8803 AGA / 4803 AGA / LCC 8823 MR : Advanced Game AI
All papers are available in T-Square
[Pederson09] Christoffer Pedersen, Julian Togelius and Georgios Yannakakis (2009): Modeling Player Experience in Super Mario Bros. Proceedings ot the IEEE Symposium on Computational Intelligence and Games (CIG).
This paper investigates the relationship between
level design parameters of platform games, individual playing
characteristics and player experience. The investigated design
parameters relate to the placement and sizes of gaps in the level
and the existence of direction changes; components of player
experience include fun, frustration and challenge. A neural
network model that maps between level design parameters,
playing behavior characteristics and player reported emotions
is trained using evolutionary preference learning and data from
480 platform game sessions. Results show that challenge and
frustration can be predicted with a high accuracy (77.77% and
88.66% respectively) via a simple single-neuron model whereas
model accuracy for fun (69.18%) suggests the use of more
complex non-linear approximators for this emotion. The paper
concludes with a discussion on how the obtained models can
be utilized to automatically generate game levels which will
enhance player experience.
[Thue07] David Thue, Vadim Bulitko, Marcia Spetch, and Eric Wasylishen. Interactive Storytelling: A Player Modelling Approach. The Third Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE).
In recent years, the fields of Interactive Storytelling and
Player Modelling have independently enjoyed increased interest
in both academia and the computer games industry. The
combination of these technologies, however, remains largely
unexplored. In this paper, we present PaSSAGE (Player-
Specific Stories via Automatically Generated Events), an interactive
storytelling system that uses player modelling to automatically
learn a model of the player’s preferred style of
play, and then uses that model to dynamically select the content
of an interactive story. Results from a user study evaluating
the entertainment value of adaptive stories created by
our system as well as two fixed, pre-authored stories indicate
that automatically adapting a story based on learned player
preferences can increase the enjoyment of playing a computer
role-playing game for certain types of players.
[Sharma07] Manu Sharma, Manish Mehta, Santiago Ontanon, and Ashwin Ram. (2007). Player Modeling Evaluation for Interactive Fiction. Third Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-07), Workshop on Optimizing Player Satisfaction
A growing research community is working towards employing
drama management components in story-based
games that guide the story towards specific narrative
arcs depending on a particular player’s playing patterns.
Intuitively, player modeling should be a key component
for Drama Manager (DM) based approaches to succeed
with human players. In this paper, we report a
particular implementation of the DM component connected
to an interactive story game, Anchorhead, while
specifically focusing on the player modeling component.
We analyze results from our evaluation study and
show that similarity in the trace of DM decisions in previous
games can be used to predict interestingness of
game events for the current player. Results from our
current analysis indicate that the average time spent in
performing player actions provides a strong distinction
between players with varying degrees of gaming experience,
thereby helping the DM to adapt its strategy based
on this information.
[Togelius08] Julian Togelius and Juergen Schmidhuber (2008): An Experiment in Automatic Game Design.Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG).
This paper presents a first attempt at evolving the
rules for a game. In contrast to almost every other paper that
applies computational intelligence techniques to games, we are
not generating behaviours, strategies or environments for any
particular game; we are starting without a game and generating
the game itself. We explain the rationale for doing this and
survey the theories of entertainment and curiosity that underly
our fitness function, and present the details of a simple proofof-
[A.Smith10] Adam M. Smith, Mark J. Nelson, and Michael Mateas. Ludocore: A Logical Game Engine for Modeling Videogames. IEEE Conference on Computational Intelligence and Games (CIG), 2010
LUDOCORE is a logical “game engine”, linking
game rules as reasoned about by game designers to the formal
logic used by automated reasoning tools in AI. A key challenge
in designing this bridge is engineering a concise, safe, and
flexible representation that is compatible with the semantics of
the games that logical models created with our engine intend
Building on the event calculus, a formalism for reasoning
about state and events over time, and a set of common structures
and idioms used in modeling games, we present a tool that is
capable of generating gameplay traces that illustrate the game’s
dynamic behavior. It supports incremental modeling of player
and non-player entities in the game world, modification of
game rules without extensive non-local changes, and exploratory
temporal and structural queries. In addition, its logical models
can support play as real-time, graphical games with minimal
[Togelius07] Julian Togelius, Renzo De Nardi and Simon M. Lucas (2007): Towards automatic personalised content creation for racing games. Proceedings of IEEE Symposium on Computational Intelligence and Games (CIG).
Evolutionary algorithms are commonly used to
create high-performing strategies or agents for computer games.
In this paper, we instead choose to evolve the racing tracks
in a car racing game. An evolvable track representation is
devised, and a multiobjective evolutionary algorithm maximises
the entertainment value of the track relative to a particular
human player. This requires a way to create accurate models of
players' driving styles, as well as a tentative definition of when
a racing track is fun, both of which are provided. We believe
this approach opens up interesting new research questions and
is potentially applicable to commercial racing games.
[Johnson10] Lawrence Johnson, Georgios N. Yannakakis and Julian Togelius (2010): Cellular automata for real-time generation of infinite cave levels. FDG workshop on Procedural Content Generation.
