Intentionally Generating Choices in Interactive Narratives
Michael Mateas and Peter Mawhorter and Noah Wardrip-Fruin
Computer Science Department
University of California Santa Cruz
Santa Cruz, CA 95064 USA
{michaelm, pmawhorter, nwf}@soe.ucsc.edu
Abstract
Interactive stories face a famous “authorial bottleneck.” Two
existing approaches to this problem are story management
systems, such as drama managers, and interactive narrative
generators. Existing work leverages well-understood qualities
of linear narrative such as suspense to generate content, but
interactivity brings new capacities, like the ability to make a
player experience regret. These interactive poetics arise from
the player’s ability to make choices, and depend heavily on
the structure of the choices that are presented to the player.
This system description paper presents a system that creates
choices by reasoning about their structure, and describes the
architecture that enables it to do so.
Introduction
Since the 1970’s, researchers in artificial intelligence have
been making systems that can creatively generate stories
(Klein et al. 1971). With the rise of digital games, and in
particular, interactive narratives1, this research has found a
new application: generating and managing the complexities
of interactive narratives. One approach to this problem is
to manage players’ experiences. A managed experience lets
authors create a diverse array of content while letting players
experience a coherent narrative that includes different parts
of the content depending on their choices. Another approach
is to create systems that generate content, letting authors
work at a more abstract level (perhaps writing re-combinable
actions or events) which the system can then use to generate
a wide variety of possible stories. Both approaches are proposed
solutions to the fact that the work necessary to create a
truly open world is overwhelming for human authors (Orland
2011). Existing systems have demonstrated the viability of
reasoning about traditional narrative qualities for both experience
management and story generation. Qualities unique to
interactive narratives have not yet been widely used for reasoning
in such systems, however. For example, the ability to
make a player regret their own actions is unique to interactive
contexts, and it depends on aspects of the narrative (such as
which actions the player intended, and which outcomes were
consequences of player actions) that go beyond traditional
narrative qualities.
1
The authors are aware that interactive narratives predate digital games in several
forms, but digital games have popularized interactive narrative as a medium.
Interactive narrative systems thus stand to gain by reasoning
about interactive as well as traditional poetics. Presented
here is a system called Dunyazad that attempts just that: It
dynamically builds choices with the goal of achieving specific
poetic effects. Dunyazad focuses on choice poetics as
a subset of interactive poetics, attempting to structure the
choices that it gives the player so that they evoke feelings
like safety or confusion (Mawhorter et al. 2014). As an
operationalization of choice poetics, Dunyazad’s successes
and failures can also inform the theory that drives it.
Liapis, Yannakakis, and Togelius recently stated that
games were an ideal domain for computational creativity,
and listed interactive narrative as an important part
of that domain (Liapis, Yannakakis, and Togelius 2014).
Human authors are now exploring the full potential of interactive
narrative: Many independent games have earned
praise for their stories, and communities that produce innovative
interactive narratives have formed around tools like
Inform 7 (http://inform7.com/) and Twine (http:
//twinery.org/).2 If generative narrative systems want
to leverage the potential of interactive narrative, they will
need to reason about interactive poetics, and in particular,
how the choices they present to players are perceived.
Prior Work
In computational narrative systems, there has been a recent
trend towards explicit poetics. Szilas’ 2003 IDTension first
proposed the idea of creating an interactive narrative by “simulating
the laws of narrative” (Szilas 2003), much as one can
produce a wide range of gameplay by simulating the laws of
physics. This direction of work naturally proceeds by identifying
the mechanism of specific poetic effects and building
computational systems to produce those effects. El-Nasr’s
2007 Mirage (El-Nasr 2007) is another example of this approach;
it attempts to apply a range of dramatic techniques to
increase engagement in an interactive narrative. In contrast,
systems such as Suspenser (Cheong and Young 2006) and
Prevoyant (Bae and Young 2008) have focused on specific
poetic effects (suspense and surprise respectively).
Dunyazad as described here can be viewed as continuing
this line of research because it reasons explicitly about poet-
2
These are both examples of tools not explicitly designed to encourage creativity
which nonetheless support it by making authoring faster and easier.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 292
ics; it emphasizes interactive poetics, and in particular, the
poetics of discrete choices. Whereas IDTension and Mirage
incorporate traditional poetics into interactive experiences,
Dunyazad focuses on interactive poetics, leveraging choice
structures to create affect. In some respects Dunyazad is designed
as much to illuminate interactive poetics as to exploit
them: because it uses declarative code to construct poetic
choices, its successes and failures can be traced to concrete
parts of its theory, and that theory can thus be informed by
the system’s performance.
