bluuebunny/crossref_metadata_2025 huggingface dataset:
Recombinable Game Mechanics for Automated Design Support
Authors: Nelson Mark , Mateas Michael
Published: October 2022

Systems that provide automated game-design support — whether fully automated game generators, or tools to assist human designers — must be able to maintain a representation of a game design and add or remove game mechanics to support incrementally modifying the game. The system should then be able to reason about the design to support the designer. For example, it might point out that the set of mechanics makes the game unwinnable; or that there's only one complex possible way to beat the game; or that some room is impossible to get to. In addition, the same representation should be actually playable as a game. Existing game representations encode a fairly narrow range of games, most commonly symmetric board games; these representations are also difficult to extend or revise. We propose an architecture based on the event calculus, a logical representation designed for reasoning about time in an elaboration-tolerant way, meaning that designs can be changed by adding or removing sets of axioms rather than modifying brittle hard-coded representations. The resulting game design is a declarative specification in formal logic, so can be critiqued by making queries that are answered through logical inference. Since it specifies the game's simulation rules, it may be executed by logical inference as well; if symbols specifying input and graphical representation are appropriately mapped to input devices and a graphical display, the same declarative representation can be executed as a fully interactive, graphical game. We describe how to organize a library of game-design mechanics using this event-calculus framework, and describe a simple tile-based game, showing how it can be easily modified, critiqued and debugged, and played.
Intelligent Trading Agents for Massively Multi-Player Game Economies
Authors: Reeder John , Sukthankar G. , Georgiopoulos M. , Anagnostopoulos G.
Published: October 2022

As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increasingly complex problem. Human players in many games must achieve their objectives through financial skills such as trading and supply chain management as well as through combat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets. Using historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, selling, and supply chain management. We demonstrate that using reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive advantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participating in virtual economies on an equal footing with humans.
Talking with NPCs: Towards Dynamic Generation of Discourse Structures
Authors: Strong Christina , Mateas Michael
Published: October 2022

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.
TAP: An Effective Personality Representation for Inter-Agent Adaptation in Games
Authors: Tan Chek , Ho-lun Cheng Cheng
Published: October 2022

Tactical Agent Personality (TAP) is a modeling concept to capture tactical patterns in game agents, based on a personality concept introduced by Tan and Cheng (2007), to allow behavior adaptation to different play styles. We introduced a weighted sequential topology to the actions set to capture tactical preferences rather than individual action preferences. This produces a personality representation of much higher expressive power that improves the adaptation performance and subsequently enables a larger variety of action genres to be adaptable. A TAP-based learning framework is then constructed and it is shown to perform better than the one based on the previous agent personality. Consequently, we also implement an RPG scenario that demonstrates its ability to generate adaptive plausible behavior in a much larger variety of action genres.
Explicit Knowledge Programming for Computer Games
Authors: Witzel Andreas , Zvesper Jonathan , Kennerly Ethan
Published: October 2022

The main aim of this paper is to raise awareness of higher-order knowledge (knowledge about someone else's knowledge) as an issue for computer game AI. We argue that a number of existing game genres, especially those involving social interaction, are natural fields of application for an approach we call explicit knowledge programming. We motivate the use of this approach, and describe a simple implementation based upon it. A survey of recent literature and computer games illustrates its novelty.
Otello: A Next-Generation Reputation System For Humans and NPCs
Authors: Sellers Michael
Published: October 2022

This paper introduces Online Alchemy’s Otello technology as a way to enable reputational capabilities beyond any found in games or other online social contexts today. This technology allows participants to quickly and easily assess another’s reputation in ways meaningful to them, and enables individuals -- both players and non-player characters (NPCs) -- to contribute to an individual’s reputation in unique and novel ways. Otello also enables new forms of 'relational gameplay' that feature social management, effectively an extension of resource management into the social realm. The player’s actions and opinions affect others, including how they see the player, and how ideas and opinions propagate through a population.
Automatically-Generated Convex Region Decomposition for Real-Time Spatial Agent Navigation in Virtual Worlds
Authors: Hale D. , Youngblood G. , Dixit Priyesh
Published: October 2022