This paper presents a reliable and effcient approach to pro-
cedurally generating level maps based on the self-organization
capabilities of cellular automata (CA). A simple CA-based
algorithm is evaluated on an innite cave game, generating
playable and well-designed tunnel-based maps. The algo-
rithm has very low computational cost, permitting realtime
content generation, and the proposed map representation
exibility with respect to level design.
[G.Smith10] Gillian Smith, Jim Whitehead, Michael Mateas. Tanagra: A Mixed-Initiative Level Design Tool. In Proceedings of Foundations of Digital Games (FDG 2010). Monterey, California, USA. 19--21 June 2010.
Tanagra is a prototype mixed-initiative design tool for 2D platformer level design, in which a human and computer can work together to produce a level. The human designer can place constraints on a continuously running level generator, in the form of exact geometry placement and manipulation of the level’s pacing. The computer then fills in the rest of the level with geometry that guarantees playability, or informs the designer that there is no level that meets their requirements. This paper presents the design of Tanagra, a discussion of the editing operations it provides to the designer, and an evaluation of the expressivity of its generator.
[Shaker10] Noor Shaker, Georgios N. Yannakakis and Julian Togelius (2010): Towards Automatic Personalized Content Generation for Platform Games. To be presented at AIIDE'10.
In this paper, we show that personalized levels can be automatically
generated for platform games. We build on previous
work, where models were derived that predicted player
experience based on features of level design and on playing
styles. These models are constructed using preference learning,
based on questionnaires administered to players after
playing different levels. The contributions of the current paper
are (1) more accurate models based on a much larger data
set; (2) a mechanism for adapting level design parameters to
given players and playing style; (3) evaluation of this adaptation
mechanism using both algorithmic and human players.
The results indicate that the adaptation mechanism effectively
optimizes level design parameters for particular players.
[Roden04] Roden, T., and Parberry, I. (2004) From Artistry to Automation: A Structured Methodology for Procedural Content Creation. In Proceedings of the 3rd International Conference on Entertainment Computing
Procedural techniques will soon automate many aspects of content creation for computer games. We describe an efficient, deterministic, methodology for procedurally generating 3D game content of arbitrary size and complexity. The technique progressively amplifies simple dynamically generated data structures into complex geometry. We use a procedural pipeline with a minimum set of controls at each stage to facilitate authoring. We show two examples from our research. Our terrain generator can synthesize massive 3D terrains in real-time while our level generator can be used to create indoor environments offline or in real-time.
[Parish01] Yoav I. H. Parish, Pascal Müller. (2001). Procedural modeling of cities. Proceedings of the 28th annual conference on Computer graphics and interactive techniques.
Modeling a city poses a number of problems to computer graphics.
Every urban area has a transportation network that follows
population and environmental influences, and often a superimposed
pattern plan. The buildings appearances follow historical,
aesthetic and statutory rules. To create a virtual city, a roadmap
has to be designed and a large number of buildings need to be
generated. We propose a system using a procedural approach
based on L-systems to model cities. From various image maps
given as input, such as land-water boundaries and population
density, our system generates a system of highways and streets,
divides the land into lots, and creates the appropriate geometry
for the buildings on the respective allotments. For the creation of
a city street map, L-systems have been extended with methods
that allow the consideration of global goals and local constraints
and reduce the complexity of the production rules. An L-system
that generates geometry and a texturing system based on texture
elements and procedural methods compose the buildings.
[Hecker08] Chris Hecker, Bernd Raabe, Ryan W. Enslow, John DeWeese, Jordan Maynard, and Kees van Prooijen (2008). Real-time motion retargeting to highly varied user-created morphologies. International Conference on Computer Graphics and Interactive Techniques.
Character animation in video games—whether manually keyframed
or motion captured—has traditionally relied on codifying
skeletons early in a game’s development, and creating animations
rigidly tied to these fixed skeleton morphologies. This paper introduces
a novel system for animating characters whose morphologies
are unknown at the time the animation is created. Our authoring
tool allows animators to describe motion using familiar
posing and key-framing methods. The system records the data in
a morphology-independent form, preserving both the animation’s
structural relationships and its stylistic information. At runtime,
the generalized data are applied to specific characters to yield pose
goals that are supplied to a robust and efficient inverse kinematics
solver. This system allows us to animate characters with highly
varying skeleton morphologies that did not exist when the animation
was authored, and, indeed, may be radically different than anything
the original animator envisioned.
[Hastings09] Erin J. Hastings, Ratan K. Guha, and Kenneth O. Stanley. (2009). Automatic Content Generation in the Galactic Arms Race Video Game. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES. VOLUME 1. ISSUE 4. DECEMBER 2009.