More recently, several studies have attempted to formally
investigate and model poetic effects in interactive narrative
contexts, with a focus on choices. In 2011, Thue, Bulitko,
Spetch, and Romanuik measured players’ perceptions of
agency and found that they often differed from what one
might expect based on the choices available to the player
(Thue et al. 2011). Their system did manipulate an interactive
narrative to achieve a poetic effect (give the player a
sense of agency), but it focused on manipulating events in a
way that was invisible to the player, rather than on changing
a player’s perceived options at any particular choice. In a
study of agency which did not involve a generative system,
Fendt, Harrison, Ware, Cardona-Rivera, and Roberts were
able to create an illusion of agency, albeit in the context of
an extremely simple interactive narrative (Fendt et al. 2012).
A follow-up to the Fendt et al. study by Cardona-Rivera,
Robertson, Ware, Harrison, Roberts, and Young linked players’
perceptions of differences between outcomes to their
perceptions of agency (Cardona-Rivera et al. 2014). In another
paper focusing on choices in interactive narratives, Yu
and Riedl were able to predict player choices using collaborative
filtering (Yu and Riedl 2013).
This active research surrounding choices in interactive
narratives shows that authors are interested in the poetic
effects of choices. However, systems that actually reason
about the poetics of the choices they generate are scarce–
most existing systems reason about different options and
outcomes independently. Barber and Kudenko’s 2007 work
on dilemma-based interactive narrative is a notable exception
(Barber and Kudenko 2007). Their work focuses on a single
type of choice, generating interactive experiences where each
choice is a dilemma.
Ideally, a system that took choice poetics into account
would dynamically construct each choice that it offers the
player for maximum poetic impact. Of course, just as IDTension
and Mirage reason about a range of classical poetics,
such a system could take into account a range of interactive
poetics (including aspects beyond choice poetics). But even
a system that only considers choice poetics is a step in the
right direction.
Choice Poetics
The theory of choice poetics described by Mawhorter, Mateas,
Wardrip-Fruin, and Jhala in (Mawhorter et al. 2014) provides
a framework for reasoning about choices, which is
crucial for an interactive narrative system which must generate
them. When analyzing the poetics of a choice, the first
consideration is the player’s mode of engagement: how is
the player approaching the game, and what do they hope
to achieve through their play? Common modes of engagement
include power play (playing to achieve ludic goals like
scoring points), avatar play (playing by projecting yourself
into the game and making the choices you would make in
a character’s situation) and role-play (playing to express a
particular role through the actions of one or more characters
you control). There are also other less common modes
of engagement like critical play, and players can (and usually
do) engage with multiple modes at once. Taking modes
of engagement into account does not require reading the
player’s mind, however: just as with any other element of a
game, designers can make decisions based on their intuitions
about how players will play, and they can refine their designs
through playtesting.
Dunyazad directly encourages avatar play, and assumes
that this will be players’ primary mode of engagement when
it constructs choices. Role play is also supported to some
extent, but because there are only minimal game mechanics,
Dunyazad’s stories do not lend themselves to power play. The
game mechanics that do exist (skills which affect outcomes)
are deployed in such a way that favorable outcomes from an
avatar play perspective (which are favorable for the diegetic
protagonist) are aligned with favorable gameplay outcomes
(those in which the action attempted is successful, generally
leading to successful endings).
Once a choice is considered in terms of a particular mode
of engagement, it may fall into one of several classes of recognizable
choice idioms, such as the dilemma or the false
choice. Recognizing these idioms is based on an analysis of
the framing, options, and outcomes of a choice (for example,
a classic dilemma must have exactly two options, and the
options’ outcomes should each thwart a different player goal).
Besides classifying choices as examples of choice idioms,
(Mawhorter et al. 2014) does not say much about how to
construct choices, although it does list some aspects of player
experience that can be manipulated through the use of different
choice structures. More analysis of existing interactive
fictions within the framework of choice poetics would likely
yield more specific methods for choice construction, however,
and there is some existing advice on choice construction in
the form of authoring advice for human authors of interactive
narrators (e.g., (Choice of Games LLC 2010)). The choice
construction methods in Dunyazad are currently based on this
latter body of work, as described in the Choice Generation
section on page 5.