This paper presents a new method for decomposing environments of complex geometry into a navigation mesh represented by bounding geometry and a connectivity graph for real-time agent usage in virtual worlds. This is accomplished by the generation of a well-defined and high-coverage set of convex navigable regions and the connected gateways between them. The focus of this paper is a new automated algorithm developed for decomposing a 2D representation of world-space into arbitrary sided high-order polygons. The DEACCON (Decomposition of Environments for the Creation of Convex-region Navigation-meshes) algorithm works by seeding a 2D polygonal representation of world-space with a series of quads. Each quad is then provided with the opportunity to grow to its maximum extent before encountering an obstruction. DEACCON implements an automatic subdividing system to convert quads into higher-order polygons while still maintaining the convex property. This allows for the generation of navigation meshes with high degrees of coverage while still allowing the use of large navigation regions, providing for easier agent navigation in virtual worlds. Compared to the Space-filling Volumes and Hertel-Mehlhorn navigation mesh decomposition methods, DEACCON provides more complete coverage, controllable mesh sizes, and better overall algorithmic control to desired decomposition quality with an improvement in agent navigation speed due to better decompositions.
Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games
Authors: Hefny Ahmed , Hatem Ayat , Shalaby Mahmoud , Atiya Amir
Published: October 2022

Applying machine learning techniques to real-time computer games is an active research field. In this paper we present Cerberus, a machine learning framework for team-based Capture The Flag (CTF) games. This framework utilizes reinforcement learning to select high-level actions that achieve best team behaviour and utilizes neural networks to control fighting behaviour of team individuals. Our proposed framework also combines waypoints and influence maps for effective path planning
Direction Maps for Cooperative Pathfinding
Authors: M. Renee Jansen M. Renee Jansen , Sturtevant Nathan
Published: October 2022

Cooperative behavior is a desired trait in many fields from computer games to robotics. Yet, achieving cooperative behavior is often difficult, as maintaining shared information about the dynamics of agents in the world can be complex. We focus on the specific task of cooperative pathfinding and introduce a new approach based on the idea of "direction maps" that learns about the movement of agents in the world. This learned data then is used to produce implicit cooperation between agents. This approach is less expensive and has better performance than several existing cooperative algorithms.
Agent Architecture in Social Games — The Implementation of Subsumption Architecture in Diplomacy
Authors: Krzywinski Aleksander , Chen Weiqin , Helgesen Arne
Published: October 2022

Social games are challenging for AI research because they involve not only the mechanism of game (taking actions, planning, etc), but also the social aspects including communicating, cooperating and reaching agreements. Some social games also require cunning, duplicity, or bad faith. In this paper we present the implementation of a prototype that utilizes the properties of the subsumption architecture to provide an interesting computer opponent in a social board game — Diplomacy. The evaluation result indicates that the subsumption architecture is appropriate for social games.
kNN LRTA*: Simple Subgoaling for Real-Time Search
Authors: Bulitko Vadim , Björnsson Yngvi
Published: October 2022

Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by revisiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempts to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle of applying state-of-the-art real-time search methods in video games. In the domain of pathfinding on eight video game maps, the new algorithm used approximately nine times less memory than D LRTA* while finding solutions 9% worse.
An Architecture for Game Behavior AI: Behavior Multi-Queues
Authors: Cutumisu Maria , Szafron Duane
Published: October 2022

We describe an AI behavior architecture that supports responsive collaborative interruptible and resumable behaviors using behavior queues. This architecture wraps sets of behaviors into roles, which provide a simple efficient mechanism for encapsulating behaviors into components that can change dynamically, based on environmental criteria. To illustrate the viability of this architecture in a commercial setting, we implemented this architecture in BioWare Corp.’s Neverwinter Nights game. To demonstrate the usability of this architecture by game designers, we implemented a simple interface in ScriptEase, so that the architecture can be utilized with no coding skills.
Automatically Choosing Appropriate Gestures for Jokes
Authors: Hasegawa Dai , Sjobergh Jonas , Rzepka Rafal , Araki Kenji
Published: October 2022