Simulation and game content includes the levels,
models, textures, items, and other objects encountered and
possessed by players during the game. In most modern video
games and in simulation software, the set of content shipped with
the product is static and unchanging, or at best, randomized
within a narrow set of parameters. However, ideally, if game
content could be constantly and automatically renewed, players
would remain engaged longer. This paper introduces two novel
technologies that take steps toward achieving this ambition: (1) A
new algorithm called content-generating NeuroEvolution of Augmenting
Topologies (cgNEAT) is introduced that automatically
generates graphical and game content while the game is played,
based on the past preferences of the players, and (2) Galactic
Arms Race (GAR), a multiplayer video game, is constructed to
demonstrate automatic content generation in a real online gaming
platform. In GAR, which is available to the public and playable
online, players pilot space ships and fight enemies to acquire
unique particle system weapons that are automatically evolved
by the cgNEAT algorithm. A study of the behavior and results
from over 1,000 registered online players shows that cgNEAT
indeed enables players to discover a wide variety of appealing
content that is not only novel, but also based on and extended
from previous content that they preferred in the past. Thus GAR
is the first demonstration of evolutionary content generation in
an online multiplayer game. The implication is that with cgNEAT
it is now possible to create applications that generate their own
content to satisfy users, potentially reducing the cost of content
creation and increasing entertainment value from single player
to massively multiplayer online games (MMOGs) with a constant
stream of evolving content.
[Hunicke04] Hunicke, R., Chapman, V., AI for Dynamic Difficulty Adjustment in Games. In Proceedings of the Challenges in Game AI Workshop, Nineteenth National Conference on Artificial Intelligence (AAAI '04)
Video Games are boring when they are too easy and
frustrating when they are too hard. While most singleplayer
games allow players to adjust basic difficulty (easy,
medium, hard, insane), their overall level of challenge is
often static in the face of individual player input. This lack
of flexibility can lead to mismatches between player ability
and overall game difficulty.
In this paper, we explore the computational and design
requirements for a dynamic difficulty adjustment system.
We present a probabilistic method (drawn predominantly
from Inventory Theory) for representing and reasoning
about uncertainty in games. We describe the
implementation of these techniques, and discuss how the
resulting system can be applied to create flexible interactive
experiences that adjust on the fly.
[Jennings-Teats10] Martin Jennings-Teats, Gillian Smith, Noah Wardrip-Fruin. Polymorph: Dynamic Difficulty Adjustment through Level Generation. In Proceedings of the Workshop on Procedural Content Generation in Games (Co-located with FDG 2010). Monterey, California, USA. 18 June 2010.
Players begin games at different skill levels and develop their skill
at different rates so that even the best-designed games are
uninterestingly easy for some players and frustratingly difficult for
others. A proposed answer to this challenge is Dynamic Difficulty
Adjustment (DDA), a general category of approaches that alter
games during play, in response to player performance. However,
nearly all these techniques are focused on basic parameter
tweaking, while the difficulty of many games is connected to
aspects that are more challenging to adjust dynamically, such as
level design. Further, most DDA techniques are based on designer
intuition, which may not reflect actual play patterns. Responding
to these challenges, we present Polymorph, which employs
techniques from level generation and machine learning to
understand game component difficulty and player skill,
dynamically constructing a 2D platformer game with continuallyappropriate
challenge. We believe this will create a play
experience that is unique because the changes are both
personalized and structural, while also providing an example of a
promising new research and development approach.
[Beaudry10] Beaudry, É., Bisson, F., Chamberland, S. and Kabanza, F. Using Markov Decision Theory to Provide a Fair Challenge in a Roll-and-Move Board Game. Proc. of the 2010 IEEE Conference on Computational Intelligence and Games, 2010.
Board games are often taken as examples to teach
decision-making algorithms in artificial intelligence (AI). These
algorithms are generally presented with a strong focus on
winning the game. Unfortunately, a few important aspects, such
as the gaming experience of human players, are often missing
from the equation. This paper presents a simple board game
we use in an introductory course in AI to initiate students to
the gaming experience issue. The Snakes and Ladders game
has been modified to provide different levels of challenges for
students. The game with such modifications offers theoretical,
algorithmic and programming challenges. One of the most
complex is the generation of an optimal policy to provide a
fair challenge to an opponent. A solution based on Markov
Decision Processes (MDPs) is presented. This approach relies
on a simple model of the opponent’s playing behaviour.
[Agapitos08] Alexandros Agapitos, Julian Togelius, Simon M. Lucas, Juergen Schmidhuber and Andreas Konstantinidis (2008): Generating Diverse Opponents with Multiobjective Evolution. Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG).
For computational intelligence to be useful in
creating game agent AI, we need to focus on creating interesting
and believable agents rather than just learn to play the games
well. To this end, we propose a way to use multiobjective
evolutionary algorithms to automatically create populations
of Non-Player Characters (NPCs), such as opponents and
collaborators, that are interestingly diverse in behaviour space.
Experiments are presented where a number of partially conflicting
objectives are defined for racing game competitors, and
multiobjective evolution of GP-based controllers yield pareto
fronts of interesting controllers.
[Dimovska10] Dajana Dimovska, Patrick Jarnfelt, Sebbe Selvig, and Georgios N. Yannakakis. (2010). Towards procedural level generation for rehabilitation. Proceedings of the FDG 2010 Workshop on Procedural Content Generation in Games.
This paper introduces the concept of procedural content gen-
eration for physical rehabilitation. In this initial study a
ski-slalom game is developed on the Wii platform that pro-
cedurally places the gates of the game according to player
performance. A preliminary game evaluation study is con-
ducted on patients with injured legs and showcases the effi-
ciency of the procedural gate generation mechanism tailoring
the game difficulty to match rehabilitation goals. The study
also validates certain usability aspects of the patients.
