Generating Non-linear Narrative for Serious Games

with Scenario Templates

Koen Samyn

, Ga

etan Deglorie

, Peter Lambert

, Rik Van de Walle

and Soﬁe Van Hoecke

DAE, University college of West Flanders, Ghent University Association, Botenkoperstraat 2, B-8500 Kortrijk, Belgium

Multimedia Lab, ELIS, Ghent University-iMinds, Gaston Crommenlaan 8 Bus 201, B-9050 Gent, Belgium

Keywords:

Serious Game, Domain-speciﬁc Modeling Languages.

Abstract:

Complex social interactions between NPC’s and players in a serious game remains a challenge. Standard

game engines are focused on a game world with a ﬁxed set of rules and linear gameplay. The problem is

compounded by the unfamiliarity of many researchers with the basic structure and technicalities of a serious

game. In this paper we propose a method to use linear scenario elements as templates for the generation of

a non-linear narrative. We implement this method by creating an extra layer on top of the existing ATTAC-L

modeling language which is a tool for developing virtual interactive scenarios. Users are thus presented with

a language that offers limited ﬂow control and simpliﬁes the authoring process for the creation of scenario

elements. Our method uses these existing scenario elements together with metadata, and ﬁlls in the templated

elements with the contextual information of the current game state. By separating scenario concerns from

non-linear narrative concerns, we hope to make it easier to develop interesting non-linear serious games that

still conform to the requirements of evidence-based serious game research.

1 INTRODUCTION

Non-linear gameplay is a concept that is high on the

wishlist for a lot of game developers. However, an

example of real non-linear narrative is hard to ﬁnd

in the wide range of commercial or serious games.

This should not come as a surprise because there are

many hurdles to take before linear narrative offers the

necessary consistency in terms of game logic and im-

mersion. Every bifurcation in the story results in a

doubling of the amount of scenarios that are needed

to deﬁne the game. Needless to say that a naive ap-

proach towards the implementation of non-linear nar-

rative leads to a huge amount of work, and ultimately

to compromises that reduce the combinatorial explo-

sion.

In this paper, we propose a solution for a non-

linear narrative that is built on top of ATTAC-L

(Broeckhoven and Troyer, 2013). ATTAC-L is a do-

main language for deﬁning virtual scenarios that is

being developed as part of the Friendly Attac project.

The Friendly Attac project studies and develops an

innovative serious game to help youngsters deal with

cyberbullying issues. This is done by allowing young-

sters, through the use of the virtual scenarios, to ex-

perience different roles (bully, victim, or bystander)

Figure 1: Attac-L example.

in cyber bullying incidents during the game, to re-

act to those experiences, and to get adjusted feed-

back based on their individual reactions. This will

increase their empathy, enhance their social skills or

teach/train them relevant coping strategies. ATTAC-

L offers the user (domain experts) a set of graphi-

cal building blocks that can be assembled to create

a simple scenario. An example of a simple ATTAC-L

scenario is presented in ﬁgure 1. In this example, a

player sends a tweet to Kate with the message ’Hi’.

In the ATTAC-L language, an action or sentence can

be deﬁned by using a simpliﬁed grammatical struc-

ture, in which a subject can declare the execution of a

verb. ATTAC-L also allows the deﬁnition of a choice

structure (where the user can pick one of the provided

options), and the deﬁnition of synchronous actions,

where all the actions are executed at the same time.

A recent implementation of ATTAC-L for serious

games (Janssens et al., 2014) translates these graphi-

cal building blocks into an XML declarative language

523

Samyn K., Deglorie G., Lambert P., Van de Walle R. and Van Hoecke S..

Generating Non-linear Narrative for Serious Games with Scenario Templates.

DOI: 10.5220/0005363705230530

In Proceedings of the 10th International Conference on Computer Graphics Theory and Applications (GRAPP-2015), pages 523-530

ISBN: 978-989-758-087-1

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

which is interpreted within a serious game engine.

This implementation provides support to test ATTAC-

L scenarios in isolation and also to integrate the sce-

narios in a serious game.