We examine the problem of automatically selecting gestures that are appropriate to use when telling a joke or a short story. Our current application of this is a joke telling humanoid robot that needs to be able to select natural gestures for arbitrary input. The topic is important because humans use body language and gestures, thus socially interactive robots should also be able to do so for more natural interaction. We asked evaluators to assign appropriate gestures from a set of gestures the robot can perform to 50 jokes from a corpus of jokes in Japanese. We then evaluated different methods for automatically selecting gestures on this data set. While human inter-agreement was rather low, indicating that this is a fairly difficult task and that some jokes have no obviously fitting gesture, the best method performs on par with humans and clearly outperforms the baseline.
Case-Based Reasoning for Build Order in Real-Time Strategy Games
Authors: Weber Ben , Mateas Michael
Published: October 2022

We present a case-based reasoning technique for selecting build orders in a real-time strategy game. The case retrieval process generalizes features of the game state and selects cases using domain-specific recall methods, which perform exact matching on a subset of the case features. We demonstrate the performance of the technique by implementing it as a component of the integrated agent framework of McCoy and Mateas. Our results demonstrate that the technique outperforms nearest-neighbor retrieval when imperfect information is enforced in a real-time strategy game.
Optimizing Motion-Constrained Pathfinding
Authors: Sturtevant Nathan
Published: October 2022

High-quality motion planning can be quite expensive to perform, yet is critical for many video games. As a result, many approximations are employed to reduce costs. Common approximations include pathfinding on a grid or on a navigation mesh instead of in a real-valued world. As a result, the paths that are found are not immediately appropriate for use, and often require post-processing, such as smoothing. An alternate approach is to incorporate pathfinding constraints directly into A* search. This approach is expensive and so it hasn't been widely applied in games. In this paper we analyze the complexity of planning with complex motion constraints and suggest optimizations which make such planning feasible for use in modern games. We propose the use of two ideas, abstract perimeter heuristics and intermediate search truncation, which can reduce the cost of search by an order of magnitude or more.
Dynamic Artificial Fields for Autonomous Camera Control
Authors: Burelli Paolo , Jhala Arnav
Published: October 2022

Camera control in real-time interactive 3D applications is a challenging problem. Developing a generalized system able to produce high quality visual results and smooth camera movements in dynamic environments remains an open problem in the research community. In this paper, we describe the implementation and evaluation of Artificial Potential Fields for automatic camera placement. We first describe the recasting of the frame composition problem as a solution to a two particles suspended in an Artificial Potential Field. We demonstrate the application of this technique to control both camera location and camera aim direction. We show that this technique can be successfully used to solve both camera animation and frame composition tasks in dynamic environments in real-time through an example implemented on an existing 3D game engine.
A Real-Time PDDL-based Planning Component for Video Games
Authors: Bartheye Olivier , Jacopin Éric
Published: October 2022

In this paper, we argue that PDDL-based real-time planning can be achieved for today's video-games. PDDL is an expressive language which offers to represent many planning knowledge while freeing from the plan search algorithms. We present two case studies where we connected a PDDL-based AI Planner to a video-game. In each case, we report on the engineering decisions which turned out to be crucial to achieve real-time playability.
Supporting Dialogue Generation for Story-Based Games
Authors: Kerr Christopher , Szafron Duane
Published: October 2022

Providing compelling, realistic, immersive game worlds is one of the major goals in modern game design. The presence of unique and interesting dialogue for all of the characters in a game enhances this sense of immersion. In this paper we present the concept of an “Intentional Dialogue Line” that supports the efficient generation of multiple variations of a dialogue, where these variations are both unique and appropriate to the character who is speaking them. This paper focuses on how machine learning can be used to quickly polines with existing content.
Tanagra: An Intelligent Level Design Assistant for 2D Platformers
Authors: Smith Gillian , Whitehead Jim , Mateas Michael
Published: October 2022

We present a demonstration of Tanagra, an intelligent level design assistant for 2D platformers. Tanagra integrates reactive planning and constraint programming to automatically generate levels and respond to designer changes in realtime. A reactive planning language, ABL, allows us to easily express hierarchical patterns of level geometry, from simple patterns such as a jump over a gap, to more complex patterns such as staircases and pits. A model of player movement and constraints within and between geometry components ensure that the levels created by our system are always playable.
The Pataphysic Institute
Authors: Eladhari Mirjam
Published: October 2022