[Hullett09] Kenneth Hullett and Michael Mateas. Scenario Generation for Emergency Rescue Training Games. In Proceedings of the Fourth International Conference on the Foundations of Digital Games (FDG 2009).
This paper presents a reliable and efficient approach to pro-
cedurally generating level maps based on the self-organization
capabilities of cellular automata (CA). A simple CA-based
algorithm is evaluated on an innite cave game, generating
playable and well-designed tunnel-based maps. The algo-
rithm has very low computational cost, permitting realtime
content generation, and the proposed map representation
exibility with respect to level design.
[Ashmore07] Calvin Ashmore and Michael Nitsche. 2007. The Quest in a Generated World. Proceedings of DiGRA 2007 Conference.
As procedural content becomes a more appealing option for
game development, procedurally determined context is
necessary to structure and make sense of this content. We
find that a useful means to structure content in 3D games is
the quest. The task of generating necessary context then
becomes one of quest generation. This paper describes how
we implemented a basic quest generator based on key and
lock puzzles into a procedural game world. It uses notion of
quest as spatial progression and discusses the design of the
game world and how our quest generator connects to it. Its
findings are twofold: on the technical level we managed to
implement a highly flexible content and context generator
into an existing game engine; one the content level we can
trace signs for higher player interest in quest-enhanced
procedural game worlds in comparison to unstructured
[Dormans10] Joris Dormans. (2010). Adventures in level design: generating missions and spaces for action adventure games. Proceedings of the FDG 2010 Workshop on Procedural Content Generation in Games.
This paper investigates strategies to generate levels for action adventure games. This genre relies more strongly on well-designed levels than rule-driven genres such as strategy or roleplaying games for which procedural level generation has been successful in the past. The approach outlined by this paper distinguishes between missions and spaces as two separate structures that need to be generated in two individual steps. It discusses the merits of different types of generative grammars for each individual step in the process.
[Li10] Boyang Li and Mark O. Riedl. An Offline Planning Approach to Game Plotline Adaptation. Proceedings of the 6th Conference on Artificial Intelligence for Interactive Digital Entertainment, Palo Alto, California, 2010.
Role-playing games, and other types of contemporary video
games, usually contain a main storyline consisting of several
causally related quests. As players have different motivations,
tastes and preferences, it can be beneficial to customize game
plotlines. In this paper, we present an offline algorithm for
adapting human-authored game plotlines for computer roleplaying
games to suit the unique needs of individual players,
thereby customizing gaming experiences and enhancing replayability.
Our approach uses an plan refinement technique
based on partial-order planning to (a) optimize the global
structure of the plotline according to input from a player model,
(b) maintain plotline coherence, and (c) facilitate authorial intent
by preserving as much of the original plotline as possible. A
theoretical analysis of the authorial leverage and a user study
suggest the benefits of this approach.
[Riedl04] Mark O. Riedl and R. Michael Young. (2004) An Intent-Driven Planner for Multi-Agent Story Generation. Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems
The ability to generate narrative is of importance to computer systems that wish to use story effectively for entertainment, training, or education. We identify two properties of story – plot coherence and character believability – which play a role in the success of a story. Plot coherence is the perception by audience members that character actions have relevance to the outcome of the story. Character believability is the perception that character actions are motivated by agents' internal beliefs and desires. Unlike conventional planning in which plan goals represent an agent's intended world state, multiagent story planning involves goals that represent the outcome of a story. In order for the plans' actions to appear believable, multi-agent story planners must determine not only how agents' actions achieve a story's goal state, but must also ensure that each agent appears to be acting intentionally. We present a narrative generation planning system for multi-agent stories that is capable of generating narratives with both strong plot coherence and strong character believability. The planning algorithm uses causal reasoning and a simulated intention recognition process to drive plan creation.
[Diaz-Agudo04] B Díaz-Agudo, P Gervás, F Peinado. (2004). A Case Based Reasoning Approach to Story Plot Generation. In Proc. of the 7 th European Conf. on Case Based Reasoning.
Automatic construction of story plots has always been a longed-for utopian dream in the entertainment industry, especially in the more commercial genres that are fueled by a large number of story plots with only a medium threshold on plot quality, such as TV series or video games. We propose a Knowledge Intensive CBR (KI-CBR) approach to the probem of generating story plots from a case base of existing stories analyzed in terms of Propp functions. A CBR process is defined to generate plots from a user gquery specifying an initial setting for the story, using an ontology to measure the semantical distance between words and structures taking part in the texts.
[Lebowitz84] Lebowitz, M. (1984). Creating characters in a story-telling universe. Poetics, 13, 171-194.
Extended story generation, i.e., the creation of continuing serials, presents difficult and interesting problems for Artificial Intelligence. We present here the first phase of the development of a program, UNIVERSE, that will ultimately tell extended stories. In particular, after descri inb our overall model of story telling, we present a method for creating universes of characters appropriate for extended story generation. This method concentrates on the need to keep story-telling unverses consistent and coherent. We also describe the information that must be maintained for characters and interpersonal relationships, and the use of stereotypical information about people to help motivate trait values. The use of historical events for motivation is also described. Finally, we present an example of a character generated by UNIVERSE.
[Lebowitz85] Lebowitz, M. (1985). Story-telling as planning and learning. Poetics, 14, 483-502.