The structure and syntax of the ATTAC-L lan-

guage is simple as to facilitate the creation process of

scenarios for users who are not familiar with game de-

sign. However this simplicity also restricts the range

of possible scenarios and makes it very cumbersome

to introduce non-linear game play. Some notable lim-

itations of ATTAC-L are the lack of state manage-

ment and repetition. To overcome these limitations,

we introduce the concept of a story engine. The

story engine selects a scenario template from a sce-

nario database based on the current state of the player

model and provides the template with contextual in-

formation from the game. The story engine is also

responsible for maintaining the integrity of the sto-

ryline which is deﬁned by additional metadata in the

templates.

Performance and change objectives (Desmet et al.,

submitted) are an additional consideration for the de-

sign of a serious game. In an evidence-based seri-

ous game founded on the intervention mapping pro-

tocol (Bartholomew et al., 2011), performance ob-

jectives are the behaviours we would like to obtain,

whereas change objectives are deﬁned as the determi-

nants that inﬂuence these target behaviours. In our

proposal it is possible to link performance objectives

and change objectives to a scenario template. This

linking process makes it possible to track the progress

of the player throughout the duration of the game

and to present the player with the scenarios that are

needed to improve his/her proﬁciency in the subject

matter.

The remainder of this paper is as follows. In the

next section we discuss the related work. In section 3

we give an overview of the concepts of non-linear sto-

rytelling and game play adaptation. In section 4 we

delve deeper into the technical implementation of our

framework. In section 5 we discuss the adaptability

of the game play that allows us to present the player

a game environment that is tailored to his/her current

skill set. In section 6 we present a testing framework

that validates the selection of scenario templates for a

number of player types. Finally, we present the con-

clusions and future work in section 8.

2 RELATED WORK

In (Prensky, 2005) the author makes the case that dig-

ital game-based learning should maintain the balance

between learning and (fun) gameplay. Although we

present a method for creating interactive and non-

linear stories and not the actual creation of a game,

this balance must be kept in mind when determining

the feature set of the story engine. Another impor-

tant consideration is the payoff versus reward system

which indicates that players are prepared to put in the

work if the reward is sufﬁcient.

In (Greitzer et al., 2007) a cognitive model is pre-

sented that suggests that learners/players beneﬁt from

a layered and non-linear approach to learning. This

approach was applied for the serious game Cyber-

CIEGE by clustering scenarios in layers with greater

scenario complexity in each successive layer. We

present a similar approach but cluster the scenarios

in terms of performance or learning objective. The

CyberCIEGE game does not adopt an adaptive player

model, however we follow some of the recommenda-

tions of the authors for our approach (see section 5

with regards to player driven adaptation of the narra-

tive).

In project Muse (Llobera et al., 2013) a method

is presented that allows content creators to create an

interactive drama (ID) by adopting the concepts of

providence and the Zelig interaction metaphor, which

means that non-player characters can automatically

take on roles that are necessary for the advancement

of the storyline. Our presented aliasing approach (see

section 4.1) is similar but introduces additional con-

ditions on the role taking process.

3 OVERVIEW

An overview of the story engine is presented in ﬁg-

ure 2.

When the game requires the start of a new sce-

nario, a scenario is selected from a database of sce-

nario templates. The selection process takes the cur-

rent state of the player model into account, including

the previous actions and the current location of the

player. The scenario might also require the proximity

or availability of non-player characters (NPC’s). Fur-

thermore the scenario might deﬁne the characteristics

(traits and facets, see section 4.1) of these NPC’s.

Once a scenario is selected, the aliases in the sce-

nario template are replaced with the appropriate and

currently available NPC’s and/or items. The scenario

template can require an NPC, with a predeﬁned set

of personality characteristics, and/or a speciﬁc state

from the player model. An example scenario template

can deﬁne an alias for a character A with a person-

ality that contains the bully trait, and an alias for a

character B that is a victim of bullying. The aliases A

and B are then dynamically replaced with the selected

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524

Figure 2: Story engine overview.

NPC’s by querying the context of the game.

Next the actions in the scenario are played or

executed, and possible options are presented to the

player. The decision of the player will trigger an eval-

uation and leads to an update of the player model.

