8803 AGA / 4803 AGA / LCC 8823 MR : Advanced Game AI

Readings

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- concept experiment.

[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 to represent. 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 user-interface description.

[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 innite 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 provides suffcient 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 innite 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 provides sufficient 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 spaces.

[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 [10] [18]. 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-dened constraints. This allows the designer to create procedural terrain that has specic 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 [9]. 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 dialogue goals.

[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 users.

[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 (FPS) games.

[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.