The generation of extended plots for melodramatic fiction is an interesting task for Artificial Intelligece research, one that requires the application of genralization techniques to carry out fully. UNIVERSE is a story-telling program that uses plan-like units, 'plot fragments', to generate plot outlines. By using a rich library of plot fragments and a well-developed set of characters. UNIVERSE can create a wide range of plot outlines. In this paper we illustrate how UNIVERSE's plot gramgent library is used to create plot outlines and how it might be automatically extended using explanation-based generalization methods. Our methods are based on analysis of a television melodrama, including comparisons of similar stories.
[Meehan81] Meehan, J. (1981). Tale-Spin. In R.C. Schank and C.K. Riesbeck (Eds). Inside Computer Understanding (pp. 197-226). Lawrence Erlbaum Associates.
TALE-SPIN is a program that writes simple stories. It is easily distinguished from any of the "mechanical" devices one can use for writing stories, such as filling in slots in a canned frame. The goal behind the writing of TALE-SPIN was to find out what kinds of knowledge were needed in story generation. the writing of TALE-SPIN embodied the traditional AI cycle of research. Step 1 was to define a theory. Step 2 was to write a program modeling that theory and to add it to the existing system. Step 3 was to run the system and to observe where the model was incorrect or inadequate, thereby identifying the need for some more theory.
[Khosmood10] Foaad Khosmood, Marilyn Walker. (2010). Grapevine: a gossip generation system. Proceedings of the Fifth International Conference on the Foundations of Digital Games.
Generating believable and contextual dialogue among non-player-characters (NPC) remains one of the major challenges in interactive entertainment. Dialogue scenes in virtual environments are crucial to narrative progression and user believability, yet they continue to demand heavy authorial burden. In this paper, we describe our project Grapevine, a system for generating gossip-style conversation. We model the gossip conversation with a series of speech-acts controlled by a dialogue manager. We model characters with traits derived from the Big Five theory of personality. Grapevine also maintains an independent belief matrix, allowing for modeling of phenomena such as dishonesty, misunderstanding and bias. The dialogue manager decisions are a function of both narrative progression and personality traits. Surface text realization is achieved using RealPro, an off-the-shelf realizer (Lavoie & Rambow, 1997) and stylistically enhanced with the PERSONAGE generator (Mairesse & Walker, 2007). We demonstrate the current performance of the system with sample output of a three character series of gossip dialogues and discuss results of our 50 person validation survey.
[Nitsche06] M. Nitsche, C. Ashmore, W. Hankinson, R. Fitzpatrick, J. Kelly, and K. Margenau. Designing Procedural Game Spaces: A Case Study. Proceedings of FuturePlay, 2006.
Procedural content generation holds many promises
for the design, art, and production of video games. It
also poses a number of challenges. This paper
concentrates on the procedural generation of game
spaces. We specifically argue for a connection of a
player’s agency with the procedural world
generation. First, space generation in games is
broken down into four main approaches: designercreated,
random, player-created, and procedural
spaces. Then, the paper introduces our experimental
game prototype Charbitat that merges these four
stages and provides a practical case study. Charbitat
generates game worlds based on the gaming style of
its players, who create the world as they play it. We
describe how the project met the challenges in
design and implementation. Finally, we point out
new questions opened up by the project and relevant
for procedural content generation.
[Magerko06] Magerko, B., Stensrud, B., and Holt, L. Bringing the Schoolhouse Inside the Box – A Tool for Engaging, Individualized Training. 25th Army Science Conference, 2006. Orlando, FL.
The Interactive Storytelling Architecture for Training (ISAT) is designed to address the limitations of computer games for advanced distributed learning (ADL) and to fully realize the potential of games to become engaging and individualized training environments. The central component of the ISAT architecture is an intelligent director agent responsible for individualizing the training experience. To achieve this, the director tracks the trainee’s demonstration of knowledge and skills during the training experience. Using that information, the director plays a role similar to that of a schoolhouse trainer, customizing training scenarios to meet individual trainee needs. The director can react to trainee actions within a scenario, dynamically adapting the environment to the learning needs of trainee as well as the dramatic needs of the scene. This paper describes a prototype implementation of the ISAT architecture in the combat medic training domain, with an emphasis on the design of the director agent.
[Pita07] Pita,, J., Magerko, B. and Brodie, S. TRUE STORY: Dynamically Generated, Contextually Linked Quests in Persistent Systems. FuturePlay, Toronto, ON, 2007.
Massively Multiplayer Online Role-Playing Games (MMORPGs)
typically use a handful of static conventions for involving players
in stories, such as predefined quest or story paths (a quest or story
path is one in which the user experiences a sequence of related
quests that must be accomplished in a particular order). Beyond
the work done in MMORPGs there has been strong research in
designing adaptive approaches to interactive fiction/drama that
dynamically author content for users of the interactions  .
The system architecture presented in this paper, TRUE STORY, is
designed to address issues concerning dynamically generated
quest or story paths in persistent worlds, such as MMORPGs, for
users to engage in more enhanced, interactive and personal
experiences. TRUE STORY empowers persistent world designers
by offering a truly modular approach for dynamically generating
and presenting compelling content that results in user experiences
worth telling a story about. The current implementation is set in a
game model to demonstrate a dynamic quest generation system
built to present users with unique and compelling experiences
linked by context to past quests and/or experiences. This is
achieved by utilizing history and relationships developed through
interaction between world objects and actions.
