Interacting with virtual characters in interactive storytelling.
In recent years, several paradigms have emerged for interactive storytelling. In character-based storytelling, plot generation is based on the behaviour of autonomous characters. In this paper, we describe user interaction in a fully-implemented prototype of an interactive storytelling system. We describe the planning techniques used to control autonomous characters, which derive from HTN planning. The hierarchical task network representing a characters' potential behaviour constitute a target for user intervention, both in terms of narrative goals and in terms of physical actions carried out on stage. We introduce two different mechanisms for user interaction: direct physical interaction with virtual objects and interaction with synthetic characters through speech understanding. Physical intervention exists for the user in on-stage interaction through an invisible avatar: this enables him to remove or displace objects of narrative significance that are resources for character's actions, thus causing these actions to fail. Through linguistic intervention, the user can influence the autonomous characters in various ways, by providing them with information that will solve some of their narrative goals, instructing them to take direct action, or giving advice on the most appropriate behaviour. We illustrate these functionalities with examples of system-generated behaviour and conclude with a discussion of scalability issues.
Figure

1).

Figure

1. A story instantiation generated by the system: Ross
asks Phoebe Rachel's preferences, but Phoebe lies to him.
The graphic environment for our system is based on the Unreal
computer game engine. Other researchers in the field of
interactive storytelling have previously described the use of the
same game engine [12] [13], which is increasingly used in non-
gaming applications, since the work of [15]. The main advantage
of a game engine is to provide both high-quality graphics and a
seamless integration of visualisation and interaction with the
environment objects. Further, the software architecture offers
various modes of integrating software, via C++ plugins or UDP
socket interfaces, through which we have integrated a
commercial speech recognition system.
3. NARRATIVE REPRESENTATIONS FOR
CHARACTER-BASED STORYTELLING
As a general rule, character-based storytelling systems do not
represent explicitly narrative knowledge, such as narrative
functions or decision points, as in [5] or [7], which could be
direct target for user interaction. For instance, in the system
described by Sgouros et al. [7], the user is prompted for strategic
decision to be made, and narrative causality is maintained via an
Assumption-based Truth Maintenance System (ATMS), a
process described as user-centred plot resolution. In the
interactive storytelling authoring system of Machado et al. [5],
narrative events are generated using a description in terms of
narrative functions inspired from Propp [16], which can
constitute basic building blocks for the plot.
The Story Nets described by Swartout et al. [2] correspond to a
plot-like representation of the consequences of user action.
However, unlike with user-centered plot resolution [7], these plot
models need not be explicit and can be derived from rules
operating on key decision points corresponding to user actions
[17]. This system integrates aspects from both plot-based and
character-based systems. It is however strongly centred on user
behaviour and its nominal mode assumes permanent user
involvement.
On the other hand, in character-based approaches, the plot is
generated by the multiple interactions between autonomous
characters. The problem with which character-based systems are
generally faced is to ensure that the actions they take are
narratively relevant. This corresponds to the narrative control
problem and has been studied by Young [13] and Mateas and
Stern [18] among others.
In our system, the plot should be mainly driven by the synthetic
characters, which is the only approach supporting continuous
storytelling with anytime user intervention. In order to reconcile
the character-based approach with the problem of narrative
control, we describe characters' behaviours in terms of roles, i.e.
a narrative representation of their goals and corresponding
actions.
For instance, our principal character, Ross, plans to seduce the
character Rachel. His role can be described into greater details as
a refinement of this high-level goal. Such a refinement will
define the various steps he'll take in seducing Rachel, such as
acquiring information about her, gaining her friendship, finding
ways to talk to her in private, offering her gifts, inviting her out,
etc. These also correspond, at its first level of refinement, to the
various stages of a (yet linear) story. However, this role
representation also includes, as it is refined, a large set of
alternative solutions at each further level. The terminal nodes
correspond to the final actions actually played on-stage through
3D animation of the synthetic characters. They consist in
interactions with on-stage objects (watching TV, reading a book,
buying gifts, making/drinking coffee) and other members of
the cast (talking, socialising, etc.

