Models for Interactive Narrative Actions
Ulrike Spierling
FH Erfurt
Altonaer Str. 25
99085 Erfurt, Germany
+493616700 -646
[email protected]
ABSTRACT
In Interactive Storytelling, authors need to conceive events in an
indirect way, which differs from traditional storytelling that
assumes to pre-define a linear order of narrated events. Actions of
characters are to be described including the whole acting situation
as conditions before and after the action. This concept is
compared with narrative theory and illustrated by a practical
authoring example. The goal is to find general conceptual models
and a vocabulary for authors in Interactive Narrative.
Categories and Subject Descriptors
H.1.2 [User/Machine Systems]: Human factors
General Terms
Design, Human Factors, Theory.
Keywords
Interactive Storytelling, Authoring, Conceptual Models.
1. INTRODUCTION
Interactive Storytelling has been and will be an important research
topic within the realm of interactive entertainment. There are
several challenges involved in the realization of interesting and
suspenseful story artifacts to interact with.
First, there is the need that a digital artifact can provide
meaningful responses to user’s actions, while “automatically”
maintaining a kind of dramatic discourse. At previous conferences
on Interactive Storytelling [15], several solutions have been
presented how dedicated software – e.g., a story engine, or a
drama manager, or a director agent – address this problem of
creating a logical flow of causally dependent events.
Second, there is a big challenge for authors to conceive and create
content in such a way that it runs smoothly with such story
engines. Previous attempts to overcome this problem have been
mainly focusing on proposing so-called “authoring tools”. They
mostly address the difficulty for authors “to program” the engines
by supporting them with GUIs, easing the effort of correct coding.
However, most of these authoring tools currently constitute
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anecdotal examples how input of content can be accomplished for
one specific engine approach. There is only some sparse and tacit
agreement among researchers about the harmonizing of design
steps for creation across different approaches. Of course this can
be explained by a general immaturity of the field and by the
diversity of approaches of engines.
The concepts described in this paper are part of an undertaking to
suggest general conceptual models for authors in highly
interactive digital storytelling (IS). It builds on previous work
[21] and puts an emphasis on the comparably “simple” problem of
defining actions, states and events in a storyworld that can be
“run” with some existing story engines for IS.
Theoretical bases and technical conceptions of actions and events
logic have been covered in lots of recent publications (see section
2), within different disciplines, such as in Narratology or
Artificial Intelligence (AI). However, there is not yet an
applicable and simple bridge to practical authoring. Potential
creative authors are left confused when being directed to papers
from the AI community. Some of many problems are:
 AI engines and methods appear obscure for authors from
non-computer-science areas. In fact, many concepts
tackled are immanent to approaches in AI planning (e.g.,
[19, 24, 25, 26]) but are hard to grasp and hard to
visualize, also because of a lack in available playable
prototypes.
 Naïve authoring approaches using branching paths are
generally too linear to suffice for highly interactive
storytelling, which means granting end-users frequent
participation in the storyworld.
 Of 60 respondents in recent IS lectures and workshops
(many of them computer scientists), more than 50% used
branching structures to visualize their initial concepts of IS
in a questionnaire – addressing the ramified narrative flow
as their understanding of the main IS problem.
The IS research community widely shares the belief that
branching of linear story paths is a dead-end approach for
achieving satisfying experiences of user agency in interactions
with stories. From the authoring point of view, it is often more
intuitive to think in terms of a branching plot that can be mapped
by a graph visualization. An often-heard answer to this problem is
that authors need to be able to program, or at least be procedurally
literate, even if programming is supported by authoring GUIs.
On top of creating GUIs, there is a need to identify simple
primitives for creation, which do not contradict with the potential
dynamics of future procedural approaches. There is a need to start
to educate authors with systems that are as simple as possible, but
still distinguishable from those using linear branching methods.
Better educated writers for IS then could contribute a lot to the
successful development of future tools.
As a first “general” concept, regardless of the particularities of an
engine used, one can state that principles for conditional actions
and events form the basis for creation in IS. Traditionally, a story
is considered to be a sequence of events. For IS, authors have to
rethink the creation of event structures, as story events and states
can be influenced by actions of the participating user. Therefore, a
general elementary assumption for writers should be: “In
Interactive Storytelling, there is no unconditional action or event.”
