Evaluating the Authoring Complexity of InteractiveNarratives with Interactive Behaviour Trees

Mubbasir Kapadia∗

Disney Research ZürichRutgers University

Fabio ZundETH Zürich

Jessica FalkETH Zürich

Marcel MartiETH Zürich

Robert W. SumnerDisney Research Zürich

ETH Zürich

Markus GrossDisney Research Zürich

ETH Zürich

ABSTRACTThis paper evaluates the use of Behavior Trees (BT) for au-thoring compelling narrative experiences with free-form userinteraction. We systematically study extensions to tradi-tionally BT representations, which decouple the monitoringof user input, the narrative, and how the user may influ-ence the story outcome – referred to as Interactive BehaviorTrees (IBT’s). By quantitatively evaluating the authoringcomplexity of BT formalisms with traditional story graphrepresentations, we show that IBT’s better scale with thenumber of story arcs, and the degree and granularity of userinput. Our theoretical estimate of authoring complexity iscorroborated with a qualitative user study, which confirmsthat subjects take lesser time with reduced effort to authornarratives using IBT’s. The subjective difficulty of IBT’s isalso lower than traditional story graphs.

Categories and Subject DescriptorsI.3.7 [Computer Graphics]: Virtual Reality; I.2.1 [ArtificialIntelligence]: Applications and Expert Systems—Games

Keywordsinteractive narratives, behavior trees, augmented reality

1. INTRODUCTIONInteractive narratives place users in immersive virtual worldswhere they become an integral part of an unfolding story.These users create or influence dramatic storylines throughtheir actions. The growing maturity of Augmented Reality(AR) technologies opens up a new host of interaction pos-sibilities and bridges the gap between the real world andvirtual content to create immersive experiences. However,the ability to author interactive narrative content has not

∗Email : mubbasir.kapadia@rutgers.edu

Figure 1: Augmented Reality application for Inter-active Narratives authored using Interac-tive Behavior Trees.

kept pace with the promise of AR technology. Modern andcompelling interaction has seldom been combined with deepnarrative experiences yet.

Computer games provide a prime example of interactive nar-rative: at predefined points in time, the player is asked tomake a choice or take a certain action, which influences thecourse of the story. The quality of the narrative experi-ence generally increases with the number of possible choices.However, the authoring complexity grows exponentially withthe number of choices. Therefore, to keep the content au-thoring manageable in strong and compelling narratives theinteraction is limited. This keeps the set of possible actionsto alter the story minimal, and hence the player experiencesonly little user agency to influence the outcome of the story.Alternatively, a narrative with more interaction possibilitiesis created but at the cost of a simple and trivial narrativestructure.

In this work we evaluate novel story authoring techniques,which overcome the problem of exponential growth of au-thoring complexity. Behaviour Trees (BT’s) [5] allow au-thoring complex (non-interactive) stories containing vari-ous branching story arcs. They are created in a modularand thus extensible and easily maintainable fashion. As ex-tension to BT’s, Interactive Behavior Trees (IBT’s) havebeen recently proposed and handle free-form user interac-tion, which is desirable in interactive narratives. IBT’s are

split into three subtress: one for monitoring the user input,one defining the narrative, and one defining how the usermay influence the story outcome. This empowers contentcreators to create compelling narratives involving free-formuser interactions with minimal authoring complexity.

We study the authoring complexity of designing interactivenarratives using BT formalisms and show that our IBT’s in-crease the modularity, reusability, and maintainability of au-thored narratives, in comparison to traditional approaches.Our theoretical estimate of authoring complexity is corrob-orated with a user study, which confirms that subjects takelesser time with reduced number of errors to author narra-tives using IBT’s. The subjective difficulty of BT’s is alsolower than traditional story graphs.

To demonstrate the potential of IBT’s, we author an inter-active narrative for an AR application where the user canfreely interact with both real and virtual content to progressthe narrative along a direction of their choosing. Fig. 1 de-picts the interaction between the user and an example ARapplication. The user experiences an unfolding narrativethrough the lens of his or her portable device where thevirtual characters are controlled using authored IBT’s. Sen-sors on the device are used to register different interactions,which branch the narrative in different directions. This al-lows the user to freely engage with the characters and be anactive participant in the story.

2. RELATED WORKInteractive narratives have been studied from many differ-ent perspectives [21] and we provide a brief review below.Scripted approaches [11, 12] describe stories as pre-definedaction sequences where small alterations often require far-reaching modifications of monolithic scripts. Improv [17]and LIVE [15] define rules, which govern how actors actbased on certain conditions. These systems are not designedto generate complicated agent interactions that dramaticallyimpact the story outcome. Facade [13] executes authoredbeats to manage the intensity of the story. A Behavior Lan-guage (ABL) [14] provides a generalized scripting languagefor single- or multi-character actions based on manually au-thored preconditions for successful action execution.