Dunyazad
Although not yet complete, Dunyazad is a novel story generation
system that is intended to generate interactive narratives
in the style of Choose Your Own Adventure books
using second-person narration and explicit choices. Dunyazad
treats choices as first-class objects, and reasons about
their structures. In particular, it has rules for constructing
a variety of choice types based on the player’s estimated
expectations and evaluations in its choice structure module.
Dunyazad ultimately produces natural language narratives.
Each story consists of sections of text followed by choices,
where each choice leads to another section of text or to an
ending. Dunyazad is not interactive, but instead generates
Proceedings of the Sixth International Conference on Computational Creativity June 2015 293
entire interactive narratives that players can interact with
separately (importantly, this allows players to re-play parts
of the narrative).
As you journey onwards, a leviathan rises majestically up
from the ocean, tentacles curling. It is threatening you.
! You try to flee from it.
– You attempt to pacify it with music.
You flee from it and escape. You travel onwards.
Figure 1: A minimal example vignette
Because Dunyazad is focused on operationalizing choice
poetics, its default domain is simple travel/adventure stories
in a fantasy setting, made up of sequences of relatively independent
“vignettes” or scenes. Each vignette is made up
of a setup plus a few basic actions, some of which may be
player-initiated choices (“choice” nodes) and some of which
may be events dictated by the system (“event” nodes). Each
story node thus represents a single event or choice, including
a context, one or more actions that might happen, and any
outcomes of those actions. Although most world state is reset
at the beginning of each vignette, the state of the player’s
party is not, allowing for some overall continuity. Figure 1
shows the full text of a minimal example vignette composed
of a single choice (at which the player chose to flee), and
a single event (the default vignette-ending event “travel onwards”).
This vignette also introduces some new context at
its choice node (an attacking monster) which is described in
the text. Of course, when presented to the player, the text
stops at the choice until the player has selected an option.
Although more complex vignettes are possible, the system
is designed to create a stream of simple, direct choices, imitating
the game Spent (http://playspent.org/html/)
(McKinney 2011). By limiting the complexity of vignettes
and refreshing most of the world state between vignettes,
the user experience is directed towards shallow and playful
interaction, and at the same time, the system has fewer opportunities
to accidentally create plot holes. This also creates an
environment in which the poetics of individual choices (e.g.,
was the last choice relaxing?) are important to the feel of the
story overall, as opposed to merely supporting a dramatic arc
defined by traditional narrative elements.3 The system accordingly
assumes that players will mainly engage in avatar
play, and perhaps also light role play, and attempts to provide
choices that enable these modes of engagement.4
From a technology standpoint, Dunyazad combines imperative
Python code with declarative answer set programs to
iteratively grow a branching story. The imperative code manages
the iteration, at each step filling in single story node and
adding new blank child nodes for each new option created.
Filling in a node is accomplished by using the Potassco Labs
tools gringo and clingo to ground and solve an answer
set program (Gebser et al. 2011). The answer set program
3
In this case, the overarching plot of a journey to an exotic destination constrains
little in terms of tension, narrative developments, etc.
4
For more details about of modes of engagement, refer to (Mawhorter et al. 2014)
for each node includes predicates that represent the entire
current story state, but facts from the solution to the program
are only used to modify the currently-focused node.
After a complete story structure is created, Dunyazad’s
imperative code uses a set of text templates to render the
story into natural language. This module takes care of verb
conjugation and pronominalization where necessary, and the
text templates form a generative grammar which adds extra
variation to the story. This variation doesn’t change the
underlying sequence of events, and mostly consists of word
choice and sentence-structure variation that reduces literal
repetition when similar events are described multiple times.
While Dunyazad’s hybrid iterative/declarative approach
does limit the kinds of constraints that the system can easily
place on multi-node story structures, it is necessary to
keep the answer set problems tractable: Asking the solver
to produce a complete story with hundreds of nodes in a
single step is not a task that many modern computers could
handle (if any), whereas just solving a single node can be
accomplished in seconds. At the same time, being able to use
answer set programming for the creation of individual nodes
provides two benefits. First, answer set programming reasons
simultaneously about all of its constraints, which means that
building some logic which detects a certain condition also
allows direct control over that condition (by e.g., prohibiting
it or requiring that it hold). This means that there is little
distinction between writing code which recognizes a phenomenon
and writing code which produces it: the answer set
solver does the hard work of figuring out what has to happen
in order for the phenomenon to occur.
