42
Techniques for AI-Driven
Experience Managementin Interactive Narratives
Vadim Bulitko, Greg Lee, Sergio Poo Hernandez,
Alejandro Ramirez, and David Thue

42.1 Introduction
42.2 AI-Driven Experience
Management
42.3 AI-Driven Experience
Management: Common
Techniques

42.4 Implementations
42.5 Conclusion and Future
Work
References

42.1 Introduction
Recently, there has been a trend to include open-world gameplay in story-rich video games(e.g., [Petit 14]). Giving the player agency can lead to his or her affecting the original story
in a way that the game designers may not have anticipated. To illustrate, imagine a gameversion of the Little Red Riding Hood story [Perrault 1697, Grimm 1812]. Suppose that theplayer, controlling Red, kills the wolf early in the game. The rest of the story is then broken, since the wolf is unable to eat the grandmother.
This chapter describes several artificial intelligence (AI) techniques that can help gamedesigners provide players with more agency while still maintaining a desired narrative
feel for their games. In the example of Red killing the wolf, the AI techniques can automatically revise the story upon the wolf’s demise (e.g., another predator appears) so thatcertain authorial goals are maintained (e.g., the grandmother is eaten).

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42.2 AI-Driven Experience Management
The task we consider in this chapter is that of managing a player’s experience, driven by
AI. This is an expansive area in which several approaches have been proposed, and it offers
several avenues to explore [Riedl 13]. The key idea is to create an AI game master (AI GM),
which is bundled with every copy of the game. As the player proceeds through the narrative, making narrative choices, the AI GM monitors the player’s actions and dynamicallymodifies the story in a way that the story designer would have wanted. In this chapter, we
use the terms story designer and story author interchangeably, and this view of interactive
storytelling is also known as delayed authoring [Thue 08].
For the sake of consistency with existing literature, we also use the terms AI GM and AI
experience manager interchangeably throughout the chapter. Computationally, we viewexperience management as an optimization problem where the AI GM attempts to optimize the player’s experience according to a designer’s specified objectives [Thue 12]. We
detail our approach in the following sections.
42.2.1 Player in a Game Environment
If a game narrative is going to be modified by AI, it first needs to be represented in acomputer-readable format. This means that the narrative state (e.g., Red just met the wolf)
and all actions available to change the state (e.g., shoot the wolf or reason with the wolf)
need to be formally described. While numerous methodologies have been used to represent game worlds in an AI-readable format, in this article, we will use the Planning
Domain Definition Language (PDDL) [McDermott 98]. Listing 42.1 illustrates the PDDL
encoding of the action eat, which one game character may do to another in the Little Red
Riding Hood story.
Note that the action is represented in a programming-like notation with variables(e.g., ?eater) and predicates (e.g., alive). It says that the eater can eat the eatee
if the eater is a predator, the eatee is a person, the eater knows the eatee, and theeater is hungry. They both must be alive and the eatee cannot yet be eaten. Once theaction is applied, the eatee is eaten and is located inside the eater, and the eater is nolonger hungry. Table 42.1 illustrates an application of this action.

Listing 42.1. An action template that can be instantiated into an in-game action.
(define (action eat):parameters (?eater ?eatee):precondition (and (knows ?eater ?eatee)(predator ?eater)(alive ?eatee)(alive ?eater)(not (eaten ?eatee))(hungry ?eater)(person ?eatee)):effect (and (eaten ?eatee)(in ?eatee ?eater)(not (hungry?eater))))524

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Table 42.1 Changes in Game Narrative State Caused by an Action
Current State(person red)(alive red)
(predator wolf)(alive wolf)(hungry wolf)(knows wolf red)(knows red wolf)

Action(eat wolf red)