The Pataphysic Institute (PI) is a research prototype multi-player game world. In PI, the personalities of the inhabitants are the base for the game mechanics. When interacting with other characters the potential emotional reactions depend upon avatars' current mood and personality. PI is built with inspiration from personality psychology and affect theory in an attempt to mimic possible emotional responses in order to give the player support in role-playing. The mental states of characters depend on their personalities and on their current moods. Moods differ according to context and to recent experiences. Emotional experiences become memories and define the relationships between characters. The mental state is the sum of the character and governs what actions can be performed in a given moment. In order to do certain things the characters need to be in certain moods — and for this the players need to game their avatars' emotions, and game their relationships.
The Prom: An Example of Socially-Oriented Gameplay
Authors: McCoy Joshua , Treanor Mike , Samuel Ben , Tearse Brandon , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

The Prom is a game where the player manages the social life of a group of high school students and creates the situations from which dramatic, thought provoking or at least funny stories can unfold. The setting of The Prom involves a group of alternative high school kids (e.g. Emos, Goths, Geeks, etc.) and their dramatic lives as they prepare for the upcoming school prom. Through creating friendships, making people become enemies, controlling who gets to be in the "in" crowd and much more, the player can shape the social world of the characters. Each character has a distinct personality represented by interests (e.g. what bands they like), needs (e.g. a character may need to demonstrate a certain degree of dominance over others), traits (e.g. being a particularly jealous person), social networks (e.g. to what degree a characters like, are attracted to or respect one another) and social status (e.g. who is dating who).The social artificial intelligence system Comme il Faut (CiF) drives this gameplay experience by simulating per character needs and traits, social statuses, social networks, social history and most importantly to gameplay, the outcomes and effects of social games. CiF is a playable computational model of social interactions designed specifically to allow autonomous characters to play social games. By giving player controls to navigate a social, rather than physical, space, The Prom is being created to demonstrate how CiF and social games can create a practically limitless numbers of possibly compelling stories and gameplay.
Rapid Development of Characters in FPS/3PS Games Using Visually-Specified Behavior-based Control
Authors: Youngblood G. , Heckel Frederick , Hale D. , Carroll Arthur
Published: October 2022

First/third-person simulations in virtual environments have become increasingly used in training; however, creating intelligent, interactive characters to populate these environments presents a large authorial burden. Our work focuses on building tools to enable rapid creation of intelligent characters for first/third-person game-like environments with no programming knowledge required by the user. This is made possible using behavior-based control combined with a user interface employing natural language-like character specification in the form of English sentences and interactive testing during development.
Preface
Authors: Youngblood G. , Bulitko Vadim
Published: October 2022

The goal for the AIIDE conference is to be the primary yearly meeting where industry and academe discuss AI for interactive digital enter- tainment. It is organized and run by a combination of industry developers and academic researchers. This year is the fourth time the conference is being held at Stanford University in Palo Alto, California, making it accessible to industrial developers as well as attractive to all participants.
An Automated Technique for Drafting Territories in the Board Game Risk
Authors: Gibson Richard , Desai Neesha , Zhao Richard
Published: October 2022

In the standard rules of the board game Risk, players take turns selecting or "drafting" the 42 territories on the board until all territories are owned. We present a technique for drafting territories in Risk that combines the Monte Carlo tree search algorithm UCT with an automated evaluation function. Created through supervised machine learning, this function scores outcomes of drafts in order to shorten the length of a UCT simulation. Using this approach, we augment an existing bot for the computer game Lux Delux, a clone of Risk. Our drafting technique is shown to greatly improve performance against the strongest opponents supplied with Lux Delux. The evidence provided indicates that territory drafting is important to overall success in Risk.
Perceptually Realistic Behavior through Alibi Generation
Authors: Sunshine-Hill Ben , Badler Norman
Published: October 2022