Figure

2. HTN representation for character behaviour.
The characters' roles can thus be represented in a consistent
fashion as Hierarchical Task Networks (HTN): this represents an
actor's potential contribution to the overall plot (see Figure 2). A
single HTN corresponds to several possible decompositions for
the main task: in other words, an HTN can be seen as an implicit
representation for the set of possible solutions (Erol et al., 1995).
This naturally led us to investigating the use of HTN planning
techniques to underlie characters' behaviour [12] [13]. In the
next section, we describe our approach to planning for
characters' narrative behaviours and how these have been
extended to incorporate user intervention.
4. PLAN-BASED BEHAVIOURS IN
There is a broad agreement on the use of planning techniques for
describing high-level behaviour of autonomous agents embodied
in virtual environments, both for task-based simulation [19] [20]
and for character-based storytelling [12].
Our description of characters' roles as HTNs naturally led to use
these as a starting point for the implementation of a planning
system. HTN-based planning, also known as task-decomposition
planning, is among the oldest approaches for providing domain-specific
knowledge to a planning system. While in the generic
case HTN planning may be faced with practical difficulties [21],
this approach is considered appropriate for knowledge-rich
domains, which can provide applications-specific knowledge to
assist plan generation [22]. Interactive Storytelling constitutes
such a knowledge-rich application, not least because of the
authoring process involved in the description of the baseline
story. Besides, there has been a renewed interest in recent years
for HTN planning [23], which has demonstrated state-of-the-art
performance on a number of benchmarks.
Interactive Storytelling requires interleaving planning with
execution. We have devised a search algorithm that produces a
suitable plan form the HTN. Taking advantage from our total
ordering assumption and sub-task independence, it searches the
HTN depth-first left-to-right and executes any primitive action
that is generated, or at least attempts to execute it in the virtual
stage. Backtracking is allowed when these actions fail (e.g.
because of the intervention of other agents or the user). This
search strategy is thus essentially similar to the one described by
Smith et al. [24]. In addition, heuristic values attached to the
various sub-tasks, so forward search can make use of these
values for selecting a sub-task decomposition (this is similar to
the use of heuristics described by Weyhrauch [25] to bias a
story instantiation). These heuristic values are used to represent
narrative concepts as well. Namely, the various tasks are
associated features that index them on some narrative dimension
(such as the sociable nature of an activity, or the rudeness of a
behaviour), which in turn are converted into heuristic values on
these dimensions. Using these heuristics according to his
personality and emotional status, a character will give preference
to different tasks. These heuristics can be altered dynamically,
which in turns modifies subsequent action selection in the
character's plan. For instance, Rachel may change mood because
some action by Ross has upset her; the consequence is that she
would abandon social activities for solitary ones.
Another essential aspect of HTN planning is that it is based on
forward search while being goal-directed at the same time, as the
top-level task is the main goal. An important consequence is that,
since the system is planning forward from the initial state and
expands sub-tasks left to right, the current state of the world is
always known, in this case the current stage reached by the plot.
We have adopted total ordering of sub-tasks for the initial
description of roles. Total-order HTN planning precludes the
possibility of interleaving sub-tasks from different tasks, thus
eliminating task interaction to a large extent [23]. In the case of
storytelling, sub-task independence is an hypothesis derived from
the inherent decomposition of a plot into various scenes, though
with the additional simplifying assumption that there are no
parallel storylines.
There are however additional requirements for planning
techniques that control synthetic actors. The environment of the
synthetic characters is by nature a dynamic one: the world in
which they evolve might constantly change under the influence of
other characters or due to user intervention. This would
traditionally call for an approach interleaving planning and
execution, so that the actions taken are constantly adapted to the
current situation. In addition, the action taken by an actor may
fail due to external factors, not least user intervention. The latter
requires that characters' behaviour incorporate re-planning
abilities. As we will see in section 5, these features also support
the interactive aspects of storytelling, allowing user intervention
to trigger the generation of new behaviours and the
corresponding evolution of the plot.
The behaviours for the various characters, corresponding to their
individual roles, are defined independently as HTNs. Their
integration takes place through the spatial environment in which
they all carry out their actions. As a consequence, their on-stage
interactions generate a whole range of situations not explicitly
described in their original roles.
Examples of such situations obtained with the system are:
1. Ross wants to steal Rachel's diary but she is using it herself,
or Phoebe is in the same room, preventing him from stealing
it
2. Ross wants to talk to Phoebe about Rachel, but she is busy
talking to Monica
3. Ross bumps into Rachel at an early stage of the story, where
he has not yet obtained information about her
4. Ross talks to Phoebe but the scene is witnessed by Rachel