New State(person red)(alive red)
(predator wolf)(alive wolf)(not (hungry wolf))(knows wolf red)(knows red wolf)(in red wolf)(eaten red)In this example, the action’s variables eater and eatee are bound to the wolf andRed, respectively. The current state is shown in the first column of Table 42.1. It is represented with a series of facts (e.g., Red is a person and the wolf is a predator) and satisfies thepreconditions of the action. Once the action is applied in the current state, the new state is
generated with the new facts shown in bold.
42.2.2 AI Manager Modifying Narrative
Once the narrative state and the player’s actions are represented in a computer-readable
format, the AI can reason over the narrative and modify the game world in response to
the player’s actions on the fly. To do so, the AI GM needs to know what the author wants
to happen in the story.
Traditionally, games encode nonlinear narrative as story graphs (or story trees). The
game engine selects the appropriate branch based on story tokens (e.g., whether the player
saved Ashley or Kaiden in Mass Effect [BioWare 07]). An alternate approach to specifying
the author’s wishes is through PDDL-described game facts. Figure 42.1 depicts a skeletonof the interactive narrative for the Little Red Riding Hood [Ramirez 13] where the story
author has two goals: (1) a predator should eat both Red and her grandmother, and (2) Red
should give her grandmother a cake (shaded nodes in the story graph on the left).
When the player kills the wolf, both (eat predator red) and (eat predator
granny) are invalidated as the wolf is dead and unable to eat anyone. The AI manager
can then engage an automated planner to satisfy these goals from the current state. Given
the action templates that are available, the planner may come up with two stories. In one,another predator (named Grendel) greets the player and takes over from the deceased
wolf. In the other, a magic fairy resurrects the original wolf who can then resume his eating duties.
42.2.3 Choosing between Stories
Whenever several stories are available that satisfy the authorial goals, the AI GM needs todecide which one to run. To choose among them, the game master can rely on the author’s
specification of an ideal player experience. For instance, suppose that the author wants tomaximize the player’s enjoyment of the story. Suppose also that on the basis of the player’s
previous actions, the AI experience manager believes the player to have an inclination
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(Greet red wolf )(Kill red wolf )(Talk-to red wolf )?

(Eat predator red)

(Eat predator granny)(Greet red grendel)(Resurrect fairy wolf )(Kill hunter predator)(Escape granny)(Escape red)

(Give red granny cake)(a)

Exemplar narrative(b)

Planning

Figure 42.1
Author’s desired events (a) and AI experience manager’s actions (b). The arrows show possible temporal relations between the events.

toward combat. Then in deciding between two possible narratives in Figure 42.1, themanager may decide that introducing Grendel—effectively a bigger, meaner version of theoriginal wolf—is a more suitable option than presenting a resurrecting fairy. The manager
then runs the narrative content with Grendel until the player takes the next action andonce again prompts the manager to modify the game world. We give examples of how an
author can describe an ideal player experience in the following sections.

42.3 AI-Driven Experience Management: Common Techniques
The previous section introduced AI-driven player experience management. The interactive narrative was encoded in a computer-readable format, story branches were either preauthored or automatically planned out, and a specification of the player’s ideal experience
was used to select among them. Figure 42.2 shows how these steps are related to gameplay,with the AI GM monitoring and updating the game to manage what happens next.
In this section, we detail computational techniques that power those steps. In doing so,
we provide the game designer with a set of tools that he or she can mix and match to suitthe needs of his or her particular game.
42.3.1 Technique: Narrative Generation
Given a formal representation of the story world and a history of the player’s actions,
the AI GM can automatically compute possible narratives that (1) are consistent withthe player’s actions and (2) satisfy any future authorial goals. One approach to doing so
is to use an automated planner. In the past, narrative-centric planners have been used
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PlayerGame
environment
Playing
Monitors
the game

Narrative
generation

AI
GM

tes t
da en
Up ronmvi
enPlay stylemodeling

Machinelearned
selection
Objectivefunction

Goal
inference

Emotionmodeling

Figure 42.2
A high-level view of AI experience management.