The second main benefit of using answer set solving is
that it directly encodes constraints. Dunyazad as a project
aims to apply choice poetics to the generation of interactive
narrative, but it should also be able to push back on
choice poetics when constructing choices based on theory
fails to produce the expected results. Setting aside
the difficult issue of blame assignment between the system
and the theory, using answer set programming enables
the system to better inform the theory because the constraints
responsible for producing behavior can be directly
translated into theoretical statements. For example, a rule
like regret(Choice) :- consequence(Choice,
Outcome), bad for player(Outcome) translates
directly to a theoretical statement “When the player chooses
an outcome that leads to something which is bad for them,
they will feel regret.” If testing reveals that players do not
feel regret when the system thinks they should, the rule can
be refined, and because it is a direct encoding of the theory,
such refinement can directly inform the theory.
Representation
Although the output of Dunyazad is natural language, it has
an underlying predicate representation of the stories it generates.
Each story node describes either a choice or an event,
and the structure of the two is the same, the only difference
being events have only one option. Story nodes have a
rich predicate representation of their initial state, which can
encode arbitrary properties of and relations between story
elements, including characters and items. Story nodes also
Proceedings of the Sixth International Conference on Computational Creativity June 2015 294
1. st(root, inst(actor, monster 76)).
2. st(root, property(name, inst(actor,
monster 76), "leviathan")).
3. st(root, relation(threatening,
inst(actor, monster 76), inst(actor,
you))).
4. at(root, action(option(1), flee)).
5. at(root, outcome(option(1),
o(success, escape))).
6. at(root, outcome(option(1),
o(get injured, safe))).
7. at(root, arg(option(1), fearful,
inst(actor, you))).
8. at(root, arg(option(1), from,
inst(actor, monster 76))).
9. at(root 1, action(option(1),
travel onwards)).
Figure 2: Some example predicates describing parts of fig. 1
have some number of options, each of which has an action
associated with it, along with argument bindings for that action.
Figure 2 shows some of the predicates that describe the
example vignette in fig. 1.
Story states are sets of state predicates each of which takes
one of four forms:
1. st(root, inst(Type, ID)).
Declares the existence of a particular instance, which has
a Type of either actor or item.
2. st(root, state(State, Inst).
Assigns a unary state such as injured to an instance.
3. st(root, property(Prop, Inst, Value).
Associates a property with an instance and specifies its
value. For example, an actor can have the has skill
property with a value of music indicating that they possess
the music skill. Properties can be multi-valued.
4. st(root, relation(Rel, From, To).
Asserts a relation between two instances. For example, an
actor can have the has item relation with an item. Some
constraints (like exclusivity of the has item relation) are
enforced.
Frame axioms dictate that state changes only occur when
specified by actions. Actions are defined by arguments, outcome
variables, skill links, preconditions, and post-conditions
as follows:
1. argument(Action, Arg, Type).
Specifies an argument Arg which must bind an instance
of type Type in the current state.
2. outcome val(Action, Var, Val).
Specifies that outcome variable Var can take on value
Val. Each variable has multiple possible values.
3. skill link(Skill, Type, NeedsTool,
Action, Arg, o(OutVar, OutVal)).
Skill links specify how character skills influence action
outcomes. The four link types are required,
promotes, avoids, and contest. These indicate
player expectations. For example, the healing skill is
linked to the healed value of the success outcome
variable for the treat injury action via a required
link that also specifies that a tool is needed. Thus if the
player lacks the healing skill and an option for them to
take the treat injury action is presented, the system
assumes that the player will expect the action to fail.
4. Pre- and post-conditions. These have no fixed form, but
instead are arbitrary logical constraints. For example, it is
an error for the treat injury action to be performed
on a patient who is not injured. Most depend on outcome
variables having specific values. Another example: if
the success variable of a treat injury action has
a value of healed, then the injured state is removed
from the patient, but if the success variable is either
still injured or killed this doesn’t happen.
