl(ER(d)), S ⊆ Comms(s) and ecS(d, m) > ecS(d)If m is an explain (E, S) move, then
− S explains E and E ∩ F ≠ ∅− S explains E and; ∀e ∈ E: H(d) ∪ T(d) e and e is a literal
If m is a concede ϕ move, then ϕ is in an element of ER(d) and ϕ ∉Commss(d)If m is a retract ϕ move, then ϕ ∉ F and ϕ ∈ Commss(d)¬∃S: S ∈ Expl(ER(d)), S ⊆ Commss(d), S ⊆ Commss(d) and for each otherexplanation S′ , S′ ∈ Expl(ER(d)) and S′ ≠ S, it holds that ecS > ecS′.

The first two conditions say that only the player-to-move can make allowed moves andthat a player may not repeat his moves. Condition (3) regulates the players’ logicalconsistency. The first point of condition (4) states that an argument may be moved if it
attacks another argument, that is, it attacks an evidential generalization or it attacks anelement from input that is not a source of evidence. The second point states that anargument may be moved if attacks an explanation, that is, it attacks a causal
generalization. The third point states that an argument may be moved if it improves theevidential coverage of an explanation to which the player is committed. Condition (5)states that an explain move may be done if it explains (see definition on page 3) an
explanandum, or if it explains a literal that follows from the current hypotheses and thecurrent theory. Condition (6) ensures that a player concedes a proposition only if it is in
the current framework and the player is not already committed to it. Condition (7) saysthat a player can only retract a proposition to which he is committed. Finally, condition(8) implies that a dialogue terminates if both players are fully committed to the best
explanation.
In the current dialogue game, the legality of moves is defined in terms of thecurrent framework for evidential reasoning: every move must be a sensible operation
on ER. This way of defining the relevance of moves is different from, for example, [12],where relevance is enforced by a strict protocol and [16], where relevance is enforcedby the reply structure on the speech acts in the communication language.
4. Example
For the example we return to the Haaknat case on page 5. The set of explananda F inthis case is {Haaknat is found by police}. Player p1 starts the dialogue by providing an
explanation for this explanandum:
p1: explain ({Haaknat is found by police}, {Haaknat robs supermarket} ∪ T1)
where T1 = {gc1: x robs supermarket C x flees, gc2: x flees C x hides in a moat,
gc3: x hides in moat C x is found by police}
Now p1 is winning, because he is committed to the one explanation for F, which is
obviously the best explanation. p2 at this point only has one option if he wants tobecome the current winner: he has to provide an explanation for F which is better than
p1’s explanation.
p2: explain ({Haaknat is found by police}, {argument between Haaknat and Benny} ∪ T2
where T2 = {gc4: argument between x and y C x flees, gc2, gc3}

After providing this explanation, it is still p2’s turn, as the explanation he has provided
is not better than p1’s explanation. p2 supports his explanation by providing anargument. Below, stands for the application of the defeasible modus ponens.
p2: argue AR1:(e1: Haaknat’s testimony “I had an argument with Benny” ∧ ge1: witness testifies that “p”
argument between Haaknat and Benny)

E p

Now p2 is the current winner: there is one piece of evidence in the case and it iscovered by p2’s explanation. The current framework is pictured in the figure below (thedifferent arrows and boxes are explained on page 5). For each event, it is indicatedwhich players are committed to that event.

At his point p1 can, for example, provide an argument for ¬gc4; if he does this, then p2
no longer has an explanation for F so p1 automatically has the best explanation. He canalso try to support “Haaknat robs a supermarket” with at least two pieces of evidence,as this would make p1’s explanation have a higher evidential coverage. Another option
is to decrease the evidential coverage of p2’s explanation by defeating the argument
AR1. Suppose that p1 chooses to take this last option:
p1: argue AR2:(e2: Haaknat is a suspect in the case ∧ ge2: suspects do not make reliable witnesses)¬ge1

For the sake of the example, assume that this argument defeats AR1. p1 is still not thecurrent winner: both explanations have an evidential coverage of 0, so p1 has to makeanother move in order to make his explanation better or p2’s explanation worse. p1
could increase the evidential contradiction of p2’s explanation by providing anargument for ¬(argument between Haaknat and Benny). However, in this case p1 chooses
to increase the evidential coverage of his own explanation. He does this by first
expanding the explanation and then supporting it with evidence:
p1: explain ({Haaknat robs supermarket}, {Haaknat is from Suriname} ∪ T3
where T3 = {gc5: x is from Suriname C x robs supermarkets}
p1: argue AR3:(e1: Haaknat’s birthplace is “Republic of Suriname” ∧ge2: x birthplace is “Republic of Suriname” E x is from Suriname)Haaknat is from Suriname
The following picture represents the current situation. The light grey argumentation
arrow means that the inference is defeated and the arrow connected to AR2 stands for a
defeat relation.

p1 is now the winner: his explanation has an evidential coverage of 1 while p2’s
evidential coverage is 0. However, part of p1’s generalization is based on thegeneralization “people from Suriname rob supermarkets”. p2 does not agree with thisand he argues that the generalization is based on prejudice:p2: argue AR4: (i1: gc5 is based on prejudice, ge2: gci is based on prejudiceE ¬gci)¬gc5

Note that AR4 is not based on evidence, so it is possible for p1 to attack i1. By attacking
gc5, p2 ensures that {Haaknat is from Suriname} ∪ T1 ∪ T3 is no longer an explanation.
This is shown in the following figure, where AR4 attacks the causal generalization andthe part of p2’s explanation that is no longer considered is rendered in light grey.

p2’s explanation {argument between Haaknat and Benny} ∪ T2 and p1’s explanation{Haaknat robs supermarket} ∪ T1 now both have an evidential coverage of 0, so p2 needs
to make another move in order to become the winner.
5. Conclusions
In this paper we have shown how the combined story and argumentative approach toreasoning with evidence can be fit into a formal dialogue game. This dialogue game not
only allows for the construction of defeasible arguments but also allows the players to
explain events using abductive explanations, which can then be compared according totheir conformity with the evidence in the case. Furthermore, the argumentative part of
the game and framework allows players to have critical discussions about theplausibility and validity of the causal and evidential generalizations. The winning andturn taking conditions ensure that the players are forced to advance and improve theirown explanations as well as criticize their opponent’s explanations, which hopefully
avoids ‘tunnel-vision’ or confirmation bias.
Our dialogue game can be seen as a guideline for a critical discussion between
investigators in a criminal case. Furthermore, we intend to use the ideas presented inthis paper in the further development of our software for police investigators, AVERs,
which is currently being developed by other members of our project [7].
The precise form of our dialogue game is, to our knowledge, new. As far as weknow, there are only two other dialogue games that allow the players to jointly build atheory ([11],[3]) build a Bayesian network and an argumentation-graph, respectively,
whereas in our game a combined framework for argumentation and IBE is built. Our
dialogue is a combination of an enquiry and a persuasion dialogue [21]; on the one
hand, the players have a shared ‘quest for knowledge’ but on the other hand, theplayers try to persuade each other that their explanation is the best.
Acknowledgements
This research was supported by the Netherlands Organisation for Scientific Research(NWO) under project number 634.000.429. The authors thank Bart Verheij for his
useful comments on the ideas reported in this paper.

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