(e.g., Longbow [Young 94]). More recent systems attempt to use general-purpose off-the-shelfplanners such as Fast Downward [Helmert 06].
Since a planner cannot produce the actual high-quality writing or multimedia content
to go with the narrative stubs generated in PDDL, the corresponding narrative pieces need
to be pregenerated during game development and then fleshed out by human writers andartists. Then, during the game, the AI planner is effectively selecting from a library of narrative fragments in order to assemble start-to-finish narratives on the fly while satisfying
the world consistency (e.g., the wolf is dead) and authorial constraints (e.g., the grandmother needs to be eaten).
42.3.2 Technique: Play Style Modeling
Knowing something about the player can be helpful when choosing between narratives.
One approach is to model the player as a vector of numbers, each number indicating theplayer’s inclination toward a certain play style. The play styles can be taken from player
archetypes such as the canonical RPG types [Laws 01]. For instance, the vector could be(F: 0.9, M: 0.2, S: 0.1, T: 0.4, P: 0.3), which indicates that the player has often played as afighter (0.9) but less so as a method actor (0.2), storyteller (0.1), tactician (0.4), or power
gamer (0.3). These values are maintained by the AI GM when it observes the player’s
actions in game [Thue 07]. A simple way to implement this is to annotate each action
template (e.g., Listing 42.1) with a vector of updates to the model. For instance, whenever the player kills anyone, the model’s estimate of his or her fighter inclination could
increase by 0.3.
The player’s play style inclinations may change over time. Thus, the model should track
the player’s current/recent inclinations. This can be handled by gradually moving all vector components toward their neutral values over time.
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42.3.3 Technique: Goal Inference
Knowing something about the player’s inclinations toward different play styles can help
the AI manager infer the player’s current goals. For instance, if the player controlling Red
has previously shown an inclination toward fighting, it is likely that he or she will try tokill Grendel.
We can implement this type of inference by having the game designer encode potential
player goals in PDDL and specify a correlation between play styles and goals. To illustrate,
suppose that when Grendel is first introduced, any player may intend to either kill Grendel(goal #1) or avoid him (goal #2). Then, the correlation can be specified as in Table 42.2.
This means that someone purely interested in fighting would have a strong (0.9 out of 1)
intention to kill Grendel and a weak intention (0.1) to avoid him. In practice, a player isusually inclined toward a mix of different play styles. Thus, the goal intentions of a given
player are computed as a matrix product of the correlation matrix and the player model(F: 0.9, M: 0.2, S: 0.1, T: 0.4, P: 0.3):

 0.9

 0.1

0.7
0.3

0.2
0.6

0.4
0.8

 0.9  
0.2
0.6     1.31  ×  0.1  ≈ 
0.1     0.56 
0
.
4
 
 0.3  

(42.1)

Note that both the correlation matrix and the player model shown in Equation 42.1 aretransposed to make the matrix product work. The result shows us the player represented
by our current player model has a weight of 1.31 to fight Grendel and a weight of 0.56 toavoid it. We also normalize the vector to keep all intention values between 0 and 1. The
normalized result is approximately (0.7, 0.3), indicating that this player is likely intending
to kill (and not avoid) Grendel [Poo Hernandez 14].
42.3.4 Technique: Emotional Modeling
One way for the game designers to specify an ideal player experience is to give the desired
emotional arc. For instance, many games follow the classic Aristotelian tension arc[BioWare 07, BioWare 09, Bethesda 11], while some experiment with periodic functions.
A commercially deployed example of the latter is Valve’s Left 4 Dead, which uses an AI
Director to keep the player on a sinusoidal tension curve by dynamically shaping the zombie influx [Booth 09].
Table 42.2 Play Style and Goal Correlation Example
Play Style Inclination