As an example, the text “You try to flee from it,” in
fig. 1 is a rendering of an action “flee” with the player
character and the monster as arguments. The flee action
has two outcome variables: success which has values
escape and failure, and get injured, which has
values injured and safe. In fig. 2, facts 4 to 8 describe
the action, outcome, and arguments of this option (the node
that it is part of is root, and it is the first option at that
node). Of course, each option at one node leads to another
story node, and any consequences of the outcome associated
with that option are reflected in the starting world state of the
linked node. In fig. 1, the initial choice story node links to
two successor nodes, only one of which is displayed.5 In this
case, the consequence of the successful flee action is that you
have escaped the threat of the monster (which was part of
the initial world state). The “travel onward” action’s initial
world state thus does not include the threat of the monster attack,
which is actually a precondition for the “travel onwards”
action–it requires an absence of “problems.”
“Problems” and more generally “potentials” (which are
either “problems” or “opportunities”) are an important part of
how the system builds stories. Dunyazad represents a “setup”
as a partial world state which is added to the current world
state when a new vignette begins. In fig. 1, the “monster
attack” setup is used, which introduces a monster (in this
case a leviathan) which is threatening the player-character.
5
From a developer’s perspective, all of the linked nodes are part of the same
vignette, but of course without re-play, a player will only see one of them.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 295
The fact that the player is being threatened (which is encoded
as a relation) is explicitly recognized by the system as a
“problem”–in fact all instances of the “threatening” relation
are considered “problems,” even when the player is not the
target. This explicit representation of both problems and
opportunities drives the basic consideration of what actions
are appropriate in a given situation, and also plays into the
rules about choice structures.
Reasoning
Dunyazad uses answer set programming to create the individual
events and choices that make up a story. It is thus
governed by a set of logical constraints which dictate what
event configurations are acceptable. As already mentioned,
solving for dozens of story nodes simultaneously is infeasible,
because solving time is exponential in the number of
nodes considered. As a compromise, Dunyazad iteratively
solves individual story nodes.
Thus Dunyazad’s reasoning revolves around the construction
of a single event or choice node. The rules governing
node construction can be divided into three categories:
• Constructive rules–rules that help create the basic structure
of facts, such as the rule that stipulates that each option
has an action associated with it.
• Sense rules–rules that disallow nonsensical story structures,
such as the rule that says that no choice should have
two identical options or the rule that disallows trading
items with oneself.
• Content rules–rules that discard some valid stories as uninteresting
or otherwise undesirable, such as the rule that
requires successive vignettes to use different setups.
Without constructive rules, core facts like those that assign
values to arguments would be missing from result answer
sets, and the system would crash. Without sense rules, all of
the basic components of a story would be there, and the story
could be rendered to natural language by the text generation
system, but the result would be at best surreal and at worst
gibberish. Without content rules, the result would be an
understandable sequence of events, but it would probably not
be an interesting story.
Because of the nature of answer set programming, Dunyazad
effectively chooses an arbitrary permutation of an
event among all possibilities that satisfy its rules (consult
(Gebser et al. 2011) for more background on how answer set
programming works). Each rule represents a constraint on
the generative space of story nodes, which makes it both easy
to prune the generative space, and easy to see how an individual
constraint effects the generative space. The following
variables determine the space of possible choice structures:
• The number of options (minimum 2 for a “choice” node;
maximum 4 for performance reasons).
• The action for each option (there are 13 actions in the
current domain model).
• The possible argument bindings for each action (most
actions have 2-4 arguments, and each argument generally
has 5-7 type-appropriate bindings at a given story state).
• The values for each outcome variable of each action (most
have 1-2 outcome variables with 2 values each).
Unsurprisingly, there are a staggering number of possibilities
under the constructive rules, but this space is reduced
drastically by the sense and content rules.
Choice Generation
Dunyazad’s design is in part based on concrete human advice
for writing choice-based narratives offered by Choice
of Games (an interactive narrative publisher) in several online
articles (Choice of Games LLC 2010). In particular,
Dunyazad focuses primarily on expectations and outcomes,
which factor prominently in an article about the fundamentals
of choice design titled “5 Rules for Writing Interesting
Choices in Multiple-Choice Games,” (Fabulich 2010).
Dunyazad’s choice structure subsystem is devoted to estimating
and managing the poetics of the choices it generates.
Abstractly, this subsystem reasons about choices in terms of
expectations and outcomes, using estimates of player perception.
This same structure for representing and reasoning
about choices could be used by other systems that wanted to
generate choices intentionally.