Dialogue Trees [18] and Story Graphs [4] accommodate userinteraction as discrete choices at key points in the authorednarrative. Behavior Trees (BT’s) are gaining popularity inthe computer gaming industry for designing the artificialintelligence logic for non-player characters [5, 6]. BT’s of-fer graphical constructs for authoring modular, extensiblebehaviors which can be extended to control multiple inter-acting characters [24]. For communication between nodes,BT’s rely on a blackboard [16], which is a centralized, flatrepository of data that can be accessed by nodes in the tree.Animation systems [28, 27] can be used to visualize the exe-cution of these behaviors on fully articulated virtual humans.

Computational tools have also been developed that auto-matically generate digital stories [10, 9]. Event-centric plan-ning [25, 26] plans in the space of pre-authored behaviortrees, thus mitigating the combinatorial explosion of plan-ning in the action space of individual character actions. Re-cent work explores the use of partial-order planning to pro-

vide a computational tool for authoring interactive narra-tives [8]. PaSSAGE [29] and the Automated Story Direc-tor [22] monitor a user’s experience through the story tochoose between scenes and character behavior. The focusof this work is to revisit and evaluate the underlying repre-sentations of the stories themselves, in order to reduce thecomplexity of authoring interactive narratives.

User Interaction. Rapid advances in interaction modal-ities [30] have helped bridge the divide between real andvirtual content where natural user interfaces are effectivelyinvisible and heighten the degree of immersion. However,the ability to author interactive narrative content that cantruly harness the power of these interaction experiences stillremains an open challenge. Augmented Reality [31] helpsbridge the gap between real and virtual content to create im-mersive experiences. Combining different input modalitiessuch as touch, gestures, and voice with the inherent cam-era control in AR applications empowers content creatorsto create new forms of interactive experiences that were notpossible before.

3. INTERACTIVE NARRATIVESWe identify three main requirements towards authoring free-form interactive narrative experiences:

1. Modular Story Definition. Complex interactivenarratives have many interconnected story arcs thatare triggered based on user input leading to widelydivergent outcomes. The complexity of authoring nar-ratives must scale linearly with number of story arcs,which can be defined in a modular and independentfashion.

2. User Interactions. User interaction should be free-form, and not limited to discrete choices at key stagesof the story, with far-reaching ramifications on the out-come of the narrative. Monitoring user input and storylogic should be decoupled to facilitate the modifica-tion of user interactions without requiring far-reachingchanges to the story definition.

3. Persistent Stories. The actions and interactionsbetween the user and characters over the entire courseof the narrative must persist and influence story pro-gression.

3.1 Behavior Trees for Narrative AuthoringBehavior Trees (BT’s) enable graphical and modular author-ing of complex narratives. Branching narratives can easilybe created using BT control nodes that connect multiplesubtress, which represent independent story arcs. Recentwork [24] facilitate the authoring of complex multi-actor in-teractions in a parametrizable fashion, enabling the reuse ofmodular plot elements, and ensures that the complexity ofthe narrative scales independently of the number of char-acters. These properties of BT’s make them ideally suitedfor authoring complex, branching narratives (Requirement1). However, BT’s are not suited to to handle free-forminteractions and persistent state (requirements 2 and 3).

Monitoring User Interactions. Fig. 2 illustrates a naiveapproach to monitoring user interactions in a BT. This re-

quires the subtree that monitors user input to be insertedinto the narrative tree at all points where user selects howthe narrative proceeds, thus producing a tight coupling be-tween the narrative definition and user interaction. Reduc-ing the number of instances in the narrative where user inputis monitored severely limits interactivity, while free-form in-teraction increases the complexity of the tree. Also, userinput can be monitored only at the granularity of a singlenode in the tree, and not at every frame. These complica-tions produce a tradeoff between complexity in the narrativeand degree of interactivity.

MonitorTap(input)

ThrowBall

PickUpBall

Sequence (AND)

NaiveUserInteraction(a1: Actor, a2: Actor, input: InputSignal)

Loop (until success)

Sequence (AND)a1

MonitorUserInput

PlayBall(a1, a2)

MonitorUserInputMonitorUserInput

Figure 2: Handling user interaction in a traditionalBehavior Tree. The MonitorUserInputsubtree is required at all points in the nar-rative tree where the user selects how thenarrative proceeds, resulting in unneces-sary large trees.

MonitorRub(input) Story

Selector (OR)

NaiveStatePersistance(a1: Actor, a2: Actor, input: InputSignal)

Story

PlayBallWithUser(a1, a2)

Sequence (AND)

PlayBall(a1, a2)

a1

Figure 3: A naive approach to handle state persis-tence in Behavior Trees.

State Persistence. Behavior Trees traditionally don’t haveany means of explicitly storing the past state of the charac-ters involved in the narrative, where the current state of thestory is implicit in the current active node. Fig. 3 illustratesa simple narrative where the bears choose to include theuser in the game of catch contingent on whether the userplayfully interacted with the bear at the beginning of thestory. A traditional BT requires a branch at the very begin-ning of the narrative where the presence or absence of theplayful rub produces two largely redundant tree definitions

with minor variations in its ending.

3.2 Interactive Behavior TreesTo address the challenges described above, Interactive Be-havior Trees facilitate free-form user interaction and statepersistence. We refer the readers to [8] for a detailed sum-mary of IBT’s and provided a brief overview below.