Rather than a restricting definition of Interactive Storytelling, this
is meant to be part of a conceptual image for creation. Indeed, for
practitioners in AI, who are familiar with agent modeling and
behavior, this may seem rather trivial. For storytellers, it can be
more difficult. The main difficulty lies in a form of vicariousness
of the implicitly resulting storyline, which is quite opposite from
traditional so-called “linear” storytelling.
1.1 Two Dimensions of Implicit Creation
In traditional, non-interactive drama and narrative, conditions for
actions are inferred by the audience from the action representation
alone. According to several scholars in narrative [1, 13], our
experience of story is actually a construction, something “put
together” by inference from what we see, hear or read – that
means, from some concrete representation of events. During
conception of any story, authors are aware that concretely and
orderly described actions of a character are situated in a range of
possibilities. These options for action are almost as important as
the character’s chosen actions and their illustration contributes a
lot to the suspense and immersion of the audience. Suspense is at
work even if these possibilities are not at all explicitly illustrated,
but just implicitly existent in a shared cultural background.
Generative approaches to Interactive Storytelling, in which
representations of events and states are inferred by a story engine
from abstract behavioral models of AI-based agents, turn this
concept upside down [27]. What the author needs to define is not
the explicit order of events, but rather the abstract story as a
dynamic world model of states and rules, from which appropriate
actions and events can be implicitly inferred through the story
engine or digital drama manager (see Figure 1, right). During
runtime and the interaction of a user with the content, this
generative technique may lead to more flexibility in the possible
reactions to user events than with explicitly authored actions.
Hence, during IS authoring, not sequences but situational
conditions of events have to be made explicit in order to let an
engine generate the action progression (select proper actions) for
characters dynamically.
Figure 1. The concept of a storyworld
in the context of narrative interpretation (left) and generative
systems (right)
Working with a specific engine means that authors in the end
need to have applied knowledge about the engine’s “mind” or
used formalism. It has been found useful when authors are
procedurally literate, which often has been compared with “being
able to program”. [17] But it is more complicated. With implicit
creation, there are several “unknowns” at the time of creation.
Authors have to conceive at a high abstraction level, and leave
detail to an engine. There are two dimensions of concretion
following from implicit stipulations (see Figure 2).
Figure 2. Two dimensions of potential influence from
abstract levels.
Both dimensions in Figure 2 refer to automatic generation of
actions. First, the arrow to the right is related to the ad-hoc
selection of the next situated actions and events suited to build the
narrative flow, as a sequence of events. The arrow downwards
concerns the situated shaping of the representational levels. This
can e.g. stand for 3D rendering and behavioral animation, or
concrete wording of dialog as in the example in section 3.
This indirect creation of possible actions and events before the
time of interactive narration was described as implicit creation in
[21]. Figure 1 shows the two opposed meanings of a “storyworld”
in this context, including an indication where the creative work is
applied. The left “bottom-up” part shows the storyworld as the
recipient’s mental model, built by interpreting states, actions and
events that a storyteller describes explicitly (after Herman [13]).
The right “top-down” part depicts the storyworld as a designer’s
created dynamic model with rules, leading to implicitly generated
sequences of states, actions and events.
The two dimensions are highly dependent on each other. In an
ideal computational storytelling system, it would be possible to
formalise and therefore abstract all actions. However, current
prototypes are often incomplete, meaning that the demand of
generating all story events would be too high for existing
formalisms. Further, the merit of “completely generated” stories is
also unclear, and still needs to be researched. Therefore, alongside
algorithmic decisions, hard-coded shortcuts are often used to
make ends meet. [22] In practice, there is more likely to be a
combination of explicit and implicit authoring, or in other words,
a combination of authored and generated events.
In order to be able to create events implicitly, it is necessary to
anticipate to a certain extent what is going to happen under the
specified conditions.