Figure

3. Dramatisation of action repair.
These bottom-up situations illustrate why the characters'
behaviour cannot be solely determined by their top-down
planner, in order to be realistic. Situations 1 and 2 would
normally lead to re-planning, while more convenient solutions
can be devised, such as action repair [26]. In example 1 for
instance, Ross could just wait for Rachel to leave, which would
restore the executability conditions of the read_diary action
(see

Figure

3). Examples 3 and 4 represent situations that should
be actively avoided by the character. A practical solution consists
in using situated reasoning, implemented as sub-plans. These are
triggered by rules recognising the potential occurrence of such
situations and return active post-conditions to the initial plans
when it resumes. These mechanisms are further described in
[27]. Finally, characters also exhibit reactive behaviour based on
some situations: in some cases Rachel can get jealous if she sees
Ross in sustained conversation with another female character, or
Phoebe can get upset if Ross interrupts her. Reactive behaviours
can directly alter the character's plans or trigger scripted
response (such as leaving the room). In most cases, though, the
output of reactive behaviour is generally to alter the emotional
response of the reacting character, which in turns affects its
subsequent role. Altering the mood value is equivalent to
dynamically changing the heuristic coefficients attached to
certain activities. Hence, emotional representations, however
simple, play an important role in the story's consistency by
relating character behaviour to some personality variables.
Even though the individual mechanisms for actors' behaviour are
fairly deterministic, the overall plot generated is not generally
predictable by the spectator. Several mechanisms have been
incorporated to support, such as the random allocation of
characters on-stage, which together with the duration of their
actions, greatly affects the probability for encounters, which is a
major determinant of plot variability.
The important conclusion is that, while most user interaction
takes place through the characters' top-down plans, every
mechanism supporting an agent's behaviour is a potential target
for user intervention. This will be further discussed in section 6.
5. SYSTEM ARCHITECTURE
The system has been implemented using the Unreal game
engine as a development environment. The implementation
philosophy, like in previous behavioural animation systems is to
go from high-level planning to lower-level actions down to
animation sequences (which in our case are keyframe animation,
but can be interrupted at anytime in case of re-planning).
The game engine offers an API via its scripting language,
UnrealScript. Using this scripting language, it is possible to
define new actions out of basic primitives provided by the
engine; for instance, offering a gift, which consists in passing an
object from one character to another. The implementation of an
elementary action comprises the updating of graphic data
structures (e.g. the object list of a given character or of the
environment itself) plus the associated keyframe animation
played in the graphic environment.
Characters' roles are generated from HTN plans in the following
way. Each character's plan interleaves planning and execution;
the lowest-level operators of each plan are carried out in the
environment in the form of Unreal actions (Figure 4, 1-2), and
the action outcome is then passed back to the planner (Figure 4,
3). In terms of architecture the planning component is a C++
module, integrated in the game engine using a dynamically
linked library (.dll), which interfaces with the graphic
environment via the actions' representation layer programmed in
UnrealScript. Similarly, changes taking place in the environment
are analysed in this layer and passed back to the planner (Figure
4, 3).