[Doran10] Jon Doran and Ian Parberry, "Controlled Procedural Terrain Generation Using Software Agents", IEEE Transactions on Computational Intelligence and AI in Games, Vol. 2, No. 2, pp. 111-119, June 2010.
Procedural terrain generation is used to create landforms for applications such as computer
games and flight simulators. While most of the existing work has concentrated on algorithms
that generate terrain without input from the user, we explore a more controllable system that
uses intelligent agents to generate terrain elevation heightmaps according to designer-dened
constraints. This allows the designer to create procedural terrain that has specic properties.
[Charles05] Darryl Charles, Michael McNeill, Moira McAlister, Michaela Black, Adrian Moore, Karl Stringer, Julian Kücklich, Aphra Kerr. (2005). Player-Centred Game Design: Player Modelling and Adaptive Digital Games. Proceedings of DiGRA 2005 Conference.
We describe an approach to player-centred game design through adaptive game technologies . The work presented is the result of on-going collaborative research between Media and Computing groups at the University of Ulster, and so we begin with a review of related literature from both areas before presenting our new ideas. In particular we focus on three areas of related research: understanding players, modelling players, and adaptive game technology. We argue that player modelling and adaptive technologies may be used alongside existing approaches to facilitate improved player-centred game design in order to provide a more appropriate level of challenge, smooth the learning curve, and enhance the gameplay experience for individual players regardless of gender, age and experience. However, adaptive game behaviour is a controversial topic within game research and development and so while we outline the potential of such technologies, we also address the most significant concerns.
[Strong08] Christina Strong and Michael Mateas. (2008). Talking with NPCs: Towards Dynamic Generation of Discourse Structures. Proceedings of the 2008Conference on Artificial Intelligence for Interactive Digital Entertainment.
Dialogue in commercial games is largely created by
teams of writers and designers who hand-author every
line of dialogue and hand-specify the dialogue structure
using finite state machines or branching trees. For
dialogue heavy games, such as role playing games
with significant NPC interactions, or emerging genres
such as interactive drama, such hand specification significantly
limits the player’s interaction possibilities.
Decades of research on the standard pipeline architecture
in natural language generation has focused on how
to generate text given a specification of the communicative
goals; one can imagine beginning to adapt such
methods for generating the lines of dialogue for characters.
But little work has been done on the problem
of procedurally generating dialogue structures, that is,
dynamically generating dialogue FSMs or trees (more
generally, discourse managers) that accomplish communicative
goals. In this paper we describe a system
that uses a formalization of backstory, character information,
and social interactions to dynamically generate
interactive dialogue structures that accomplish desired
[Porteous09] Porteous, J. and Cavazza, M., 2009. Controlling Narrative Generation with Planning Trajectories: the Role of Constraints. International Conference on Interactive Digital Storytelling.
AI planning has featured in a number of Interactive Story-
telling prototypes: since narratives can be naturally modelled as a se-
quence of actions it has been possible to exploit state of the art plan-
ners in the task of narrative generation. However the characteristics of
a \good" plan, such as optimality, aren't necessarily the same as those
of a \good" narrative, where errors and convoluted sequences may oer
more reader interest, so some narrative structuring is required. In our
work we have looked at injecting narrative control into plan generation
through the use of PDDL3.0 state trajectory constraints which enable
us to express narrative control information within the planning repre-
sentation. As part of this we have developed an approach to planning
with such trajectory constraints. The approach decomposes the problem
into a set of smaller subproblems using the temporal orderings described
by the constraints and then solves these subproblems incrementally. In
this paper we outline our method and present results that illustrate the
potential of the approach.
[Swanson08] Reid Swanson and Andrew S. Gordon. Say anything: A massively collaborative open domain story writing companion. In First International Conference on Interactive Digital Storytelling, Erfurt, Germany, November 2008.
Interactive storytelling is an interesting cross-disciplinary area that
has importance in research as well as entertainment. In this paper we explore a
new area of interactive storytelling that blurs the line between traditional
interactive fiction and collaborative writing. We present a system where the
user and computer take turns in writing sentences of a fictional narrative.
Sentences contributed by the computer are selected from a collection of
millions of stories extracted from Internet weblogs. By leveraging the large
amounts of personal narrative content available on the web, we show that even
with a simple approach our system can produce compelling stories with our
[Mairesse08] François Mairesse and Marilyn Walker. A Personality-based Framework for Utterance Generation in Dialogue Applications. In Proceedings of the AAAI Spring Symposium on Emotion, Personality, and Social Behavior, Palo Alto, March 2008.
Conversation is an essential component of social behaviour,
one of the primary means by which humans express emotions,
moods, attitudes and personality. Thus a key technical
capability for dialogue applications, such as interactive narrative
systems (INS), human robot interaction (HRI) and spoken
dialogue systems (SDS), is the ability to support natural
conversational interaction. However, system utterances in existing
systems are typically handcrafted, leading to problems
of portability and scalability. We propose a framework for automatically
generating language projecting different personality
traits based on the ‘Big Five’ model of personality. We
show that our PERSONAGE generator can produce utterances
with recognisable personality for all Big Five traits, according
to human judges. We also test the ability of PERSONAGE
to vary the characters’ personality in an existing interactive
narrative system, showing that some forms of variation can
be automatically obtained in a new domain, depending on the
level of utterance representation.