A traditional linear authoring approach would define the sequence
of events during experienced narrative time in a fixed order, even
if with opening branching possibilities. Approaches to AI story
engines tend to break hard connections of branches and replace
them with a more or less complex selection mechanism for the
next actions. Sophisticated story engines use AI planning, others
use state machines. Other engines control the shape of behavior of
virtual agents (for example, graphical engines). In the practical
example discussed later, action selection is done by a state
machine. The non-linear approach to authoring does not let define
a sequence of events, but a sequence of possibilities taking into
account changing world states. The task is to create a model of the
possible actions in the world.
2. MODELS OF ACTIONS AND STATES
Models for the sequencing of events in narrative can be found in
Narratology and in AI literature, stemming from logical formulas
for actions and states. AI formalisms recently used in IS for
automatically structuring content often follow one or more
narrative formalism, mostly of those stemming from
structuralism. Cavazza and Pizzi [6] gave an overview on
narrative theories and pointed out their applications in Interactive
Storytelling. At first sight, it is obvious that theories including
alternative possibilities for actions are more interesting for IS than
those caring only about identifying certain actions at typical
stages of the narration, which is fixed in order and looked upon
backwards (from the end) for causal relations. In [6], Bremond’s
approach of action deliberation of an agent has been highlighted
for Interactive Storytelling, which includes the consideration of
the situation of a possible action and anticipated consequences for
selection of the next action taken.
computational models of state machines or models of dynamic
worlds in games and emergent systems. Here, an “action” is
mainly judged by its potential for world transformation. G.H. v.
Wright quoted in [13] p.74: “An agents life situation […] is […]
determined […] by his total life behind him and by what would be
nature’s next move independently of him.” The term “nature” here
includes actions of other agents. The quote also refers to those
necessary actions that prevent something from happening.
In current visions of highly interactive storytelling, also the user
is an agent, influencing the world’s state changes (bring about
changes or prevent changes). Creating a model that leads to acting
situations with narrative interest for users is a task of the author of
an interactive storyworld.
In practice, events are often equated with actions, as they change
states in the world. However, some event types have been
distinguished [4], for example “actions” as certain types of events,
which are intentionally executed by agents to accomplish
(immediate) goals. Herman [13] illustrated by comparison of
different theories such as of Vendler and Frawley, that there are
possible linguistic constructions that make it even hard to
distinguish between action/event types and states in literary
narrative. His example: “She is taking a swim out in the ocean”
can refer to her action of swimming that leads to a state change,
but also to a state itself, implied by the continuous verb tense.
Also, the problem of parallel compositions of actions with
durations has not yet been tackled properly by that simple state
description.
Another, much simpler concept of Bremond’s narrative theory is
that of the “elementary sequence” [3] of three functions for an
action: the possibility for action, the actualization itself, and the
result of the action (see Figure 3). This concept supports basic
interactivity, because it contains a simple progressive logic, one
in which choices can be made by the agent, instead of the
teleological finality of story denouement orientation. Because step
1 determines a possibility, step 2 also contains the option for a
choice to refrain from the action, and step 3 can consist of either
success or failure of the actualized action.
In AI planning widely applied to IS research projects, the
definition of a triad of “pre-condition – action – post-condition”
as a proposition of possible events is very common, e.g. in [18,
19]. It can be compared with v. Wright’s and Bremond’s minimal
sequences. For example, when using a STRIPS-like planning
formalism, it is necessary to specify facts (propositions) about
each possible action. These propositions specify pre-conditions
that are first to be checked as being “true” to execute an action, as
well as a list of changes to the world appearing after the action
would be executed, mostly by adding or deleting sentences about
facts and properties in the world. In short, while using a
formalized description language, situations for possible actions
are described.
Figure 3. Bremond’s elementary sequence, following a logic of
progression instead of a logic of finality [3]
The concept of pre-conditions and effects of actions is so crucial
for modeling acting situations that it is possibly a requirement for
authors to adopt, when authoring for IS engines. Given that it also
relates to narrative theory, it should be expected that without
programming skills, the concept should be learnable. Still,
because there are cases of actions that are not yet properly
covered (see above), the concept may be perceived as a rigid
formula that in some cases will be counter-intuitive to use.
Another drawback is that when automatic planning is used, it
might get hard for authors to anticipate the resulting flow of
events.