Real-time pedestrian simulation for open-world games involves aggressive behavior simplification and culling to keep computational cost under control, but it is diffficult to predict whether these techniques will become unrealistic in certain situations. We propose a method of perceptually simulating highly realistic pedestrian behavior in virtual cities in realtime. Designers build a highly realistic simulation, from which a perceptually identical "perceptual simulation" is generated. Although the perceptual simulation simulates only a small portion of the world at a time, and does so with inexpensive approximations, it can be statistically guaranteed that the results are perceptually indistinguishable from those of the original simulation.
Story and Text Generation through Computational Analogy in the Riu System
Authors: Ontanon Santiago , Zhu Jichen
Published: October 2022

A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.
Training Goal Recognition Online from Low-Level Inputs in an Action-Adventure Game
Authors: Gold Kevin
Published: October 2022

A method is presented for training an Input-Output Hidden Markov Model (IOHMM) to identify a player's current goal in an action-adventure game. The goals were Explore, Fight, or Return to Town, which served as the hidden states of the IOHMM. The observation model was trained by directing the player to achieve particular goals and counting actions. When trained on first-time players, training to the specific players did not appear to provide any benefits over a model trained to the experimenter. However, models trained on these players' subsequent trials were significantly better than the models trained to the specific players the first time, and also outperformed the model trained to the experimenter. This suggests that game goal recognition systems are best trained after the players have some time to develop a style of play. Systems for probabilistic reasoning over time could help game designers make games more responsive to players' individual styles and approaches.
Towards Automatic Personalized Content Generation for Platform Games
Authors: Shaker Noor , Yannakakis Georgios , Togelius Julian
Published: October 2022

In this paper, we show that personalized levels can be auto- matically generated for platform games. We build on previ- ous 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 learn- ing, based on questionnaires administered to players after playing different levels. The contributions of the current pa- per 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 adap- tation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.
A Comparison of High-Level Approaches for Speeding Up Pathfinding
Authors: Sturtevant Nathan , Geisberger Robert
Published: October 2022

Most games being shipped today use some form of high-level abstraction such as a navmesh or waypoint graph for path planning. These structures can generally be represented in a form which is compact enough to meet the tight memory constraints in a game. But, when such a graph grows too large, finding paths can still be a complex task. This challenge was faced in Dragon Age: Origins and solved by adding an additional level of abstraction.In the last few years a variety of novel approaches have been developed for finding optimal paths through graphs with specific design applications for road networks. Currently these techniques cannot be feasibly applied to the lowest detail of movement possible in a game map, but can be applied to the high-level abstractions which are commonly found in games.In this paper we describe the pathfinding challenge faced before shipping the title Dragon Age: Origins and perform a postmortem analysis on the extended abstraction that was used in comparison to building more advanced heuristics or the use of contraction hierarchies. We show that contraction hierarchies and abstractions have similar overhead and performance and are both useful approaches for high-level planning in games.
An Automated Model-Based Adaptive Architecture in Modern Games
Authors: Tan Chek Tien , Cheng Ho-lun
Published: October 2022

This paper proposes an automatic model-based approach that enables adaptive decision making in modern virtual games. It builds upon the Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly time-consuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality for the POMDP, the work in this paper aims to resolve these problems. Empirical proof of concept is shown based on an implementation in a modern game scenario, whereby the enhanced IMPLANT agent is shown to exhibit superior adaptation performance over the old IMPLANT agent whilst eliminating manual model specifications and at the same time still maintaining plausible speeds.
Quest Patterns for Story-Based Computer Games
Authors: Trenton Marcus , Szafron Duane , Friesen Josh , Onuczko Curtis
Published: October 2022

As game designers shift focus from graphical realism to immersive stories, the number of game-object interactions grows exponentially. Games use manually written scripts to control interactions. ScriptEase provides game designers with generative patterns that generate scripting code to control common interactions. This paper describes a new kind of generative pattern, quest patterns, that generate scripting code to control story plot. We present our quest pattern architecture and study results that show quest patterns are easy-to-use and reduce plot scripting errors.
Modeling Narrative Conflict to Generate Interesting Stories
Authors: Ware Stephen , Young R.
Published: October 2022