The player model is used to adapt the probability that

a given scenario template will be selected, given the

performance objective to which it belongs.

4 SCENARIO TEMPLATES

In our implementation, a story is composed of a hi-

erarchical structure of scenario templates. A scenario

template deﬁnes via metadata the circumstances, un-

der which a scenario can be selected and executed,

and the requirements for the NPC’s that will take part

in the scenario. Finally, the scenario template in-

cludes the actions of the NPC’s and the possible op-

tions for the player. The following subsections ex-

plain this in further detail.

4.1 Aliasing Approach

A central part of the scenario template is the deﬁnition

of an alias. An alias acts as a placeholder for NPC’s

or items. When a scenario template is selected for

execution, the alias is replaced with a concrete NPC

in the game. An example of an alias can be found in

listing 1.

Listing 1: Alias example.

<alias>

<trait type="victim"

value="80%" op="higher"/>

</select>

</alias>

In listing 1 a game object with type NPC is se-

lected. Within the selector it is possible to add mul-

tiple criteria for this NPC. It is possible to select for

a certain personality trait of the NPC by specifying

the type of the trait (in this case victim), a percent-

age value of the trait (in this case 80%) and an opera-

tor. In the example an NPC with a victim trait higher

then 80% is selected. Additionaly a facet of the NPC

can be selected with the facet element, in this case the

NPC must be a student.

In the remainder of the scenario template, this

NPC can be referenced by its alias CharacterA. This

is shown in listing 2.

Listing 2: Alias useage.

<property key="content"

value="Hi ${CharacterA.name}!"

</mediaObject>

</verb>

</gamemove>

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In listing 2 a special subject (the player) sends a

message to the CharacterA alias. Within the mes-

sage, an escape sequence can be used to query to

properties of the alias. When the scenario is played

the escape sequence ${CharacterA.name} will be

replaced by the name of the selected NPC.

Finally it is possible to propagate an alias to other

scenarios by deﬁning another query within the select

tag, as shown in listing 3

Listing 3: Propagation example.

<alias>

</select>

</alias>

The adopt tag instructs the select tag to adopt

the same NPC as the one that was selected for

scenario1. A prerequisite for this tag to work is that

scenario1 must be executed before the current sce-

nario. This can be enforced by the use of requirement

meta-data as explained in the next section.

4.2 Storyline Control

The scenario template system enables the reuse of

story elements in the larger context of the game. The

story engine also provides several possibilities to con-

trol the order of the scenario elements, as deﬁned in

the metadata of the scenario template.

A ﬁrst important metadata property is the replaya-

bility of a scenario template. An example of a re-

playable type of scenario template is a help scenario

template. In this case the scenario template requires

an NPC with a victim personality type that has lost

an item. The player might opt to help the NPC or to

ignore the NPC. Listing 4 shows an example of a sce-

nario with a replay meta tag.

Listing 4: Help NPC.

<property key="replayable"

value="true"/>

</metadata>

<trait type="victim"

value="80%" op="higher"/>

</select>

Help ${CharacterA.name}

</option>

Ignore ${CharacterA.name}

</option>

</eventpart>

</scenario>

Another possibility is to require the (positive) ex-

ecution of a scenario template A before scenario tem-

plate B can be executed. In the help quest exam-

ple (named help npc) helping the victim is required

to open up the execution of another scenario tem-

plate named explore quest. An additional feature

is that the explore quest scenario can require the

same NPC as the help npc scenario.

Listing 5: Explore quest.

<property key="replayable"

value="false"/>

</require>

</metadata>

</select>

</scenario>

In listing 5 the scenarioref tag deﬁnes the sce-

nario that is required before this scenario can be

started. Of course it is possible to deﬁne multiple sce-

narios as required.

The explore quest scenario in the same list-

ing can be executed unconditionally. However, if

the player did not choose the help of the NPC

in the help npc scenario, the execution of the

explore quest is inconsistent. To remedy this in-

consistency a new metadata tag can be used, as shown

in listing 6, where the explore quest has been updated

with an additional condition.

Listing 6: Explore quest with condition.