The prerequisite for perceiving any “possibilities” for action is the
appraisal of an acting situation (done by the acting character in
the storyworld, as well as by the audience!). Herman [13] refers to
von Wright’s “logic of action” where he gives a similar definition
of 3 steps, but related to the concept of “state”. For v. Wright, the
smallest descriptive action unit is a state description. The most
important aspect of action is a change in the world, respectively,
the change of some “state of affairs”. The three parts of an acting
situation are: 1) the initial state of the world, 2) the end state after
completion of the action, and 3) the state in which the world
would be without the action. This model is interesting because a
“state change” is the emphasis for action, bringing it closer to
3. ACTIONS AND STATES IN
INTERACTIVE STORYTELLING TOOLS
In the following, an example is given of a realization of the claim
made for authoring: In interactive storytelling, there is no
unconditional action or event. Further forms of conceiving IS
content are discussed for their suitability.
3.1 Example: Modeling Dialogic Actions
In this section, a case of authoring an IS storyworld is described.
Scenejo is a conversational storytelling platform. Its architecture
and an authoring example were described in [23] and [21]. Two
virtual characters talk to each other and can be interrupted by a
user’s text chat. The only possible actions for characters are
dialog acts, which are represented as concrete utterances, hearable
as text-to-speech by talking heads floating in space. A user can
interact in almost the same way, by expressing utterances through
using the keyboard. These utterances are recognized by an
A.L.I.C.E. chatbot pattern matching function [2], trying to match
the perceived wordings to a more abstract dialog act.
This interaction paradigm was used to author and implement a
moderation game, setting the objective for the user to settle a
dispute between virtual characters. Within the realm of possible
IS artifacts, this one has an emphasis on frequent user interaction,
influencing short-term situations in a conversation. However, it
differs from traditional human-computer dialog systems, as it is
not a one-by-one conversation (see Figure 4). The user interaction
is similar to that of the Façade system [17], which has a much
more complex system architecture supporting sophisticated
context recognition, but no authoring system.
Figure 5. State chart of a dialog sequence for one bot.
As a post-condition, it can be stated that the output of that
sentence has the effect of at least providing another keyword to
react upon. The underlying engine required a formalization of the
dialogs based on the philosophy of AIML chatbots. Therefore at
first, within the authoring tool, utterances were represented as
pairs of a stimulus with a response. Figure 6 shows the first editor
for defining a stimulus-response pair. The arrow indicates that
there is not only an input and output part of the rule (left/right),
but also a concrete and an abstract (lower/upper) level of the
dialog act. For example, the “new jobs” argument in the airport
debate can be expressed by several wordings, leading to
variability in the actual representation.
Figure 4. Screen of the conversational game.
The spoken actions were conceived in an experimental way by
using the authoring tools and architecture of Scenejo, which is
based on writing AIML “chatbot” stimulus/response dialogs. [2]
The tool also contains a graphical possibility to structure and
visualise dialogic flow states. ([23], see example in Figure 5).
This graph visualization was originally introduced to address the
issue mentioned in the introduction, namely that authors found it
more intuitive to think of the ramified flow of the possible
dialogic events. It also reflects other approaches followed in
modeling dialogs for IS. (See section 3.2 [10, 16]) In our case, it
assumes a character-centric approach, which means that for each
bot in the conversation, one flow of possible dialog acts like that
in Figure 5 had to be created. The creative process suggested that
at first, concrete utterances were imagined and put together in
some orderly, script-like manner. For a linear dialog of two bots,
both have to have their own flow charts, containing connectional
features that make their statements intertwine perfectly. Because
of the underlying AIML pattern matching principle, these
connectional features are word patterns functioning as catchwords
that cue the other bot. In terms of pre-conditions of an action, it
can be said that “hearing” certain keywords is a pre-condition for
uttering a certain sentence.
Figure 6. Authoring interface of a Stimulus-Response unit
(left: AIML pattern input, right: utterance output).
On a conceptual level, these stimulus-response pairs of two bots
could be juxtaposed and intertwined as shown in Figure 7.
Although this concept of actions with preconditions can be
applied to all sorts of actions (including physical actions), dialog
actions build a special case, as they often form adjacency pairs
[20], or even longer sequences of turn-taking unlikely to be
interrupted, resulting in a chain.