[Moore94] J. Moore, C. Paris. 1994. Planning text for advisory dialogues: Capturing intentional, rhetorical and attentional information. Computational Linguistics, 19(4).
To participate in a dialogue a system must be capable of reasoning about its own previous utterances.
Follow-up questions must be interpreted in the context of the ongoing conversation, and
the system's previous contributions form part of this context. Furthermore, if a system is to be able
to clarify misunderstood explanations or to elaborate on prior explanations, it must understand
what it has conveyed in prior explanations. Previous approaches to generating multisentential
texts have relied solely on rhetorical structuring techniques. In this paper, we argue that, to
handle explanation dialogues successfully, a discourse model must include information about the
intended effect of individual parts of the text on the hearer, as well as how the parts relate to one
another rhetorically. We present a text planner that records this information and show how the
resulting structure is used to respond appropriately to a follow-up question.
[Nelson05] Nelson, M. and Mateas, M. (2005). Search-based drama management in the interactive fiction Anchorhead . Proceedings of the First Conference on Artificial Intelligence and Interactive Digital Entertainment.
Drama managers guide a user through a story experience by modifying the experience in reaction to the user's actions. Search-based drama management (SBDM) casts the dramamanagement problem as a search problem: Given a set of plot points, a set of actions the drama manager can take, and an evaluation of story quality, search can be used to optimize the user's experience. SBDM was first investigated by Peter Weyhrauch in 1997, but little explored since. We return to SBDM to investigate algorithmic and authorship issues, including the extension of SBDM to different kinds of stories, especially stories with subplots and multiple endings, and issues of scalability. In this paper we report on experiments applying SBDM to an abstract story search space based on the text-based interactive fiction Anchorhead. We describe the features employed by the story evaluation function, investigate design issues in the selection of a set of drama management actions, and report results for drama managed versus unmanaged stories for a simulated random user.
[Mateas02] Mateas, M. and Stern, A (2002). Architecture, authorial idioms and early observations of the interactive drama Facade. Technical report CMU-CS-02-198, School of Computer Science, Carnegie Mellon University.
Facade is an artificial intelligence-based art/research experiment in electronic narrative - an attempt to move beyond traditional branching or hyper-linked narrative to create a fully-realized, one-act interactive drama. Integrating an interdisciplinary set of artistic practices and artificial intelligence technologies, we are completing a three year collaboration to engineer a novel architecture for supporting emotional, interactive character behavior and drama-managed plot. Within this architecture we are building a dramatically interesting, real-time 3D virtual world inhabited by computer-controlled characters, in which the user experiences a story from a first-person perspective. Facade will be publicly released as a free download in 2003.
[Cavazza02] Cavazza, M., Charles, F., and Mead, S. (2002). Planning Characters' Behaviour in Interactive Storytelling. Journal of Visualization and Computer Animation, 13, 121-131.
In this paper, we describe a method for implementing the behaviour of artificial actors in the context of interactive storytelling. We have developed a fully implemented prototype based on the Unreal Tournament game engine, and carried experiments with a simple sitcom-like scenario. We discuss the central role of artificial actors in interactive storytelling and how real-time generation of their behaviour participates in the creation of a dynamic storyline. We follow previous work describing the behaviour of artificial actors through AI planning formalisms, and adapt it to the context of narrative representation. In this context, the narrative equivalent of a character's behaviour consists in its role. The set of possible roles for a given actor is represented as a Hierarchical Task Network (HTN). The system uses HTN planning to dynamically generate the character roles, by interleaving planning and execution, which supports dynamic interaction between actors, as well as user intervention in the unfolding plot. Finally, we present several examples of short plots and situations generated by the system from the dynamic interaction of artificial actors.
[Perez01] Perez y Perez, R. and Sharples, M. (2001). MEXICA: a computer model of a cognitive account of creative writing. Journal of Experimental and Theoretical Artificial Intelligence, 13, 119-139.
MEXICA is a computer model that produces frameworks for short stories based on the engagement-refelction cognitive account of writing. During engagement MEXICA generates material guided by content and rhetorical constraints, avoiding the use of explicit goals or story-structure information. During reflection the system breaks impasses, evaluates the novelty and interestingness of the story in progress and verifies that coherence requirements are satisfied. In this way, MEXICA complements and extends those models of computerised story-telling based on traditional problem-solving techniques where explicit goals drive the generation of stories. This paper describes the engagement-reflection account of writing, the general characteristics of MEXICA and reports an evaluation of the program.
[Leuski10] Anton Leuski and David Traum. 2010. NPCEditor: A Tool for Building Question-Answering Characters. Proceedings of the Seventh conference on International Language Resources and Evaluation.