IBT’s are divided into 3 independent sub-trees that are con-nected using a Parallel control node. An IBT

tIBT = 〈tui, tstate, tnarr = {tarci |tarc1 . . . tarcm }, β〉 (1)

comprises the following subtrees: (1) Story Subtree. tnarris responsible for handling the narrative progression and isfurther subdivided into subtrees {ai} that represent a sepa-rate story arc. (2) MonitorUserInput Subtree. tui mon-itors the different interactions that are available to the userand can be easily changed depending on the application ordevice. Since tui is executed in parallel with the other sub-trees, IBT’s are able to immediately respond and registerthe interactions of the user and use it to influence the nar-rative outcome. (3) MonitorStoryState Subtree. tstatemonitors the state of the story to determine if the currentstory arc needs to be changed.

The overall design of the IBT results in three subtrees thatexecute independently in parallel with one another. A black-board β is used to store the states of the characters and thestory and is used to communicate between the subtrees andalso to maintain state persistence. tui updates β when anyinput signal is detected, which is queried by tstate to deter-mine the current state of the story, and the active story arc.tstate contains separate subtrees for each story arc whichchecks if the precondition for the particular arc is satisfied.If so, β is updated to reflect the newly activated story arcwhich is used to switch the active story in tnarr.

4. AUTHORING COMPLEXITY

4.1 Cyclomatc ComplexityCyclomatic complexity [7] quantifies the number of linearlyindependent paths through the program by measuring thenumber of branches in the code, and serves as a standardcriteria for optimizing the testability and maintainability ofthe code without the need for dynamic code analysis. Cyclo-matic complexity estimates the number of decisions made bythe source code where a complex, often poorly written pro-gram with many branches leads to a high value of cyclomaticcomplexity, indicating that the program is difficult to test,maintain and prone to errors. By analogy, narratives thataccount for the different ways a user may influence the storyoutcome require many decision points, and are complex toauthor. For this reason, we use cyclomatic complexity toquantify authoring complexity for interactive narratives.

Cyclomatic complexity, c(g) is computed by first convertingthe program into its equivalent control flow graph (CFG)representation g, where the nodes correspond to atomic com-mands, and the directed edges connect commands that ex-ecute in sequence. c can be calculated in a variety of ways,including:

c(g) = p(g) + 1 (2)

where p(g) is the number of decision points (e.g., if, while, ...etc.) in the program. However, Eq. 2 assumes that there is asingle termination point in the program which is not the casewith BT’s where every node may terminate with success orfailure. Additionally, there is a third “runnning” state thatis returned at each frame while the node is still executing.To generalize the measure of c(g) for multiple terminationpoints, we use a modified equation shown below where s(g)denotes the number of exit points in g.

c(g) = p(g)− s(g) + 2 (3)

4.2 Computing Cyclomatic Complexity for Be-havior Trees

Fig. 4 illustrates the equivalent control flow representationsfor the different control nodes used for defining behaviortrees, which are used to compute its cyclomatic complex-ity. All subsequent calculations of c(.) assuming that thebehavior trees are converted to their equivalent control flowgraphs.

Leaf Node. A leaf node tleaf represents an atomic com-mand in a BT which returns either success or failure. If itis in the “running” state, it continues executing itself until itsucceeds or fails. Fig. 4(a) shows the CFG for a leaf node.Depending on the number of return states in the particu-lar leaf node implementation, we have p(tleaf) = {0, 1, 2}and s(tleaf) = {1, 2}. If the leaf node immediately returnssuccess or failure without using the running state, we havec(tleaf) = 1. If the leaf node can enter the running state,the complexity is c(tleaf) = 2.

Sequence Node. A sequence node tseq returns failureif any one of its child nodes fails, else it returns success.If a child returns “running”, it simply continues executingthis child until it has reached failure or success. Fig. 4(b)illustrates the CFG for tseq. To calculate c(tseq) of tseq witha set of m child nodes {ti|t1, t2 . . . tm}, we need to considerthat each child node may be its own subtree with multipledecision points.

p(tseq) =

m∑i=1

p(ti), s(tseq) = 2,

∴ c(tseq) =m∑

i=1

p(ti) (4)

Selector Node. The selector node tsel returns success assoon as its first child node returns success, and produces asimilar control flow graph as compared to tseq (Fig. 4(c)).Hence, c(tsel) = c(tseq).