From subtle political intrigue to outright physical combat, conflict is essential to interesting stories. Narratology research emphasizes that conflict provides structure and engagement, so narrative systems stand to benefit greatly from a computational model of this phenomenon. We present such a model based on AI planing, along with formulas for measuring seven essential properties: participants, subject, duration, directness, intensity, balance, and resolution. We also sketch an algorithm which uses this model to create stories structured around a central struggle.
Fuzzy Micro-Agents for Interactive Narrative
Authors: Magerko Brian , Fiesler Casey , Baumer Allan
Published: October 2022

This paper describes our current approach in implementing computational improvisational micro-agents. This approach is intended to foster bottom-up research to better understand how to build more complex agent behaviors in a theatrical improvisational setting. Micro-agent designs are based on our current findings in a multi-year study focused on studying real life theatrical improvisers with an aim towards better understanding the cognition employed inimprovisation at the individual and group level. It also introduces a key architectural component from the domain of fuzzy logic that enables us to clearly represent some of our current findings.
Detecting Real Money Traders in MMORPG by Using Trading Network
Authors: Fujita Atsushi , Itsuki Hiroshi , Matsubara Hitoshi
Published: October 2022

We have developed a method for detecting real money traders (RMTers) to support the operators of massively multiplayer online role-playing games (MMORPGs). RMTers, who earn currency in the real world by selling properties in the virtual world, tend to form alliances and frequently exchange a huge volume of virtual currency within such a community. The proposed method exploits (1) the trading network, to identify the communities of characters, and (2) the volume of trades, to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG showed that using the trading network is more effective in detecting RMTers than conventional machine learning methods that assess individual character without referring to the trading network.
All the World's a Stage: Learning Character Models from Film
Authors: Lin Grace , Walker Marilyn
Published: October 2022

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.
Learning Probabilistic Behavior Models in Real-Time Strategy Games
Authors: Dereszynski Ethan , Hostetler Jesse , Fern Alan , Dietterich Tom , Hoang Thao-Trang , Udarbe Mark
Published: October 2022

We study the problem of learning probabilistic models of high-level strategic behavior in the real-time strategy (RTS) game StarCraft. The models are automatically learned from sets of game logs and aim to capture the common strategic states and decision points that arise in those games. Unlike most work on behavior/strategy learning and prediction in RTS games, our data-centric approach is not biased by or limited to any set of preconceived strategic concepts. Further, since our behavior model is based on the well-developed and generic paradigm of hidden Markov models, it supports a variety of uses for the design of AI players and human assistants. For example, the learned models can be used to make probabilistic predictions of a player's future actions based on observations, to simulate possible future trajectories of a player, or to identify uncharacteristic or novel strategies in a game database. In addition, the learned qualitative structure of the model can be analyzed by humans in order to categorize common strategic elements. We demonstrate our approach by learning models from 331 expert-level games and provide both a qualitative and quantitative assessment of the learned model's utility.
A Computational Model of Perceived Agency in Video Games
Authors: Thue David , Bulitko Vadim , Spetch Marcia , Romanuik Trevon
Published: October 2022

Agency, being one's ability to perform an action and have some influence over the world, is fundamental to interactive entertainment. Although much of the games industry is concerned with providing more agency to its players, what seems to matter more is how much agency each player will actually perceive. In this paper, we present a computational model of this phenomena, based on the notion that the amount of agency that one perceives depends on how much they desire the outcomes that result from their decisions. Using a structure for high-agency stories that we designed specifically for this intent, we present the results of a 141-participant user study that tests our model's ability to select subsequent events in an original interactive story. Using a newly validated survey instrument for measuring both agency and fun, we found with a high degree of confidence that event sequences selected by our model result in players perceiving more agency than players who experience event sequences that our model does not recommend.
Ultra-Fast Optimal Pathfinding without Runtime Search
Authors: Botea Adi
Published: October 2022