NPCEditor is a system for building and deploying virtual characters capable of engaging a user in spoken dialog on a limited domain. The dialogue may take any form as long as the character responses can be specified a priori. For example, NPCEditor has been used for constructing question answering characters where a user asks questions and the character responds, but other scenarios are possible. At the core of the system is a state of the art statistical language classification technology for mapping from user's text input to system responses. NPCEditor combines the classifier with a database that stores the character information and relevant language data, a server that allows the character designer to deploy the completed characters, and a user-friendly editor that helps the designer to accomplish both character design and deployment tasks. In the paper we define the overall system architecture, describe individual NPCEditor components, and guide the reader through the steps of building a virtual character.
[Magerko04] Magerko, B. and Laird, J.E. Mediating the Tension Between Plot and Interaction. AAAI Workshop Series: Challenges in Game Artificial Intelligence, 2004.
When building a story-intensive game, there is always the
question of how much freedom to give the player. Give the
player too little, and he may feel constrained and
disconnected from the character he is controlling. Give him
too much freedom, and the progression of the story may lag
or stop altogether. This paper focuses on our attempt to
find a balance between offering the player a high degree of
interaction and providing a story-based experience where the
player is a key character. Our approach is embedded in our
Interactive Drama Architecture (IDA), which includes an
omniscient story director agent who manages the player’s
narrative experience. The director agent uses a declarative
description of the plot to track the player’s progress, detect
deviations from the plot, and make directions to supporting
characters in the game. Our director is embedded within a
game we have developed, called Haunt 2, which is an
extension to the Unreal Tournament engine.
[Riedl08] Mark O. Riedl, Andrew Stern, Don Dini, and Jason Alderman. Dynamic Experience Management in Virtual Worlds for Entertainment, Education, and Training. International Transactions on Systems Science and Applications, Special Issue on Agent Based Systems for Human Learning, vol. 4(2), 2008.
Modern computer systems have the ability to make
the storytelling experience interactive by involving a
participant or learner as a character in the narrative itself. We
present a framework for creating interactive narratives for
entertainment, educational, and training purposes based on a
type of agent called an experience manager. An experience
manager (a generalization of a drama manager) is an
intelligent computer agent that manipulates a virtual world to
coerce a participant’s experience to conform to a set of
provided properties. Our realization of the experience
manager automatically generates narrative content in order to
adapt to the user’s actions in the virtual world. The
experience management framework has been used to develop
an interactive version of Little Red Riding Hood and an
interactive practice environment called IN-TALE for
educating and training cognitive skills such as situation
awareness, cultural awareness, leadership, and decisionmaking.
[Rowe08] Mark O. Riedl, Jonathan P. Rowe, and David K. Elson. Toward Intelligent Support of Authoring Machinima Media Content: Story and Visualization. Proceedings of the 2nd International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN), Playa del Carmen, Cancun Mexico, 2008.
The Internet and the availability of authoring tools have enabled a
greater community of media content creators, including nonexperts.
However, while media authoring tools often make it
technically feasible to generate, edit and share digital media
artifacts, they do not guarantee that the works will be valuable or
meaningful to the community at large. Therefore intelligent tools
that support the authoring and creative processes are especially
valuable. In this paper, we describe two intelligent support tools
for the authoring and production of machinima. Machinima is a
technique for producing computer-animated movies through the
manipulation of computer game technologies. The first system
we describe, ReQUEST, is an intelligent support tool for the
authoring of plots. The second system, Cambot, produces
machinima from a pre-authored script by manipulating virtual
avatars and a virtual camera in a 3D graphical environment.
[Gonalzo08] Gonzalo Florez-Puga, Marco Gomez-Martın, Belen Dıaz-Agudo and Pedro A. Gonzalez-Calero. Dynamic Expansion of Behaviour Trees. Proceedings of AIIDE 2008.
Artificial intelligence in games is typically used for creating
player’s opponents. Manual edition of intelligent behaviors
for Non-Player Characters (NPCs) of games is a cumbersome
task that needs experienced designers. Our research aims to
assist designers in this task. Behaviours typically use recurring
patterns, so that experience and reuse are crucial aspects
for behavior design. The use of hierarchical state machines
allows working on different abstraction levels, sharing transitions
and reusing pieces from the more detailed levels. However,
the static nature of the design process does not release
the designer from the burden to completely specify each behaviour.
Our approach applies Case-Based Reasoning (CBR)
techniques to retrieve and reuse stored behaviors represented
as hierarchical state machines (actually, behaviour trees). In
this paper we focus on dynamic retrieval of behaviours taking
into account the world state and the underlying goals to
select the most appropriate state machine to guide the NPC
behaviour. The global behaviour of the NPC is dynamically
built in run time querying the CBR system. We exemplify our
approach through a serious game, developed by our research
group, with gameplay elements from First-Person Shooter
[McNaughton04] McNaughton et al. Script Ease: Generative Design Patters for Computer Role-Playing Games, Proceedings of ASE 2004.
Recently, some researchers have argued that
generative design patterns (GDPs) can leverage the
obvious design re-use that characterizes traditional
design patterns into code re-use. This paper provides
additional evidence that GDPs are both useful and
productive. Specifically, the current state-of-the-art in
the domain of computer games is to script individual
game objects to provide the desired interactions for
each game adventure. We use BioWare Corp.’s
popular Neverwinter Nights computer role-playing
game to show how GDPs can be used to generate
game scripts. This is a particularly good domain for
GDPs, since game designers often have little or no
programming skills. We demonstrate our approach
using a new GDP tool called ScriptEase.