Models for Interactive Narrative Actions

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 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. IE '09, December 17-19, 2009, Sydney, Australia. Copyright © 2009 ACM 978-1-4503-0010-0/09/12...$10.00.

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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