Not before the structure of a first dialog was finally completed,
the authors were thinking of the actual effects of these words as
actions influencing the overall storyworld. Also, the question
came up late of how interacting users can achieve meaningful
effects in the storyworld. Drawing from these naïve, novice-level
experiences in IS authoring, the result had to be rethought. When
the user joins into the conversation, technically the same
mechanisms as those between the bot characters apply.
Figure 7. Dialog chains of two separately modeled
interlocutors by connecting their output to preconditions.
User’s potential utterances are to be conceptually included from
the start, at first again as preconditions for certain bot reactions.
Further, it became necessary to model world states that were
changeable by the dialog, for example, the overall stress level in
the debate that was raised by uttered “killer phrases” of the bots.
This process of model creation was described in [21]. The design
tasks consisted mainly in identifying critical incidents and
parameters suiting the purpose of the application.
Essentially, what is shown here is that independently of the way
of authoring, a conceptual model of “acting situations” can be
applied, and all dialog elements can be described with the
minimal action triad mentioned in the previous section. Figure 8
shows examples of minimal triads of action description that can
be conceived. It is an alternative representation of possible actions
to the stimulus-response pairs shown in Figure 7.
precondition, due to the AIML-based logic. One could always
place a wildcard as that precondition, which might result in
random chat. Also, one can force reactions to a previous utterance
of other characters by constraining the pre-condition towards only
one or few possibilities, resulting in a linear dialog (see Figure 7,
below). This example shows that depending on the specific design
of the pre-conditions, different grades of interactivity or
variability can be assumed.
With this new structure of explicitly modeled pre-conditions for
each utterance, a user could potentially join in at any situation of
the conversation, depending on the author-chosen variations in
constraining these pre-conditions to more or less precise other
utterances. Of course, specifying pre- and post-conditions for
every uttered sentence places a burden on the author, making the
task of dialog writing quite tedious. We still need more
experience and develop heuristics about when to allow users to
interrupt in a given dialog. As the lower part of Figure 7 suggests,
there are such triggers that prompt a new chain of exchanges,
which is at first unlikely to be interrupted at any step, for a certain
amount of turns. This is due to the necessity of context in human
conversation management – which can simply be the occurrence
of adjacency pairs, in which – for example – answers have to
follow questions, or, more complex structures such as those called
“storytelling sequences” by Schegloff [20]. In our debate
example, certain arguments and also provocations were triggered
that led to short linear conversations. The most important thing
was to provide enough triggers for such linear mini-conversations,
so that many variations can occur.
In the beginning of getting acquainted with the system, authors
conceived dialogs that were too long and too linear. With the
concept of inserting post-conditions as state changes at almost
every utterance, the interaction with the dialog became less
boring. Beyond hearing the characters talk, it was necessary that
something had to be perceived as a consequence, an “effect”, to
make it more entertaining, for example, to watch the stress level
increase, or to experience how the own moderating actions
influence it. Therefore, another pre-condition besides text patterns
can be a check for certain parameter states.
To sum up and compare with the model presented in Figure 2, it
can be said that the flow and order of some events has not been
explicitly defined, but implicitly authored in a way that allows for
variations. This can be achieved by defining actions with preconditions and post-conditions rather than with explicit and
unconditioned connections in a runtime flow graph. In the running
prototype, the dialog engine was managed by a simple state
machine with no AI planner, therefore the high-level dialog flow
was explicitly authored (connections were hard-coded). For
example, there is a branching decision point at which the debate
either escalates or leads to a positive end, based on the game state
so far.
Figure 8. Utterances as abstract actions (dialog acts),
with preconditions and effects.
Figure 8 also mentions some preconditions (e.g. “have turn”) that
in the running example actually were hard-coded in the dialog
engine. They are mentioned here nonetheless, because in a
thinkable future version of the system, they shall be made
accessible for authors, to allow the definition of special turntaking rules. Placing a text pattern (“hear X”) was a mandatory
Figure 9 shows how the top-down dimension of representation in
Figure 2 has been applied to the Scenejo concepts. Each action is
defined on an abstract dialog act level and on a concrete wording
level that is finally spoken by the text-to-speech. This concept
was found necessary to get an overview of some logic flow of the
dialog, independent of knowing the exact spoken text. It also
allows that alternative text is added to perform the same abstract
dialog acts, which results again in some variations of the
experienced conversation.
connecting arrows to constrain actions or dialogs sequentially, in
order to delimit the number of possible following acts during
interaction. In Scenejo, this has been applied to model groups of
utterances as parts of ongoing arguments in the sense of
conversational “storytelling sequences”. [20] Really complex
systems could be modelled as well, but mostly by sacrificing
clarity of the visualization.