<property key="replayable"

value="false"/>

</require>

<faction

idA="CharacterA"

idB="player"

value="friend"/>

</conditions>

</metadata>

</select>

</scenario>

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With a faction tag it is possible to specify that the

two characters, as deﬁned by the attributes idA and

idB must be friends with each other. The player value

is a special value that references the player and the

CharacterA value is deﬁned by the select tag in the

same scenario. In this case the type of the condition

was set to faction but it is also possible to query spe-

ciﬁc attributes of an NPC, or to check the inventory of

a character or player. Some of these possibilities are

shown in listing 7.

Listing 7: Conditions.

<inventory

id="player"

item="key#731"

value="true"/>

<attribute

id="CharacterA"

attribute="health"

value="50%"

op="higher"/>

</conditions>

The trait and facet selectors, deﬁned in sec-

tion 4.1, are valid within the condition tags. The

faction, inventory and attribute tags are also valid

within a select tag.

5 PLAYER-DRIVEN

ADAPTATION

Serious games are used to teach certain behavior or

knowledge to users. To provide an optimal learn-

ing experience, the teacher should adapt to the re-

spective student, e.g. by taking more time to thor-

oughly explain the concepts that the student has trou-

ble with. However, the average video-game does not

adapt gameplay to each individual player. Typically,

one constructs some kind of player-model based on

real-time player input or post-game feedback. The re-

alized model can then be used to tweak certain pa-

rameters of the game (e.g. generating different level

geometry to increase challenge for skilled players). In

an educational context, learning state can be checked

through performance or learning objectives. We use

these performance objectives to track the player’s

learning state in real-time and adapt the occurrence of

learning challenges in order to better suit the problem

areas of player.

In the next subsections we expand on performance

objectives, extracting the player model and probabil-

ity distribution of template selection respectively.

5.1 Performance Objectives

A serious game based on the Intervention Mapping

Protocol needs to deﬁne change and performance ob-

jective, and link them to the correct scenario template.

Performance objectives deﬁne the desired behavior

for the player in the game world but mostly in the

real world. A game with cyberbullying as topic can

for example deﬁne positive bystander behaviour for a

player which means that the player has to disapprove

of bullying behaviour in a visible and public manner.

Change objectives deﬁne the underlying determinant

for the behavior. It may be that, in order to disapprove

of bullying in a public way, the player needs to expect

that this will end the bullying without putting himself

at risk. Another change needed in the determinants

could be that the player has to believe the victim is

not to blame for being bullied.

In our implementation a scenario template can ad-

dress one performance objective to induce the desired

behavior together with one (typically) or more change

objectives. This information is stored into the meta-

data of the scenario template. The scenario templates

can then be clustered by performance objective which

enables the evaluation of the player and the player-

driven adaptation of the storyline.

5.2 Player Evaluation

To predict the player model, we assume that a player

has a certain proﬁciency with each performance ob-

jective. We map this in range of 0 to 1, where 0 states

that the player does the opposite of what the perfor-

mance objective requires, 0.5 that the player acts ran-

domly (i.e. neither in favour nor in opposition of the

performance objective) and 1 that the player fully acts

in accordance with the performance objective.

Our concept is to predict the player model during

gameplay based on the actions performed by the

player. Currently the actions that can be performed

are of a binary nature, acting either for or against the

performance objective. We start by assuming that the

player has a 50% proﬁciency in all performance ob-

jectives (i.e. starting out without a bias). For each

event, the action is checked to be for or against its

performance objective. If it’s for, we simply increase

the proﬁciency for that objective by a certain value. If

it’s against, we decrease the proﬁciency.

This increase/decrease value, named adaptation rate

from here on, is changed according to the current

proﬁciency level (see Figure 3). For the mapping of

proﬁciency to adaptation rate, we chose a bell curve

(modelled as 2 sigmoid-functions) because of follow-

ing reasons:

GeneratingNon-linearNarrativeforSeriousGameswithScenarioTemplates

527

Figure 3: Adaptation rate as a function of proﬁciency.

• A high adaptation rate near the center makes the

proﬁciency go in either direction fast and thus al-

lows the system to quickly revert to 50% when

evaluations alternate (i.e. avoids creating an un-

necessary bias).