Loop Node. The loop node tloop is used to repeatedlyexecute its child node tc until a certain condition is met. Aloop node may only have a single decorator or leaf node as its

child and does not define how to traverse through multiplechildren. The following termination conditions may be used:(1) loop until success, (2) loop until failure, (3) loop N times,(4) loop forever. Fig. 4(d) illustrates the loop node whichterminates when its child node returns success. It has onlyone termination node, and an additional decision point isintroduced for looping.

p(tloop) = 1 + p(tc), s(tloop) = 1,

∴ c(tloop) = p(tc) + 2 (5)

The same calculations apply for a loop node that repeatsuntil failure. For tloop which repeats a fixed number of times.

p(tloop) = 1 + p(t), s(tloop) = 2,

∴ c(tloop) = p(t) + 1 (6)

Fig. 4(e) illustrates a loop node that never returns. Sincethe node never terminates and has neither a decision pointnor an exit point, we only need to consider the child of theloop node.

p(tloop) = p(t), s(tloop) = 0,

∴ c(tloop) = c(t) (7)

Parallel Node. The parallel node tpar executes its childnodes in parallel and has two types depending on the ter-mination condition: (1) Selector Parallel: It executes untilany child node returns success or all of them return failure.(2) Sequence Parallel: It executes until any child node re-turns failure or all of them succeed. Fig. 4(f) illustrates thecontrol flow graph of a selector parallel node. An additionalnode “Sync” is used to symbolize the synchronization bar-rier between the child nodes and termination. The sequenceparallel node exhibits similar behavior and both their com-plexity measures can be calculated as shown below.

p(tpar) = 2 ·m∑

i=1

p(ti), s(tpar) = 2,

∴ c(tpar) = 2 ·m∑

i=1

p(ti) (8)

4.3 Complexity ComparisonUsing the complexity measure described above, we comparethe authoring complexity of traditional story graphs, naiveBT definitions for interactive narratives, and IBT’s.

Story Graphs. A story graph gs is a directed graph wherethe vertices correspond to story atoms during which the userhas no outcome on the narrative, and the edges represent adiscrete set of choices that the user has to influence how thestory ends. A linear narrative represents a lower bound onc(gs) = 1 with no decision points. For interactive narratives

A

Leaf Node Sequence (AND)

A B

Selector (OR)

A B A

Loop (until Success)

A

Loop (forever) Selector Parallel

A B

II

A

Failure SuccessB

Failure Success

A

B

Failure Success

AA

Success

Failure A

Success

FailureA

Success Sync

B

Failure

(a) Leaf (b) Sequence (c) Selector (d) Loop until success (e) Loop Forever (f) Selector Parallel

Figure 4: Control flow graphs for the different control nodes used in Behavior Trees.

however, an upper bound on the complexity represents abranching story graph where all possible user interactionsare possible at each stage in the story, which is supportedby IBT’s.

For a story graph gs with m story arcs {ai|a1 . . . am}, eachwith |ai| number of nodes, and d possible user interactions,there will be a maximum of d +1 outgoing edges per node ings. Therefore, each node represents d decision points. Ad-ditionally, the number of exit points depends on how manystory arcs can end the story 1 ≤ s(gs) ≤ m. We calculatec(gs) as follows:

c(gs) = d ·

(m∑

i=1

|ai|

)− s(gs) + 2 (9)

Behavior Trees (Naive Approach). Fig. 2 illustratesthe naive approach to authoring interactive narratives us-ing BT’s. This BT tBT = 〈tmu, {ai|a1 . . . am}〉 comprises asubtree tmu which monitors user input and story branching,and m story arc subtrees, where ai represents the ith arcwith |ai| nodes. However, these subtrees are tightly cou-pled together as the user input must be monitored betweeneach node of each story arc to ensure free-form user inter-action. The number of decision points for each story arc ai,p(ai) = |ai| · p(tmu) + p(ai), and tBT has two exit points.Hence, the cyclomatic complexity c(tBT) is calculated asfollows:

c(tBT) =

m∑i=1

(|ai| · p(tmu) + p(ai)) (10)

Interactive Behavior Trees. The benefit of the IBT for-malism is that the 3 subtrees 〈tui, tstate, tnarr〉 are all inde-pendent to each other and run in parallel using a ParallelSequence node. Additionally, each subtree has a Loop for-ever node at its top, Hence, we can consider each subtree asan independent program and the total complexity c(tIBT)can be computed by adding the complexities of each inde-pendent subtree.

MonitorUserInput Subtree. tui monitors all possi-ble user interactions which sets the appropriate state in the

blackboard β. In our proposed solution, all the leaf nodesthat monitor the respective user inputs were modified to onlyreturn success and thus have no decision points. tui simplychecks the different interactions in a sequence without anybranching. As a result, c(tui) = 1.

MonitorStoryState Subtree. tstate checks if all thepreconditions for each arc are satisfied and sets the currentstory arc if needed. For li nodes per story arc ai, (li − 1)of those node are assertion nodes that represent a decisionpoint and the last one sets the current story arc. Hence, thecomplexity c(tstate) is calculated as follows:

c(tstate) =

m∑i=1

(li − 1) (11)

Story Subtree. tnarr = {tarci |tarc1 · tarcm } consists of a sub-tree for each story arc ai which additionally checks if it isthe current story arc before proceeding to execute the nar-rative. This ensures that story arcs can be switched seam-lessly at any point in the narrative. This introduces 2 moredecision points per arc, in addition to the arc complexity.The number of decision points p(tarci ) for the ith story arc,p(tarci ) = 2 · (2 + p(ai)). The resulting complexity, c(tnarr):

c(tnarr) = 4 ·m +

m∑i=1

(2 · p(ai)) (12)