The most basic structure of Dunyazad’s choice representation
has already been described: a choice consists of context,
options, and outcomes. Context in this case is a world state,
options correspond to discrete, fully-specified actions that
the player-character can take, and outcomes are the changes
in world state that result from a particular action. To actually
reason about the poetics of a choice, however, the system
needs to make some assumptions about the player’s experience,
which gives rise to three more entities: player goals,
player expectations and perceived outcomes.
Player goals are the basis for reasoning about how players
might perceive choices. Some basic player goals can
be predicted by the author, and to the extent that players
actually pursue these goals, an author can design choice poetics.
For example, an author might presume that players
will want to keep their character alive and healthy, and that
players will also want to maintain the health of their allies.
A choice where the player is forced to sacrifice either their
character’s health or the health of an allied character could
then be constructed with the goal of adding to the player’s
sense of tension. If players do in fact value their character’s
health and that of their allies, the choice should be a tense
one (other details of its construction notwithstanding). For
players who don’t value one of these goals, the choice will
lack the tension that the author intended, but that doesn’t
mean that the author’s strategy for creating a tense moment
was invalid. The author also has methods for encouraging
the pursuit of various goals, such as using standard narrative
techniques to try to promote empathy with the characters.
Dunyazad relies on the same strategy as this hypothetical
author to create choice poetics: through its fixed introduction
segment and according to genre conventions, it encourages
players to pursue certain goals. It then estimates the poetic
effects of the choices it creates assuming that the player will
be invested in those goals. Dunyazad assumes are that the
player will pursue the following goals:
Proceedings of the Sixth International Conference on Computational Creativity June 2015 296
• Avoid injury to themselves and their allies (high priority).
• Avoid threats to themselves and other non-aggressive characters
(high priority).
• Have all actions they take be successful (low priority).
• Acquire and retain tools for their skills (low priority).
Given assumed player goals, a choice can be considered
in terms of its player expectations and perceived outcomes.
Both of these will vary from player to player, but just like
with player goals, human authors can often estimate them.
Like the player goal estimation, player expectation and perceived
outcome estimation depends on the system author.
This works via the skill link system as mentioned in the Representation
section above: the author of an action specifies
which skills are linked to which outcomes and how, and this
information is used by the system to estimate player expectations.
For example, if the player has a goal to maintain their
health, but they’re missing the fighting skill, an option
allowing the player to attack an enemy will be marked
as dangerous, because the fighting skill is linked to the
injured value of the aggressor state outcome variable,
and that outcome would cause the player to be injured,
threatening their goal.
As an example, consider the choice in fig. 1. This choice
has two options, which correspond to the “flee” and “pacify”
actions. Unbeknownst to the player (before they’ve made a
decision at least), the outcome of the “flee” action in this case
will be a successful escape, while the outcome of the “pacify”
action (not shown) will be a failure that does not change the
world state (i.e., the monster continues to threaten the player).
While generating this choice, the system creates a player
expectation for each option for each player goal, indicating
how the player would expect that option to impact that goal.
Taking “escape from threats” as a player goal, both options
at this choice are expected to threaten that goal, because the
system knows that both options could fail to achieve it. At
the same time, both options are expected to enable that goal,
because depending on their outcomes, either option could
achieve that goal.
But is either option likely to succeed or fail? Assuming
that the player has the “wilderness lore” skill (linked by a
“contest” link to the “flee” action) but the monster does as
well, the first option is indeterminate. However, based on a
“required” skill link, if the player does not have the “music”
skill, the second option is likely to fail.
There are thus five possible non-exclusive player expectations
per player goal:
• Irrelevant–this option is irrelevant to this goal.
• Threatens–this option risks failing this goal.
• Enables–this option might achieve this goal.
• Fails–this option is expected to fail this goal.
• Achieves–this option is expected to achieve this goal.
Threatens and enables expectations are assigned based on all
possible outcomes of an action, while fails and achieves are
based on outcomes that the player has reason to believe are
likely. Combinations of these expectations can describe a
variety of situations. For example, a choice which threatens,
enables, and fails a goal could be seen as a desperate gamble:
it has a possibility of success, but it is expected to fail.
Similarly, there are five perceived consequences for each
player goal:
• Irrelevant–this outcome does not affect this goal.