• A progressively lower adaptation rate near the

ends of the proﬁciency spectrum creates a barrier

so that truly mastering a performance objective

does not come too easy.

• Making the curve ﬂatter near the ends of the proﬁ-

ciency spectrum creates a barrier for return. When

having achieved the far end of the spectrum for

an objective, we don’t want the proﬁciency to re-

turn in the opposite direction for the slightest op-

posite action. For example having a mastered

an objective, the player’s proﬁciency should not

drop signiﬁcantly for a single mistake once and

a while. Similarly having failed an objective, the

proﬁciency should not go up signiﬁcantly if the

player happens to make a correct ‘guess’ once in

a while. Only if the player consistently makes

correct choices, the proﬁciency should change ac-

cordingly.

The peak of the curve is controlled by the learning

rate parameter α. An evaluation using our prediction

model and the current error-rate will be displayed in

the results section.

5.3 Probability Distribution of Template

Selection

The scenario templates are grouped according to per-

formance objective. During gameplay, whenever a

new scenario has to be loaded, a new template is se-

lected at random. This is achieved by ﬁrst selecting

a performance objective or pool (a set of templates

with the same performance objective) at random and

Figure 4: Proﬁciency to weight mapping.

from this pool in turn randomly selecting a template.

To adapt the narrative to the player, we change the

probability distribution of the performance objective

pools to better match the problem areas of the player.

As discussed above the predicted model attempts to

estimate the current proﬁciency of the player. Each

proﬁciency matches a certain performance objective

and is expressed as a number between 0 and 1. From

these proﬁciencies we will automatically deduce the

appropriate probability distribution.

In order to inhibit the possibility of creating in-

appropriately large or inappropriately small weight

values when using a simple formula for the weights

(with proﬁciency as input), we introduce thresholds

to create weight-plateaus (see Figure 4) and avoid

performance objectives with a probability of nearly

0% or nearly 100%. If a proﬁciency ever enters one

of these plateaus, the performance objective will be

ﬂagged as such (either ’fail’ or ’pass’ can be used to

automatically detect noteworthy events for a supervi-

sor/teacher, especially in the case of ’fail’. Between

the thresholds, the weight value are linearly interpo-

lated, creating a probability distribution that is never

in favour or disfavour of a single performance objec-

tive. Selecting the scenarios using this methodology

will hopefully lead to a more balanced game/learning

experience.

6 RESULTS

To validate our concept we ran several simulations

of our system. The system uses 5 performance ob-

jectives, marked as PO1, PO2, PO3, PO4 and PO5

respectively. Each performance objective has a pool

of 30 scenario templates, making a total of 150 tem-

plates. A single run of the system triggers 100 scenar-

ios, predicting the player model after each event and

adjusting the probabilities accordingly. All result data

is the average of 1000 runs of the system.

A digital player model was created to simulate all

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528

Figure 5: Simulated player proﬁciency/progression.

Figure 6: Predicted player proﬁency over time.

interactions. The player is modelled to have proﬁ-

ciency in all 5 performance objectives as has been de-

scribed in subsection 5.2. Table 1 displays the pro-

ﬁciency of the player for each performance objective

and the matching base learning rate or α-value. In our

current model we assume that each time the player

interacts with a scenario he/she learns from it, repre-

sented as a small increase in proﬁciency (i.e. learning

rate).

Table 1: Player statistics.

Player PO1 PO2 PO3 PO4 PO5

Starting Proﬁency 0.15 0.5 0.8 0.9 0.95

Learning Rate 0.01 0.01 0.01 0.01 0.01

The proﬁciency of the simulated player is tracked

over time, as can be seen in Figure 5. Not surprising,

proﬁciency in all objectives rises. From the interac-

tion with the scenarios the story engine tries to predict

the player’s proﬁciencies. For the adaptation rate we

chose an α-value of 0.15. If the α-value is too low,

the prediction will reach the actual proﬁciency very

slowly; if the α-value is too high, the prediction will

become unstable.