The overall complexity c(tIBT) is calculated as follows:

c(tIBT) = c(tui) + c(tstate) + c(tnarr)

= 1 + 4 ·m +

m∑i=1

(2 · p(ai) + (li − 1))(13)

Conclusion. Eq. 9 shows that the number of user interac-tions and the number of nodes in the story arcs have a multi-plicative effect on the authoring complexity for story graphs.This limits content creators to choose between freedom of in-teraction and the complexity of the narrative to prevent theauthoring complexity from becoming prohibitive. In con-trast, the number of decision points in the story arc have alinear effect on c(tIBT), while the number of ways a user can

interact has no impact on the authoring complexity. Thisallows content creators to create compelling interactive nar-rative experiences with free-form user interaction, withoutbeing burdened by limitations in the specification language.

5. APPLICATIONWe developed an interactive narrative application in an ARsetting, authored using IBT’s, where the user can freely in-teract with the characters in the story (two bears) and hisinteractions influence the outcome of the narrative.

5.1 Augmented Reality FrameworkAugmented Reality (AR) applications benefit from intuitiveand versatile input mechanisms where the user can seem-ingly physically interact with the virtual world. For see-through AR applications, the physical movement of the mo-bile device serves as a direct means of exploring the digitalcontent superimposed in a real environment, where, not onlycan the user interact with the virtual characters using thehost of sensors available on the device, but the virtual char-acters can interact with the physical world as well.

Implementation. We used a natural image-based trackingapproach [1], which registers the 2D marker image and esti-mates the camera location and pose in real-time for stabletracking. We used the implementation provided by Vufo-ria [19]. Additional image markers can also be tracked andused as triggers in the AR application, for example, to in-stantiate new objects in the world, which may branch thenarrative in a different direction. The game application wasimplemented using the Unity3D game engine using a data-driven character animation system. The animation func-tionality is exposed to the author using a set of routinesincluding LookAt(obj), Reach(target) etc., which can beinvoked from BT’s. For more details of the animation sys-tem, please refer to [28]. The narrative was authored us-ing an extended version of the BT library described in [24].The current version of the game was deployed and testedon multiple portable devices including Sony Xperia TabletZ, Apple iPad 3rd generation, Apple iPad Mini Retina, andApple iPad Air.

Interaction Vocabulary. Using the sensors available onthe mobile device, the user can interact with the virtualcharacters in the following ways: (1) Moving the device tofocus on different objects and characters. (2) Tapping on ob-jects and characters to pick them up or interact with them.(3) Shaking the device. (4) Gestures to communicate userintent. (5) Using image markers to trigger objects (e.g., ahoney pot sticker can be used to create a honey pot for thebears) in the world. The mode and effect of interactions iscompletely decoupled from the narrative and can be easilychanged depending on the platform used without impactingthe narrative definition.

5.2 Story DefinitionWe author an interactive narrative involving two bears. Weprovide a brief description below and refer the readers to thesupplementary video for more details.

Playing Catch. The first bear B1, enters the scene andlooks up at the player with curiosity. The player can ex-periment with the bear by trying out different interaction

possibilities including rubbing the bear, asking him to twirlusing a circular gesture, tapping him to attract his atten-tion, or zooming in to get a closer look. Soon, the secondbear B2 enters and asks B1 for a beach ball so they can playcatch. B1 is unable to find a ball and turns to the playerfor help. The player may choose to give a soccer ball to thebears but they only want to play with the beach ball. De-pending on where the player throws the beach ball, differentbranches of the story are triggered where giving the ball toB1 enables him to help his friend.

State Persistence. The user’s choices have ramificationslater on in the story. For example, B1 remembers if theplayer rubbed him at the beginning and includes him in thegame by throwing him the ball. If the player chooses notto interact with the bear, the bears are less friendly andexclude him from the game of catch.

Additional Interactions. At any point, the player mayuse a honey pot sticker to trigger a honey pot in the world.The bears who are obsessed with honey leave aside whateverthey are doing, even their beloved ball, and make a beelinetowards the honeypot which represents one possible conclu-sion of the story. A more mischievous player may choose totrigger bees into the world which chase the bears and disrupttheir game of catch. Only flowers may then be used to dis-tract the bees and save the bears. These different story arcsare authored as modular, independent units in the IBT andcan be triggered at any stage without the need for complexconnections and state checks in the behavior tree definition.

Freedom of Interaction. The above narrative representsa very simple baseline to demonstrate the ability to authorcompelling interactive narrative experiences. Unlike tradi-tional approaches where the user was limited to discretechoices at certain stages in the narrative, IBT’s empowerthe player with complete freedom of interaction where hemay choose to play ball with the bears, give them honey, orsimply wreak havoc by releasing a swarm bees at any pointof time. The interactions elicit instantaneous and plausibleinteractions from the characters while staying true to thenarrative intent.