Figure

4. System architecture.
6. PHYSICAL INTERVENTION ON THE
The user is a spectator of the unfolding 3D animation
corresponding to the generated story, but he can freely explore
the stage, being himself embodied through an invisible avatar.
This makes it possible for him to interfere directly with the
course of action by physical intervention on stage. In our
current system, physical interaction is limited to narrative
objects. The user can remove objects from the stage or change
their location, but cannot physically interfere with the actors, for
instance by preventing them to enter a room. This is meant to be
consistent with the spectator-based approach and its rule of
minimal involvement.
Many on-stage objects appear as affordances, i.e. candidates for
user interaction. This can be signalled either by their intrinsic
narrative significance or by their use by the synthetic characters
themselves. The former case is referred to as a dispatcher in
modern narratology [28]: a dispatcher is an object to which
choice is associated, triggering narrative consequences. For
instance, in our example scenario, roses and the chocolate box,
the potential gifts for Rachel, bear such properties and are a
natural target for user interaction. Dispatchers can also be
signalled dynamically. As the characters are acting rather than
improvising, their actions have direct narrative significance.
Hence, if Ross directs himself towards an object, such as
Rachel's diary or a telephone, this object acquires narrative
relevance and becomes a potential target for user interaction.
Other on-stage objects play a role in the behaviour and most
importantly the spatial localisation of the virtual actors. Coffee
machines or TV sets are used by the characters: if the user steals
the coffee machine that Phoebe was about to use, she would re-plan
some other activity, which might take her to another
location on the stage. As we have seen, moving to another
location can have significant narrative consequences.

Figure

5. Re-planning on action failure.
From an implementation perspective, actions that are part of the
character's plans are associated executability conditions, which
include the availability of some resources. For instance, Ross can
only read Rachel's diary if it is in the room and Phoebe will only
make coffee if the coffee machine is at its usual place. Physical
user intervention thus consists in causing character's action to
fail by altering their executability conditions. Action failure will
in turn trigger re-planning. For instance, Figure 5 shows a
fragment of Ross' plan for acquiring information about Rachel.
His initial plan consists in reading Rachel's diary, but the user
has stolen it. On reaching the diary's default location Ross
realises that it is missing and needs to re-plan a solution to find
information about Rachel, which in this case consists in asking
Phoebe. This is implemented using the search mechanism of our
HTN planner by back-propagating the failure of the action
read_diary to the corresponding sub-goal, so search will
backtrack and produce an alternative solution. From a narrative
perspective, the user has contrasted Ross' visible goal. But, apart
from the immediate amusement of doing so, because failure of
Ross' action is dramatised and part of the plot (see Figure 6), the
real impact lies in the long-term consequences of the resulting
situations. For instance, in the above example, when asking
Phoebe about Rachel, Ross might be seen by Rachel, who would
misunderstand the situation and become jealous!

Figure

6. Dramatisation of action failure.
This aspect becomes more obvious if considering the interaction
with objects used by secondary characters in their normal
activities. Phoebe's coffee machine does not have the narrative
significance of Rachel's potential gifts; however, displacing it
can have serious consequences as well, as she would move on
stage and might not be available to answer Ross, or could meet
Rachel. While this has proven to be a powerful mechanism for
story generation, at this early stage we have not explored its
impact in terms of user experience.
7. NATURAL LANGUAGE INTERACTION
WITH AUTONOMOUS CHARACTERS
Natural language intervention in interactive storytelling strongly
depends on the storytelling paradigm adopted. For instance,
permanent user involvement, e.g. in immersive storytelling or
training systems [2], requires linguistic interaction to be part of
the story itself. This most naturally calls for dialogue-based
interaction, as described for instance by Traum and Rickel [29]
for the same project.
Our own approach being based on a user-as-spectator paradigm,
the user interventions, including speech input are essentially
brief and can occur at anytime. They essentially take the form of
instructions or advice [19]. Speech input should be tailored to our
interactive storytelling context, in which the user influences
virtual characters, in order to implement a consistent user
experience. For instance, the utterance will often start with the
name of the addressee, as in Ross, be nice to Monica, not only
to identify the relevant character but also to establish a simple
relation between the user and the character he is influencing.
Also, the speech guidance should naturally be in line with the
various stages of the plot and correspond to narrative actions and
situations. The user can become acquainted with the possibilities
of intervention either by being introduced to the overall storyline
or, as otherwise suggested by Mateas and Stern [18], through
repeated use of the storytelling system.
There has been extensive research in the use of natural language
instructions for virtual actors. Webber et al. [19] have laid out
the foundations of relating natural language instruction to plan-based
high-level behaviour for embodied virtual agents. They
have also provided a classification of natural language
instructions in terms of their effects. Bindiganavale et al. [30]
have described the use of instructions and advice to influence the
dynamic behaviour of autonomous agents when dealing with
certain situations (checkpoint training). Though these are not
specifically addressing storytelling, many of these results can be
adapted to a narrative context.
We have incorporated an off-the-shelf system, the EAR SDK
from Babel Technologies into our prototype, which has been
integrated with the Unreal engine using dynamically linked
libraries like for the HTN system. The EAR SDK supports
speaker-independent input and allows for the definition of
flexible recognition grammars that include optional sequences
and joker characters. This makes possible to implement various
paradigms for speech recognition, from full utterance recognition
to multi-keyword spotting. At this stage we are experimenting
with a recognition grammar with optional sequences for added
flexibility and a small test vocabulary (< 100 words), which
includes the main actions and narrative objects, as well as some
situations.