• Hinders–this outcome hinders progress towards achieving
this goal, but does not actually cause it to fail.
• Advances–this outcome contributes to achieving this goal,
but does not actually achieve it.
• Fails–this outcome directly fails this goal.
• Achieves–this outcome directly achieves this goal.
Unlike player expectations, perceived consequences are mutually
exclusive, and while expectations only reason about
the potential outcomes of an action, perceived consequences
are assigned based on actual outcomes.
Returning to our example, the player expectations for the
first option with regards to the goal “escape from threats”
will include “threatens” and “enables,” but since both the
player and the monster have the associated contest skill, no
stronger expectation is formed. For the second option, because
the player lacks the relevant “music” skill6, the player
expectations will be “threatens,” “enables,” and “fails.” The
perceived outcome of the first option will be that it achieves
the “escape from threats” goal, while the perceived outcome
of the second option will be that it fails this goal.
This representation of player goals, expectations, and perceived
outcomes enables rich reasoning about the poetics of
a choice. To start with, it’s easy to encode simple choice
idioms (see (Mawhorter et al. 2014)). An example would be
a dilemma–traditionally a choice with exactly two options
which lead to two different negative consequences. A choice
with exactly two options, each of which is expected to fail one
of two goals and enable the other fits these criterion. The perceived
outcomes here determine what kind of dilemma it is,
for example a false dilemma might have identical outcomes
for both options.
You come to a tavern and decide to rest for a while. A
merchant is selling a music book and she is selling an
oboe and a noble is bored and a peasant is bored and an
innkeeper seems knowledgeable.
– You play a song for the peasant. (+music)
– You gossip with him. (+elocution)
– You offer to trade the merchant some perfume for the
music book. (no skill)
– You tell the noble a story. (+storytelling)
Figure 3: A “relaxed” choice.
To give a more concrete demonstration of choice generation,
consider figs. 3 and 4. These examples were generated
6
Relevant to an actual player’s expectations is whether they are aware of this lack.
For now, Dunyazad ensures this by mentioning any relevant skills (or lack thereof) in
parentheses after each option. These are omitted in fig. 1 to avoid confusion.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 297
As you travel onwards, a dragon slowly approaches you.
It is threatening you.
– You attack it. (-combat)
– You attempt to pacify it with music. (-music)
Figure 4: A “grim” choice.
using a single player goal based on the idea of power-gaming:
“Succeed at every action.” Evaluating expectations relative to
that goal, fig. 3 is the result of asking for a “relaxed” choice:
a choice where there are no options which the player expects
to fail. Figure 47 is the opposite: a “grim” choice where every
option is required to be expected to fail.
When generating individual choices, the system uses everything
available to it (the choice of setups, the background
including the player’s starting skills, and the configuration
of options and outcomes) to create choices that satisfy the
given constrains, which can be expressed directly at the level
of player expectations. This ability to reason about player
expectations is critical in a system that wants to use choice
structures to achieve poetic effects. Of course, the system
isn’t reasoning directly about the player’s actual expectations,
but merely about the system author’s guess as to what
those expectations will be. For human authors, this is often
enough to achieve their goals, and for systems without
dynamic player modelling, it will have to be enough as well.
Abstract Architecture
The underlying principles behind Dunyazad’s choice structure
rules suggest requirements for systems that want to build
choices intentionally. At a basic level, the ability to reason
about all parts of a choice: the context, options, and outcomes,
is required. It should be noted here that reasoning about the
options individually is not sufficient: a system that wants to
dynamically construct choices that create poetic effects needs
to be able to reason about the range of options available at
a choice and assert things like “There are no options available
which the player expects will lead to positive outcomes.”
That brings up the next requirement: such a system needs
to be able to reason about player goals, expectations, and
perceptions. These things do not have to be modelled exactly
as Dunyazad models them, but they should be represented in
such a way that the system can reason about them.
In order to creatively construct a choice that gives the
player a feeling of “agency” or “regret” or “power” a system
needs to be able to define those things in terms of the player’s
view of the game. It is of course possible to give a player
these feelings in an interactive narrative without representing
them in any sort of system, but that just means that a human
author has done the reasoning required, not that it never
happened. And if a human author did that reasoning, then
the system will not be able to freely generate such choices:
it will be limited to generating them in situations that the
human author was able to foresee.
7
These examples were slightly edited from their original form for brevity.