The predicted player model over time is displayed

in Figure 6. The prediction takes about 20 events to

get a good approximate measure of the player’s pro-

ﬁciencies. The predicted proﬁciencies are all lower

than the actual proﬁciencies off the simulated player.

Figure 7: Prediction error over time.

Figure 8: Adaptation of performance objective picking

probability.

Figure 7 displays the absolute difference between the

predicted and actual proﬁciencies, i.e. error rates. The

average error rate drops to about 15%, this is a rea-

sonable value as a difference of under 20% would not

yield a signiﬁcant change in the resulting weight of

a performance objective, i.e. no major differences in

the resulting probability distribution.

Finally, the resulting weights of each performance

objective change the overall probability distribution

of template selection (see Figure 8). This new dis-

tribution shows that the story engine creates a per-

sonal game/learning experience without going into

extremes.

7 DISCUSSION

We reduced the technical complexity of writing sce-

narios for serious games by separating the modeling

scenario template language from the serious game im-

plementation. By decoupling the modeling and im-

plementation, a writer can develop a scenario with

only an abstract knowledge of the game world, and

does not need to concern him- or herself with the tech-

nical details of the game engine. A disadvantage of

course is that every character, location and item de-

scribed in the scenario template needs to be provided

in the game world.

The player model introduced in section 5 offers

GeneratingNon-linearNarrativeforSeriousGameswithScenarioTemplates

529

the possibility to test scenario templates in terms of

change and performance objectives. The current ex-

perimental learning rates will, however, need to be re-

ﬁned and validated during user testing.

A potential problem with the current implemen-

tation of the player-driven adaptation lies with antag-

onistic players. If a player selects the wrong option

at every turn of the scenario the proﬁciency level for

all performance objectives will drop and the scenario

will not advance. It is out of scope of our implemen-

tation to impose a deﬁnitive solution to this problem.

Within the current framework the player will have to

work hard to recover from this situation, which might

extend the duration of the game over practical limits.

In most games antagonistic play is avoided by pro-

viding the players with rewards if they make sufﬁcient

progress. This mechanism helps to retain players and

maintain player interest, however for a serious game

an even higher retention rate is required. Most serious

games are played under supervision so there is an op-

portunity to intervene in the case of antagonistic game

play. At any rate we provide a signal that a player is

deliberately failing the game and the decision on how

to act upon this information lies with the (pedagogi-

cal) domain experts.

8 CONCLUSIONS AND FUTURE

WORK

We presented the concept of a story engine to in-

troduce non-linear narrative using scenario templates

and aliasing. The aliasing approach facilitates the de-

sign process by using simpler building blocks (sce-

nario templates) as a way to create a dynamic experi-

ence with an individualized storyline inside the game.

Speciﬁc to the topic of serious games and learn-

ing is the creation of a player-driven narrative that

tests the player on his/her skills. This was achieved

through the mapping of performance objectives to

each scenario template using metadata and by creat-

ing a model of a player that is adapted towards.

As for future work, we foresee improvements in

several areas. The learning rate for the simulation is

set to a low and possibly unrealistic value. By com-

paring actual input from a player to our player model

we should be able to derive a better player and game

speciﬁc value. Further study is required to create an

algorithm that can deduce an approximate learning

value based on the user input.

Player evaluation is performed in a binary fashion,

to allow for a more diverse way of action we would

include a choice based system. This system would

give the player a set of choices (actions) to perform

based on an occurred event.

Our implementation is used to randomize the oc-

currence of unrelated events. However, to provide a

controlled narrative while still using such a system,

linked templates with smart selection are required. In

such a system the pool of available event templates

would be populated only by those events that can fol-

low the previous one, i.e. an event that should logi-

cally follow from a choice made by the player or a

completely unrelated event.

Our work focuses on the aliasing aspects of char-

acters. However, a story may also require geographic

bifurcations. E.g. the main character could choose a

different path to reach the destination. In this case a

scenario would not only contain aliases for characters

but also for geographic locations (for example hidden

caves, treasures, bridges, . . . ).

ACKNOWLEDGEMENTS

This work was funded by the IWT SBO Friendly AT-

TAC project (http://www.friendlyattac.be/).

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