6. USER STUDYWe conducted a preliminary user study to assess the in-fluence of the authoring methods on the user’s authoringperformance. Each subject was asked to implement a pre-defined, moderately complex narrative using two methods:(1) Story Graphs SG and (2) Interactive Behavior Trees IBT.We used a basic implementation of story graphs where nodesrepresent the execution of one or more affordances in se-quence, and edges represent a condition on the status of theprevious node, or a user interaction. Recent extensions [23]encapsulate state machines within individual graph nodesto create more complex narrative atoms. However, sinceour goal is to allow the user to interact at any point in thestory, narrative atoms in story graphs are restricted to theset of available affordances.

6.1 MethodAll subjects carried out the study on a similar PC with twomonitors, where they used an extended version of the Unitygame engine for authoring. Both methods were implemented

within the same GUI to mitigate the influence of the inter-face on the analysis. Each subject was first provided witha tutorial document to introduce each method and get ac-customed to the application. For each tutorial, the subjectcould freely interact with the software and experiment withits functionality without a time limit. Following each tuto-rial, the subject was asked to author the narrative describedbelow during which a variety of metrics were measured andlogged.

Narrative. The subjects were presented with the follow-ing textual description of the narrative, which they had toauthor using each method. While relatively simple, the nar-rative exercises the use of multiple interconnected story arcsand freedom of user interaction. (1) Start Arc: Bear1 entersthe scene. Bear1 is bored. After 2 seconds Bear1 realizesthat the user is watching and waves at him. Bear2 entersthe scene. (2) Conversation Arc: Bear2 greets Bear1. Theystart arguing. After 10 seconds Bear2 apologizes to Bear1.The story ends. (3) Bee Arc: The player may trigger thebees at any time. If the bees are in the scene, the bears runaway from the bees and completely ignore any other storyelements or user interactions. (4) Flower Arc: The userscan spawn flowers at any time. If flowers are in the scene,the bees (if present) stop chasing the bears and fly to theflower. The bears are then free to continue with the previ-ously active story arc, or respond to other user interactions.

6.2 MetricsIndependent Variables. The method of authoring andauthor proficiency are treated as independent variables (IV)in our experiment. Subjects were instructed to author thesame narrative using the 2 methods: Story Graphs SG andInteractive Behavior Trees IBT. The order in which the userauthored the story using each method was chosen at random.At the beginning of the study, the subject was asked to selecthis proficiency as an expert who has previous experience withBT’s, or a novice with little or no experience.

Dependent Variables. We logged a variety of metricsto capture the authoring performance, which are treated asdependent variables (DV) in our experiment. (1) We mea-sured the time in minutes ta needed to author the narrativefor each method. (2) We counted the number of mouse clicksnc during each authoring session as an estimate of the effortto author a story. (3) For each authoring method, the userwas asked to rate its subject difficulty ds as a nominal valuefrom 1 (very easy) to 5 (very hard).

6.3 User Study ResultsWe recorded 12 subjects at the age between 21 years and 35years, 90 % male. They were Computer Science students (4undergraduate, 8 graduate) and were proficient with usingthe Unity editor. Additionally, expert users had previousexperience with Behavior Trees. All subjects (6 experts,6 novice users) authored the same story using both meth-ods. A MANOVA was conducted with Method and Pro-ficiency as independent variables, with ta, nc, and ds asdependent variables, and with the user’s ID as covariate.

Findings revealed a statistically significant difference in userperformance based on the Method prior (Roy’s LargestRoot = 6.282, F(3, 17) = 35.587, p < 0.001, partial η2 =

0.863) as well as based on the Proficiency prior (Roy’sLargest Root = 5.525, F(3, 17) = 31.309, p < 0.001, par-tial η2 = 0.847). Furthermore, the multivariate model ex-hibits high R2 values: ta: R2 = 0.888, nc: R

2 = 0.571,ds: R2 = 0.785.

The univariate effects of Method and Proficiency aresummarized in Table 1. The estimated means for both IVsare depicted in Table 2. Fig. 5 depicts the correlation ma-trix with Pearson’s Correlation coefficients for all IVs andDVs. Red coefficients indicate a significant (α = 0.05) cor-relation. The strongest significant correlation is observedbetween Method and ds (r = −0.87).

Table 1: Univariate tests analysis for Method andProficiency.

IV DV df Error df F p

Method ta 1 19 30.320 < 0.001nc 1 19 11.761 0.003ds 1 19 67.308 < 0.001

Proficiency ta 1 19 103.665 < 0.001nc 1 19 11.472 0.003ds 1 19 0.731 0.403

Table 2: Estimated means for the multivariatemodel (Method and Proficiency interac-tion). Lower and upper bounds (LB, UB)are provided for 95% confidence intervals.