Figure

7. Situational advice:
"Ross, don't let Rachel see you with Phoebe".
At this stage the natural language interpretation of user input is
based on simple template matching. We have defined templates
for several categories of advice, such as: prescribed action (talk
to Phoebe), provision of information to an actor (the diary is in
the living room, Rachel prefers chocolates), generic and
specific advice (don't be rude, be nice to Phoebe) and
situational advice (don't let Rachel see you with Phoebe (see

Figure

7)), etc.
The instantiation of the template's slots is carried out from
simple procedural Finite-State Transition Network parsing of the
relevant recognised elements. Consistency checking is based on
templates that contain role structures for a certain number of key
narrative actions that speech input is supposed to influence.
These are based on selectional restrictions for the various slots of
a given template. For instance, the advisee is often the main
character, especially when doctrine elements are involved.
The selection of the relevant candidate template is determined by
the semantic categories of verbs or action markers in the
sentence, which are used as heuristics to identify the best
template. It can be noted that there is no obvious mapping
between the surface form and the interpretation in terms of
narrative influence. For instance, talk to Monica is interpreted
as a direct suggestion for action (which will solve a sub-goal
such as obtaining information about Rachel), while don't talk to
Phoebe is more of a global advice, which should generate
situated reasoning whose result is to try to avoid Phoebe. As a
generic rule, though, it would appear that most negative
statements consist in advice or doctrine statements [19].
In our first series of test, we have been essentially focusing on
advice related to characters' behaviour, as they have the most
dramatic effect, and also as interaction with objects is often the
remit of physical intervention on stage.
Overall, we have identified various forms of natural language
intervention, such as: the provision of information to an actor
(including conspicuously false information), direct instruction for
action, warnings, and generic advice on the character's
behaviour.
In the next section, we give some examples of linguistic
interaction and relate these to the mechanisms by which their
effects on characters behaviours and on the plot are actually
implemented.
7.1 EXAMPLES
The direct provision of information can solve a character's sub-
goal: for instance, if, at an early stage of the plot, Ross is
acquiring information about Rachel's preferences, he can be
helped by the user, who would suggest that Rachel prefers
chocolates. The provision of such information has multiple
effects: besides directly assisting the progression of the plot, it
also prevents certain situations that have potentially a narrative
impact (such as an encounter between Ross and Phoebe) from
emerging. From an implementation perspective, sub-goals in the
HTN are labelled according to different categories, such as
information_goals. When these goals are active, they are checked
against new information input from the NL interface and are
marked as solved if the corresponding information matches the
sub-goal content.
[Ross I think Rachel prefers
chocolates]

Figure

8. Providing information to characters.
Provision of information can also be used to trigger action repair.
If for instance, Ross is looking for Rachel's diary and cannot find
it at its default location, he can receive advice from the user (the
diary is in the other room) and repair the current action (this
restores the executability condition of the read_diary action) (see