Fighter
Method actor
Storyteller
TacticianPower gamer

528

Goal 1: Kill Grendel

Goal 2: Avoid Grendel

0.9
0.7
0.2
0.4
0.6

0.1
0.3
0.6
0.8
0.1

Analytics, Content Generation, and Experience Management

While Left 4 Dead’s AI Director models the player along a single emotional dimension (tension), the appraisal-style models of emotions [Lazarus 91] are able to infer theplayer’s emotions in several dimensions [Ortony 90]. For example, CEMA [Bulitko 08] isa lightweight subset of EMA [Marsella 03] that focuses on just four emotions: joy, hope,fear, and distress. Thus, the player’s emotional state is represented with four numbers,
each expressing the current intensity of the corresponding emotion. Thus, (J:0.8, H:0.6,
F:0.2, D:0) would represent a player who is presently joyful (0.8) and hopeful (0.6) but
slightly fearful (0.2) and not distressed (0).
To compute the numbers, an appraisal-style model needs to know the player’s goals andthe likelihood of accomplishing them. To derive the player’s goals, one can use the technique of goal inference presented earlier in the article. The likelihood of accomplishing thegoals can be computed based on the player’s character attributes (e.g., a stealthy character
will probably be able to avoid Grendel).
To illustrate, consider a player whose goal intentions to kill Grendel and avoid dying arecomputed as (0.7, −0.3). Suppose this player has a 50% chance of killing Grendel and a 10%
chance of dying. A desirable but uncertain goal elicits hope and hence the system models
the player as being hopeful at the intensity of 0.5 × 0.7 = 0.35. According to appraisal-stylemodels, once a desirable goal is certain, it no longer elicits hope; instead, it elicits joy. Since
the goal of killing Grendel is uncertain, there is no joy from it (yet). Dying is undesirable
to this player (values for undesirable outcomes are expressed by negative numbers, −0.3 inthis example), and hence it elicits the emotion of fear with an intensity of 0.1 × 0.3 = 0.03.
There is no distress yet since the player is not certain to die. Hence, the player’s emotional
state is computed as (J:0, H:0.35, F:0.03, D:0) [Gratch 06].
42.3.5 Technique: Objective Function Maximization
Player inclinations and emotional state models can be used by the AI GM to selectamong alternative narratives, with the goal of achieving the author’s notion of an ideal
player experience. A simple way of doing so is to annotate each narrative with its suitability with respect to different styles of play. For example, introducing Grendel may
be most suitable for a fighter, whereas a resurrecting fairy may appeal to a storyteller.
Computationally, the suitability of a narrative is a vector of numbers, one for each styleof play. For instance, a simple annotation for the introduce Grendel narrative can be(F: 0.9, M: 0, S: 0, T: 0, P: 0), whereas the introduce magic fairy narrative can be annotated as (F: 0, M: 0, S: 0.9, T: 0, P: 0). Given several annotated alternative narratives, themanager can take a dot product between the player inclination model and each annotation. The narrative with the highest dot product is then presented to the player [Thue 07,
Ramirez 13]. In our example, the player’s play style inclinations are modeled as (F: 0.9,
M: 0.2, S: 0.1, T: 0.4, P: 0.3), which means that the dot product for introduce Grendel is0.81 (Equation 42.2), whereas the dot product for introduce magic fairy is 0.09 (Equation
42.3). Hence, our player will face Grendel after killing the wolf:

( 0.9
(0

0
0

0

0

0 ) ⋅ ( 0.9

0.2

0.1

0.4

0.3 ) = 0.81

(42.2)

0.9

00 ) ⋅ ( 0.9

0.2

0.1

0.4

0.3 ) = 0.09

(42.3)

Techniques for AI-Driven Experience Management in Interactive Narratives

529

A more advanced approach is to use the play style inclinations to infer the player’s intentions. Those can then be used to predict how the player’s emotional state will be affectedby each of the alternative narratives. The narrative that manages to bring the player’s emotional state the closest to the target emotional state (as specified by the author) is thenpresented to the player. To illustrate, suppose the author wants their players to experience
the following emotional state (J:0, H:0.4, F:0.03, D:0) at this point in the story. The narrative
introducing Grendel is predicted to put the player in the emotional state (J:0.8, H:0.6, F:0.2,
D:0), whereas the narrative introducing the magic fairy is expected to elicit the emotional
state (J:0, H:0.35, F:0.03, D:0). The Euclidean distance between the expected emotional
state (J:0.8, H:0.6, F:0.2, D:0) and the target emotional state (J:0, H:0.4, F:0.03, D:0) is 0.83:( 0.8 − 0 ) + ( 0.6 − 0.4 ) + ( 0.2 − 0.03 ) + ( 0 − 0 ) ≈ 0.83
2

2

2

2

(42.4)On the other hand, the distance between the emotional state (J:0, H:0.35, F:0.03, D:0) andthe target emotional state (J:0, H:0.4, F:0.03, D:0) is only 0.05. The AI manager tries tominimize the distance and thus will select the narrative with the magic fairy.
42.3.6 Technique: Machine-Learned Narrative Selection
Yet another alternative for selecting narratives is to use machine learning during thedevelopment stage to automatically acquire a mapping from game and player states tothe set of alternative narratives. One such mapping is a ranking function, similar to how
Internet search engines map user queries to a ranked list of web pages. This approach isappropriate when training data are available. For instance, if human game masters could
be observed and their narrative selection recorded, then the resulting corpus of data could
be used to machine-learn an AI approximation to a human game master. We describe an
implementation of this approach in the following section.