DV Proficiency Method LB Mean UB

nc Expert IBT 131.52 367.16 602.81SG 654.18 889.83 1125.48

Novice IBT 649.86 885.50 1121.15SG 899.19 1134.84 1370.48

ta Expert IBT 12.87 25.52 38.16SG 46.04 58.68 71.32

Novice IBT 74.34 86.99 99.63SG 107.68 120.32 132.96

ds Expert IBT 2.25 2.83 3.40SG 4.42 4.99 5.57

Novice IBT 1.93 2.51 3.08SG 4.27 4.84 5.42

Interpretation of Results. The results clearly show thatindependent of the user’s proficiency with IBT’s, there isa significant decrease in authoring time, number of clicks,and subjective authoring difficulty when comparing SG andIBT. The novice users consistently performed worse thanthe expert users, which confirms that our performance met-rics were appropriate. Finally, while the method indicatesa strong effect on the subjective difficulty, no significant ef-fects of the subject’s proficiency with IBT’s on the subjectivedifficulty could be measured.

The study yielded positive results. However, it must benoted that the scope of the study was limited due to thesignificant amount of time it took to author even the mod-erately complex narrative described above. The averagetime to complete the study for the novice group was over2 hours. More complex narratives and a larger user baseare needed to create more substantial results. Furthermore,the quality of the tutorial documents explaining the meth-ods have certainly a strong influence on how well the sub-jects perform with that method. Yet it is inherently difficultto teach all methods to exactly the same extent. In a fu-

0.00 −0.52 −0.42 −0.87

0.00 0.50 0.80 −0.10

36.005.025.0− 0.37

−0.42 0.80 0.63 0.24

−0.87 −0.10 0.37 0.24

Method Proficiency tanc ds

Met

hod

Prof

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nc

ds

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Figure 5: Correlation matrix for the IVs and DVsdepicting Pearson’s Correlation Coeffi-cients. A red coefficient indicates a statis-tically significant correlation. The plotson the diagonal depict a histogram foreach distribution.

ture study, maybe carefully created video tutorials for eachmethod might mitigate the effect of training on the studyoutcome. Our user study confirms the theoretical measuresof authoring complexity that were described earlier.

7. DISCUSSION AND FUTURE WORKIn this paper, we demonstrate the benefits of using Interac-tive Behavior Trees for authoring free-form interactive nar-ratives. The hierarchical, graphical nature of BT’s makesthem suitable for authoring complex narratives with branch-ing story arcs in a modular, extensible fashion. By decou-pling the monitoring of user input and the narrative defini-tion, users who experience the narratives can freely interactwith the characters in the story, limited only by their cre-ativity and interaction device. This enables content creatorsto author compelling interactive narrative experiences withfree-form user interaction.

We calculate the authoring complexity of authoring narra-tives by mapping different narrative specifications to theircorresponding control flow representations, which can beused to compute their cyclomatic complexity. Comparingthe cyclomatic complexity of IBT’s vs. traditional storygraphs reveals that IBT’s scale better with the number ofstory arcs, and degree and granularity of interactions. Thismakes them suitable for authoring complex narratives withfree-form user input. Our user study confirms the premisethat users, be it expert users, who are familiar with IBT’s, ornovice users, spend in average less time, require less effort,and rate it subjectively easier to author a predefined narra-tive employing IBT’s compared to traditional story graphsin our Unity authoring editor.

Limitations and Future Work. IBT’s enable content cre-ators to easily author complex interactive narratives withoutbeing burdened by limitations in the specification language.However, all the story arcs and responses to different user

inputs need to be authored beforehand and there are noemergent responses to unforeseen situations which may oftenarise in interactive applications. There is a growing trendto use automated narrative tools [21] to produce emergentinteractive experiences. Recent work [8] explores the useof automated narrative tools to assist content creators tohandle all possible user interactions during the authoringprocess. For future work, we would like to study the po-tential benefits and tradeoffs of automation for authoringinteractive narratives.

The cyclomatic complexity provides an estimate of the num-ber of decision points in the authored story, but does notaccount for the number of decisions needed while author-ing the narrative. Additional measures [2] that account forthe high-level structure of a story may also be used to com-plement this analysis. The use of story graphs provides asuitable baseline for comparing the benefits and tradeoffs ofIBT’s for interactive narratives. For future work, we wouldalso like to compare IBT’s with many additional alterna-tives including hierarchical task networks [3], narrative me-diation [20], and other planning based approaches.

8. REFERENCES[1] F.-e. Ababsa and M. Mallem. Robust camera pose

estimation using 2d fiducials tracking for real-timeaugmented reality systems. In ACM SIGGRAPHVRCAI, pages 431–435, 2004.

[2] S. Chen, M. J. Nelson, and M. Mateas. Evaluating theauthorial leverage of drama management. InProceedings of the Fifth Artificial Intelligence andInteractive Digital Entertainment Conference, AIIDE2009, October 14-16, 2009, Stanford, California, USA,2009.

[3] K. Erol, J. Hendler, and D. S. Nau. Htn planning:Complexity and expressivity. In Proceedings of AAAI,pages 1123–1128. AAAI Press, 1994.

[4] A. Gordon, M. van Lent, M. V. Velsen, P. Carpenter,and A. Jhala. Branching Storylines in Virtual RealityEnvironments for Leadership Development. In AAAI,pages 844–851, 2004.

[5] C. Hecker, L. McHugh, M. Argenton, and M. Dyckho.Three approaches to halo-style behavior tree ai. InGame Developers Conference, 2007.