Figure

8). In this case, spoken information competes with re-planning
of another solution by Ross; The outcome will depend
on the timing and duration of the various actions and of the user
intervention (once a goal has been abandoned, it cannot, in our
current implementation be restored by user advice).
Another form of linguistic interaction consists in giving advice to
the characters. Advice is most often related to inter-character
behaviour and social relationships. We have identified three
kinds of advice. Generic advice is related to overall behaviour,
e.g. don't be rude. This can be matched to personality
variables, which in turn determine the choice of actions in the
HTN. Such advice can be interpreted by altering personality
variables that match the heuristic functions attached to the
candidate actions in the HTN. For instance, a nice Ross will
refrain from a certain number of actions, such as reading
personal diaries or mail, interrupting conversations or expelling
other characters from the set. This of course relies on an a priori
classification of actions in the HTN, which is based on static
heuristic values being attached to nodes of the HTN.
Situational advice is a form of rule that should help the character
avoiding certain situations. One such example is an advice to
avoid making Rachel jealous, such as don't let Rachel see you
with Phoebe. The processing of such advice is more complex
and we have only implemented simplified, procedural versions so
far. One such example in the same situation consists in warning
Ross that Rachel is approaching (Figure 9).

Figure

9. Advice I think Rachel is coming.
Speech input mostly targets the plan-based performance of an
actor's role but can also target other forms of behaviour as
mentioned in section 4, such as situated reasoning or reactive
behaviour. For instance, specific reactive behaviour can be
inhibited by spoken instructions: Rachel can be advised not to be
jealous (Rachel, don't be jealous).
8. CONCLUSIONS
We have described a specific approach to interactive storytelling
where the user, rather than being immersed in the story is
essentially trying to influence it from his spectator position. We
would suggest that this paradigm is worth exploring for future
entertainment applications, where it could bridge the gap
between traditional media and interactive media. The long-term
interest of this approach is however a case for user evaluation,
which should first require the system to reach a critical scale.
Our prototype currently has four autonomous characters, all
based on HTN plans (though the main character Ross has the
most complex plan) and is able to generate short stories (one-act
plays, [18]) up to three minutes in duration, with approximately
one beat [18] per minute. This contrasts with the objective
suggested by Mateas and Stern [18] of 10-15 minute stories with
three characters, which is certainly a valid objective for
interactive storytelling systems. Performance of the planning
component has shown good potential for scaling-up on simulated
tests. The main difficulties are expected to arise from increased
interaction between characters and the associated descriptions of
situated reasoning, for which no clear methodological principles
have been established. On the other hand, there is much to be
learned from running larger-scale tests and these results could
have a generic interest for the study of high-level behaviour of
embodied characters.
9.


--R

Interactive Movies
Toward the Holodeck: Integrating Graphics
Narrative in Virtual Environments - Towards Emergent Narrative

Real Characters in Virtual Stories (Promoting Interactive Story-Creation Activities)
Interactive Storytelling Systems for Children: Using Technology to Explore Language and Identity.
A Framework for Plot Control in Interactive Story Systems
Grabson and Braun
Acting in Character.
"Narrative Intelligence,"
Socially Intelligent Agents: The Human in the Loop.
Creating Interactive Narrative Structures: The Potential for AI Approaches.
An Overview of the Mimesis Architecture: Integrating Narrative Control into a Gaming Environment.

Bringing VR to the Desktop: Are You Game?
Morphology of the Folktale.
Adaptive Narrative: How Autonomous Agents
Towards Integrating Plot and Character for Interactive Drama.

The intentional planning system: Itplans.
Hybrid planning for partially hierarchical domains.
A Validation Structure Based Theory of Plan Modification and Reuse
Control Strategies in HTN Planning: Thoery versus Practice.
Computer Bridge: A Big Win for AI Planning.
Guiding Interactive Drama.
A consequence of incorporating intentions in means-end planning
Emergent Situations in Interactive Storytelling.
Introduction a l'Analyse Structurale des R
Embodied Agents for Multi-party Dialogue in Immersive Virtual Worlds
Dynamically Altering Agent Behaviours Using Natural Language Instructions.
--TR
A validation-structure-based theory of plan modification and reuse
Instructions, intentions and expectations
Hybrid planning for partially hierarchical domains
Control strategies in HTN planning
Interactive movies
Interactive storytelling systems for children
Dynamically altering agent behaviors using natural language instructions
Toward the holodeck
Emergent situations in interactive storytelling
Bringing VR to the Desktop
Acting in Character
Adaptive Narrative
Real Characters in Virtual Stories
Guiding interactive drama

--CTR
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interactive storytelling;computer games;planning;synthetic characters;speech understanding