Pathfinding is important in many applications, including games, robotics and GPS itinerary planning. In games, most pathfinding methods rely on runtime search. Despite numerous enhancements introduced in recent years, runtime search has the disadvantage that, in bad cases, most parts of a map need to be explored, causing a time performance degradation. In this work we explore a significantly different approach to pathfinding, eliminating the need for runtime search. Optimal paths between all pairs of locations are pre-computed. Since straightforward ways to store pre-computed paths are prohibitively expensive even for maps of moderate size, pre-computed data are compressed, reducing the memory requirements dramatically. At runtime, pathfinding is very fast, as it requires visiting only the locations on an optimal path. In each location, a quick computation provides the next move along the optimal path. We demonstrate the effectiveness of this approach on Baldur's Gate game maps. The compression factor reaches two orders of magnitude, bringing the memory requirements down to reasonable values. Compared to A* search, the runtime speedup reaches and even exceeds two orders of magnitude. When averaged over paths of similar cost, the speedup reaches a value of 700 in our experiments.
DEXTOR: Reduced Effort Authoring for Template-Based Natural Language Generation
Authors: Narayan Karthik , Isbell Charles , Roberts David
Published: October 2022

A growing issue in the development of realistic and entertain-ing interactive games is the need for mechanisms that support ongoing natural language conversation between human players and artificial non-player characters. Unfortunately, many methods for implementing natural language generation(NLG) induce a significant burden on the author, do not scale well, or require specialized linguistic knowledge. We formalize the notion of typed-templates, an extension of standard structures employed in template-based NLG. We further provide novel algorithms that, when applied to typed-templates, ameliorate the above issues by affording computational support for authoring and increased variation in utterance and scenario generation. We demonstrate the efficacy of typed-templates and the algorithms through a user study.
Trigram Timmies and Bayesian Johnnies: Probabilistic Models of Personality in Dominion
Authors: Gold Kevin
Published: October 2022

Probabilistic models were fit to logs of player actions in the card game Dominion in an attempt to find evidence of personality types that could be used to classify player behavior as well as generate probabilistic bot behavior. Expectation Maximization seeded with players' self-assessments for their motivations was run for two different model types — Naive Bayes and a trigram model — to uncover three clusters each. For both model structures, most players were classified as belonging to a single large cluster that combined the goals of splashy plays, clever combos, and effective play, cross-cutting the original categories — a cautionary tale for research that assumes players can be classified into one category or another. However, subjects qualitatively report that the different model structures play very differently, with the Naive Bayes model more creatively combining cards.
Selecting Agents for Narrative Roles
Authors: Shoulson Alexander , Garcia Daniel , Badler Norman
Published: October 2022

We present ongoing work on a system that accommodates player agency in a digital narrative with an external plot. We focus on key events that should occur in that storyline for dramatic effect, but do not explicitly specify the characters that should fill the roles needed for those events. Instead, we define them abstractly, with characteristics that the selected characters should have (including previous events they should have completed for eligibility), and rely on a Director construct to populate those roles from agents in the selection pool that fit those criteria. Agents begin as largely homogeneous, primordial entities that accumulate data and narrative value from the events in which they participate. This creates an environment that differentiates characters by the actions they perform, conferring worth onto characters that become important to the player based on their direct involvement in the plot. The focus, then, is on defining a priori the what of the narrative, while leaving it to the Director construct to decide at runtime exactly who among a distributed pool of agents carries it out.
The Case for Intention Revision in Stories and its Incorporation into IRIS, a Story-Based Planning System
Authors: Fendt Matthew , Young R. Michael
Published: October 2022

Character intention revision is an essential component of stories, but it has yet to be incorporated into story generation systems. However, intentionality, one component of intention revision, has been explored in both narrative generation and logical formalisms. The IRIS system adopts the belief/desire/intention framework of intentionality from logical formalisms and combines it with preexisting concepts of intentionality in narrative. IRIS also introduces the crucial concept of intention revision for characters in the story. The intent of this synthesis is to create stories with dynamic and believable characters that update their beliefs, replan, and revise their intentions over the course of the story.
Automaticity and Expressive Behavior in Virtual Actors: Notes on the Organization of Mammalian Behavior Systems
Authors: Horswill Ian
Published: October 2022