[6] D. Isla. Halo 3: Building a better battle. In GameDevelopers Conference, 2008.

[7] G. Jay, J. E. Hale, R. K. Smith, D. P. Hale, N. A.Kraft, and C. Ward. Cyclomatic complexity and linesof code: Empirical evidence of a stable linearrelationship. JSEA, 2(3):137–143, 2009.

[8] M. Kapadia, J. Falk, F. Zund, M. Marti, R. W.Sumner, and M. Gross. Computer-assisted authoringof interactive narratives. In Proceedings of the 19thSymposium on Interactive 3D Graphics and Games,i3D ’15, pages 85–92, New York, NY, USA, 2015.ACM.

[9] M. Kapadia, A. Shoulson, F. Durupinar, andN. Badler. Authoring Multi-actor Behaviors in Crowdswith Diverse Personalities. In Modeling, Simulationand Visual Analysis of Crowds, volume 11, pages147–180. 2013.

[10] M. Kapadia, S. Singh, G. Reinman, and P. Faloutsos.

A behavior-authoring framework for multiactorsimulations. Computer Graphics and Applications,IEEE, 31(6):45 –55, nov.-dec. 2011.

[11] A. B. Loyall. Believable agents: building interactivepersonalities. PhD thesis, Pittsburgh, PA, USA, 1997.

[12] M. Mateas. Interactive drama, art and artificialintelligence. PhD thesis, Pittsburgh, PA, USA, 2002.

[13] M. Mateas and A. Stern. Integrating plot , characterand natural language processing in the interactivedrama facade. In TIDSE, volume 2. 2003.

[14] M. Mateas and A. Stern. A behavior language: Jointaction and behavioral idioms. In Life-Like Characters,pages 135–161. Springer, 2004.

[15] E. Menou. Real-time character animation usingmulti-layered scripts and spacetime optimization. InICVS, pages 135–144, London, UK, 2001.Springer-Verlag.

[16] I. Millington and J. Funge. Artificial Intelligence forGames, Second Edition. Morgan Kaufmann PublishersInc., San Francisco, CA, USA, 2nd edition, 2009.

[17] K. Perlin and A. Goldberg. Improv: a system forscripting interactive actors in virtual worlds. InProceedings of ACM SIGGRAPH, pages 205–216, NewYork, NY, USA, 1996. ACM.

[18] H. Prakken and G. Sartor. Argument-based extendedlogic programming with defeasible priorities. Journalof Applied Non-classical Logics, 7:25–75, 1997.

[19] Qualcomm. Vuforia Developer SDK, 2010.

[20] M. Riedl, C. J. Saretto, and R. M. Young. Managinginteraction between users and agents in a multi-agentstorytelling environment. In Proceedings of the SecondInternational Joint Conference on Autonomous Agentsand Multiagent Systems, AAMAS ’03, pages 741–748,New York, NY, USA, 2003. ACM.

[21] M. O. Riedl and V. Bulitko. Interactive narrative: Anintelligent systems approach. AI Magazine,34(1):67–77, 2013.

[22] M. O. Riedl, A. Stern, D. Dini, and J. Alderman.Dynamic experience management in virtual worlds forentertainment, education, and training. InternationalTransactions on Systems Science and Applications,Special Issue on Agent Based Systems for HumanLearning, 4(2):23–42, 2008.

[23] M. O. Riedl and R. M. Young. From linear storygeneration to branching story graphs. IEEE CGA,26(3):23–31, 2006.

[24] A. Shoulson, F. M. Garcia, M. Jones, R. Mead, andN. I. Badler. Parameterizing behavior trees. In MIG,pages 144–155. Springer-Verlag, 2011.

[25] A. Shoulson, M. L. Gilbert, M. Kapadia, and N. I.Badler. An event-centric planning approach fordynamic real-time narrative. In MIG, pages99:121–99:130, 2013.

[26] A. Shoulson, M. Kapadia, and N. Badler. PAStE: APlatform for Adaptive Storytelling with Events. InINT VI, AIIDE Workshop, 2013.

[27] A. Shoulson, N. Marshak, M. Kapadia, and N. Badler.ADAPT: The Agent Developmentand PrototypingTestbed. IEEE TVCG, 20(7):1035–1047, July 2014.

[28] A. Shoulson, N. Marshak, M. Kapadia, and N. I.Badler. Adapt: the agent development and

prototyping testbed. In ACM SIGGRAPH I3D, pages9–18, 2013.

[29] D. Thue, V. Bulitko, M. Spetch, and E. Wasylishen.Interactive storytelling: A player modelling approach.In AIIDE, 2007.

[30] D. Wigdor and D. Wixon. Brave NUI World:Designing Natural User Interfaces for Touch andGesture. Morgan Kaufmann Publishers Inc., SanFrancisco, CA, USA, 1st edition, 2011.

[31] F. Zhou, H.-L. Duh, and M. Billinghurst. Trends inaugmented reality tracking, interaction and display: Areview of ten years of ismar. In Mixed and AugmentedReality. ISMAR 2008, pages 193–202, Sept 2008.