So what is the next step once you have a system that reasons
about all parts of a choice and the player’s perspective
besides? The next step is to identify the choice poetics that
you want your system to create, and define them in terms of
the player’s perspective. For example, if you want the player
to experience “agency,” you might define your objective as
“The system should create choices with multiple options that
the player expects will lead to significantly different world
states,” as in (Cardona-Rivera et al. 2014). Given this concrete
definition of your goal in terms of player expectations,
the system should be able to construct such choices. If your
goal is hard to pin down in terms of player expectations, playing
some interactive narratives that create the feeling you
want to create and analysing their choice structures would be
the place to start.
While a computer-generated interactive narrative that successfully
evoked a particular feeling using choice structures
would be an achievement in its own right, even better would
be a system that used multiple choice structures in service
of a more complex goal. For example, if there are narrative
generation mechanisms trying to achieve a desired tension
level, making sure that choice structures are also contributing
to this goal would be a benefit. Or if the player-character is
supposed to be stumped by a mystery at some point in the
plot, perhaps generating choice structures where there are
no clear good or bad options could reinforce that point. The
ability to craft choices towards poetic ends unlocks many
new options for an interactive narrative system.
Future Work
Dunyazad is still under development, and getting it to generate
full interactive narratives as opposed to individual choices
is our current focus. Once it does so, it will be critical to
evaluate the narratives it generates, both from a creative standpoint
and to determine whether players actually perceive the
effects that it is trying to create. If Dunyazad is able to create
full interactive narratives with choices that support their
stories, it will represent an important step towards narrative
generators that take full advantage of interactive as well as
traditional poetics. However, even if it only generates individual
choices, Dunyazad still enables experiments that can
contribute to knowledge of choice poetics.
Generating full stories will require significant authoring
effort. Dunyazad’s current domain model has 13 actions,
6 potentials, 6 setups, and 4 player goals. In order to generate
experiences even few minutes long, it would need to
generate stories with dozens of nodes across perhaps 8-12
vignettes. The primary authoring effort to get to that point
with a satisfying level of variety lies in creating more distinct
setups, as well as adding a few more actions. Although
actions, potentials, setups, and player goals are all modular
from a technical standpoint and can be authored individually,
practically they need to take each other into account in order
to create interesting output. This is mostly a consequence
of the content rules. For example, adding an action which
results in a new state probably won’t change the system’s
output by itself, because without any player goals or potentials
that involve that state, the system will never consider
the new action to be relevant. Actions, potentials, setups, and
Proceedings of the Sixth International Conference on Computational Creativity June 2015 298
goals thus form interconnected subsystems that are linked by
certain key states. Although this makes authoring a bit tricky,
these subsystems are at least somewhat independent of each
other: the actions that involve trading items can be authored
without worrying about injury and death.
Besides further work on Dunyazad, there are several
promising research directions suggested by this work. First,
the fact that an interactive narrative system is making assumptions
about its players begs for a mechanism by which
the system could actually measure its players. (Thue et al.
2011) is an example of exactly that, but by increasing both
the complexity of choice manipulation and the detail of the
player model things would become even more interesting.
There is a link here to the world of intelligent tutoring system:
systems like Graesser, Chipman, Haynes, and Olney’s
AutoTutor already have components that attempt to measure
a student’s knowledge and even emotions (Graesser et al.
2005). Adapting these to work in an interactive narrative context
as opposed to a tutoring context would give the system a
much better means of estimating a reader’s expectations and
goals than authorial guesswork.
Expanding a choice-poetics based system to deal with
broader interactive poetics would be interesting too. Many
games offer interactions much more complicated than discrete
choices, and reasoning about these would be more dif-
ficult. Many of the same principles apply, however, and
creating a system that analyzed complex interactive situations
in terms of player expectations, available actions, and
their consequences would allow the deliberate construction
of more complicated and open-ended narratives.
Finally, a system capable of deliberate choice creation
might enable new and more dynamic forms of narrative. This
is an eventual aim of Dunyazad: to create a branching story
with myriad paths where the freedom to explore a huge possibility
space is enabled by collaboration between a human
author and a computer system. If generative tools could
enable human authors to design entire narrative possibility
spaces, the resulting fictions would be the products of both
human and machine creativity.
Acknowledgements
The authors would like to acknowledge the support of NSF
grant IIS-1409992.
<references_biblio/>
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