Figure 9. From abstract storyworld (top) to concrete wording.
3.2 State Charts
The emphasis of this article is to motivate the conceptual
modeling of all interactive storytelling events as “conditional
actions” of agents, situated in changing states of the storyworld.
One of the motivations for that was the existence of such models
in narrative theory, as well as in the logic of action as a
foundation for AI story engines. The practical consequence shown
in the last section was that authors had to divide a storyworld into
“possible” actions and states, while actions depend on as well as
influence state conditions.
When discussing the concepts of events, states and actions in the
context of the interdisciplinary field of IS, however, there is some
ambiguity. For example, “state” and “event” are widely applied
terms in computing in general, where they may be used with
different intentions. This applies, for example, in state chart
modeling [12], and in the conceptual modeling of emergent
systems [14] and simulations.
These concepts are also relevant for Interactive Storytelling. For
example, for modeling of conversations and other interactions in
IS, several existing authoring tools include the visualization of
state charts or directed graph representations. Scenejo (see Figure
5) is one of them, at least partially; other systems include Cyranus
[16] or Crosstalk [10], in which graphs can be hierarchically
ordered. Prism [18] uses a combination of so-called story graphs
and planning. The motivation of using state charts to represent
“plot” or narrative is to get a better overview of the flow of
possible actions.
Typically, these charts are represented by nodes (“states”) that are
connected with edges or arrows (“transitions” between the states).
Confusingly, in most of the authoring tools including Scenejo, the
state nodes contain the actual actions of agents to perform (see
Figure 5), and the edges are meant to be “events” stemming from
an external source (for example a user, the environment, or
another character), sometimes labeled with “guarding” conditions
[12]. In that sense it becomes clear that such a directed graph is
suited best as a visualization of “reactive” systems, as pointed out
by Harel [12] – for example, one user interacting with a whole
system, or story.
The concept of a “state” is meant here to be a state within the
performed flow of the ongoing narrative, laid out sequentially in
time. All performance states in this sense build a transition
network including the interactions of users as external events. It
differs substantially from a state in the storyworld, or of a “state
of affairs” for one out of many characters, as described above in
section 2. Authors can use graph visualizations including
Directed graph modeling constrains the narrative flow structure
that exists at runtime to a pre-conceived order of events and to
branches considered by the author. In the case of modeling
actions with pre-conditions and post-conditions that are different
from direct connection with other actions, more freedom and
variations are possible at runtime, with the drawback that it is
hard for authors to fully anticipate the outcome. Beyond
authoring, some IS engines use AI planning to automatically
generate this runtime graph (either before runtime or in “real
time”). [26, 19]
3.3 Actions and States in IS Systems
Today, the idioms used within existing IS authoring tools or
concepts of story engines are far from being harmonized. As
shown in the last section, it is even a difficult task to decide on
the exact meaning of the most common terms, such as “event”
and “state”. While this can be explained by the different existing
formal and technical approaches, it induces an additional
difficulty for authors and newcomers in the field.
In many existing tools, especially those employing AI
formalisms, the possibility of modeling actions in the sense of
Bremond’s elementary sequence – as triads of pre-condition,
actualized action and effect – is existent. However, it is often not
perceived by authors as a potential central concept for modeling.
Also the original Scenejo tool (see above) used a different
approach (more related to the chatbot-based engine
characteristics). Sometimes it is accessible only in programming
code, and terminology used is often rather technical or
proprietary.