Much of the most expressive behavior in humans - expressions of shock or alarm, gaze aversion, or explosive rage - are the result of automatic processes that engage before deliberative processing can respond. In some cases, such as weeping, the deliberative system may have only limited ability to override the automatic system. These processes are implemented by a network of phylogenetically old, special purpose, somewhat redundant systems that give rise to the particular idiosyncratic behavior we associate with automatic reactions to emotional events. In this paper, I'll review some of the ethological and neuropsychological results on low-level systems related to threat response, and their relation to the simulation of virtual characters. I will also discuss work in progress on building a medium-fidelity simulation of these systems.
Murder in the Arboretum: Comparing Character Models to Personality Models
Authors: Walker Marilyn , Lin Grace , Sawyer Jennifer , Grant Ricky , Buell Michael , Wardrip-Fruin Noah
Published: October 2022

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum. We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.
On the Use of Reincorporation in Interactive Drama
Authors: Tomaszewski Zach
Published: October 2022

Marlinspike is an interactive drama system. It produces a story by playing short pre-authored story components called scenes in response to the actions of a human player. Marlinspike selects the next scene to play so as to reincorporate — or narratively build upon — as much of the story-so-far as possible. In accordance with an Aristotelian definition of narrative unity, this reincorporation serves to make earlier events--particularly the player's actions--narratively necessary to the finished story. An evaluation with human players of Marlinspike's novel reincorporation feature produced mixed results. On the one hand, reincorporation does indeed produce internal story structures that are more unified and that include more of the players' significant actions. However, the players themselves did not report a correspondingly improved experience of either story quality or story-level agency.
Suggesting New Plot Elements for an Interactive Story
Authors: Giannatos Spyridon , Nelson Mark , Cheong Yun-Gyung , Yannakakis Georgios
Published: October 2022

We present a system that uses evolutionary optimization to suggest new story-world events that, if added to an existing interactive story, would most improve the average interactive experience, according to author-supplied criteria. In doing so, we aim to apply some of the ideas from drama-managed storytelling, such as authorial aesthetic control, in an unguided setting more akin to emergent storytelling: rather than guiding or directing a player towards an experience in line with an author's aesthetic goals, the storyworld is augmented with new content in a way that will tend to align with an author's goals, even if the player is not guided. In this paper, we present an offline system, and demonstrate its robustness to a number of variations in authorial criteria and player-model assumptions. This is intended to lay the groundwork for a future system that would generate new content online, allowing for interactive stories larger than those explicitly written by the author.
Simulating Mechanics to Study Emergence in Games
Authors: Dormans Joris
Published: October 2022

This paper presents the latest version of the Machinations framework. This framework uses diagrams to represent the flow of tangible and abstract resources through a game. This flow represents the mechanics that make up a game’s interbal economy and has a large impact on the emergent gameplay of most simulation games, strategy games and board games. This paper shows how Machinations diagrams can be used simulate and balance games before they are built.
Sponsors
Authors: Director AAAI
Published: October 2022

List of sponsors of the 2012 AIIDE Conference.
Understanding Speech in Interactive Narratives with Crowdsourced Data
Authors: Orkin Jeff , Roy Deb
Published: October 2022

Speech recognition failures and limited vocabulary coverage pose challenges for speech interaction with characters in games. We describe an end-to-end system for automating characters from a large corpus of recorded human game logs, and demonstrate that inferring utterance meaning through a combination of plan recognition and surface text similarity compensates for recognition and understanding failures significantly better than relying on surface similarity alone.
Fast Procedural Level Population with Playability Constraints
Authors: Horswill Ian , Foged Leif
Published: October 2022

We examine the use of constraint propagation for populating indoor game levels with enemies and other objects.  We introduce a notion of path constraints, which bound some function over the possible paths a player might take, and show how to efficiently place objects while guaranteeing path constraints.  This allows the system to guarantee that power-ups are balanced to the number of enemies occurring in the level, that they’re placed early enough to be useful, that keys are not hidden behind the doors they are intended to unlock, and so on. We describe a constraint solver based on interval methods that allows natural processing of numeric constraints and show that it is efficient enough to be used even on very low-end platforms.