42.4 Implementations
Over the last seven years, our research group at the University of Alberta has implemented
AI experience managers using different combinations of the techniques given earlier. In
the following sections, we briefly review these implementations and describe how they
were tested.
42.4.1 PaSSAGE
Player-Specific Stories via Automatically Generated Events (PaSSAGE) [Thue 07, Thue 11] isan AI experience manager that combines two techniques: (1) play style inclination modeling and (2) maximizing a simpler version of the aforementioned objective function. We
deployed PaSSAGE in the domain of RPG-style games, using off-the-shelf game engine technology from both Neverwinter Nights [BioWare 02] and Dragon Age: Origins [BioWare 09].
Our first test bed, Annara’s Tale, is an adaptation of the Little Red Riding Hood that included
five different endings, eight substantially different paths through the story, and three points
at which PaSSAGE could choose between story branches. Our second test bed, Lord of the
Borderlands, is an original story that included 16 different endings, 32 substantially different paths through the story, and two points at which PaSSAGE could influence what
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happened next. When compared to a uniform random manager (which still obeyed thegame’s authorial constraints), PaSSAGE scored higher with respect to player-reported fun
with high confidence (as tested with 133 undergraduate students). While preliminary, thisresult shows promise for applying PaSSAGE in both current and next generation games.
42.4.2 PAST
Player-Specific Automated Storytelling (PAST) [Ramirez 12, Ramirez 13] added narrative
generation to PaSSAGE. Specifically, instead of hand-scripting the narrative continuations (as was done in PaSSAGE), PAST used the automated planning module from theAutomated Story Director [Riedl 08] to compute narratives that were consistent with theplayer’s actions and the authorial goals.
We evaluated PAST with a text-based choose-your-own-adventure version of the Little
Red Riding Hood story. Within each game, the player made four consecutive narrative
choices. For each player choice, PAST computed several alternative accommodations (i.e.,alternate stories based on a virtual domain description aligned with authorial goals) andselected one using a play style model similar to the one in PaSSAGE. More than 30 different narrative trajectories were available to the players. Players’ self-reported perception of
fun and agency indicated positive trends [Ramirez 13].
42.4.3 PACE
Player Appraisal Controlling Emotions (PACE) [Poo Hernandez 14] is our latest AI experience manager. It uses the four techniques of narrative generation, play style modeling,goal inference, emotion modeling, and the second, more advanced type of the objectivefunction. Unlike PAST, we used a PDDL-compatible off-the-shelf automated planner tocompute possible narratives. PACE uses the full objective function to predict the player’s
emotional state along each of the alternative narratives. It selects the narrative that brings
the player closest to the target emotional trajectory.
PACE is currently being deployed within the novel video game iGiselle: an interactive reimaging of the classic Romantic ballet Giselle [Gautier 41]. In iGiselle, the player
assumes various ballet poses (read by Microsoft Kinect) to control their avatar and makenarrative choices. The narrative is presented via a combination of still images, prerecorded
voiceovers, videos, and music.
42.4.4 SCoReSSports Commentary Recommendation System (SCoReS) chooses stories for sports broadcasts [Lee 13]. It uses machine-learned narrative selection to pick the piece of color commentary most suitable to the state of a sports game at any given moment. It was evaluatedin the domain of baseball and received positive feedback from both the users and humanexpert color commentators.

42.5 Conclusion and Future Work
We have presented the task of managing a player’s narrative experience in a video game. In
doing so, we decomposed the problem into several steps and presented specific techniquesthat can be used to implement an AI GM for a particular video game. We then briefly
described four such game masters that we have developed in our research group.
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Future work will continue implementing and testing these types of approaches withindifferent games. We also hope to build a narrative space exploration tool that will combine
the narrative generation technique with player models data-mined from existing gametelemetry. Game designers could then use this tool to interactively explore and shape anarrative space in the early stages of a game’s story development.

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