For example, Storytron [9] uses this idea behind its proprietary
terms of inclinations and consequences. For each occurring event
in the storyworld, each character checks which role to assume for
a possible reaction, and compares different possible actions for
their degrees of desirability, by taking into account character traits
and goals. The authoring tool provides a graphical programming
interface for these complex dependencies. Also IDtension [24]
includes a more complex comparison of values (e.g. ethical
values) for different possible tasks in a precondition for an action,
whereas the definition for authors is distributed in the code. The
terminology is derived from task analysis, starting from goals of
an actor. The authoring tool for Emo-Emma in the Madame
Bovary project [19] directly employs the underlying STRIPS
planning terminology for defining propositions including preconditions and post-conditional operators.
In the non-technical widest sense, a pre-condition describes the
acting possibility and the acting situation. Since it also determines
action possibilities for users, it is the basis for influencing the
narrative discourse during user interaction. “Narrative discourse”
here determines the flow, duration and order of events [11]. For
interactive storytelling, it can be assumed that this order can vary
depending on user interaction and on the possibilities “planted”
by the author. Defining the concrete form and shape of the
actualization of an action and of the post-condition (the effect of
the action) depend highly on the way in which an IS system
integrates representational levels (the top-down arrow in Figure
2). There is a variety of forms, as there are different ways of
expressing “narrative statements” in traditional media, e.g.
pointed out by Chatman [8]. Classical distinctions include the
decision for “telling” or “showing” an event (to recount or to
enact), or the two basic forms of depicting a character’s speech in
“direct” or “indirect” discourse.
In Scenejo, actions are represented as hearable spoken utterances
of talking heads, while the user types verbal contributions. All
utterances are performed as “direct speech”. Nevertheless,
internally, the dialogic flow is processed on the higher abstraction
level of “dialog acts”. In the existing interface, the effect of an
action can be rendered as a changing parameter value in a kind of
“dashboard” style, combining the role play performance with a
simulator gauge, for example for the increasing or decreasing
stress level value. On the other hand, for example, Storytron [9]
uses an abstract language of verbs for actions, used directly on the
representation level. Also the player of the storyworld constructs
actions by combining so-called “word sockets” to a sentence, on
that same abstraction level of representation. The Storytron
engine directly operates on this linguistic representation, by using
a specially designed toy language called Deikto. All actions are
“told” in indirect discourse, while there is no specific element for
showing a state, except through adding verbal expressions of state
values within a constructed sentence. Further, it is possible to
depict mood states by explicitly adding face pictures to actions.
Other examples of systems, such as Emo-Emma [7] and FearNot!
[25], represent actions through complex graphical animations,
smoothly integrating information about post-conditional “state”,
especially emotional state, into a rendered reaction. For these
integrated systems delivering full enactment, the complexity
increases, as rules of acting and cinematography have to be
integrated on the expressional level.
These examples show only a subset of possible IS systems today,
which derive a great variety of specific terminology from their
research aims, used technology and incorporated formalisms. For
novice authors and writers with no background in computer
science, there is a need to look for generic concepts that are
shared by all the systems. Ideally, this should result in suggestions
for a vocabulary for authors in Interactive Storytelling.
4. CONCLUSION
This paper is part of an endeavor to establish general conceptual
models for the creation and authoring in Interactive Storytelling.
It is part of an activity in the IRIS network of Excellence [5]
funded by the European Commission. While there are general and
probably persistent difficulties for authors, having their source in
the immanent difficulty to anticipate the flow of events during the
interaction with a user, as well as with the employment of
complex AI technology, the situation needs to be improved.
Among other things, one step is to define principles and steps of
creation that are unique for Interactive Storytelling, distinguishing
it from other forms of narrative creation. As one creative
principle, a simple presumption has been suggested here: “In
Interactive Storytelling, there is no unconditional action or event.”
It is claimed that following this presumption through the
conception of the artifact will lead to more potential interactivity
in the resulting IS experience, than with the notion of a story as a
sequence of events. This has been illustrated by one authoring
example, in comparison with the modeling of state charts. The
conceptual model of a triad of action has its roots in Narratology,
for example [3], which is concerned with the analysis and
perception of narrative. Applying it to generative systems means
defining perceivable acting situations as models for actions.
Further work will include the definition of a vocabulary for
authors as part of educational material for creation and
conception, and the suggestion of integrated authoring tools.
5. ACKNOWLEDGMENTS
This work has been funded (in part) by the European Commission
under grant agreement IRIS (FP7-ICT-231824). [5]
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