bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Multi-Layer Level Representations for Puzzle-Platformer Level Generation
Authors: Snodgrass Sam , Ontañón Santiago
Published: October 2022

Procedural content generation via machine learning (PCGML) has been growing in recent years. However, many PCGML approaches are only explored in the context of linear platforming games, and focused on modeling structural level information. Previously, we developed a multi-layer level representation, where each layer is designed to capture specific level information. In this paper, we apply our multi-layer approach to Lode Runner, a game with non-linear paths and complex actions. We test our approach by generating levels for Lode Runner with a constrained multi-dimensional Markov chain (MdMC) approach that ensures playability and a standard MdMC sampling approach. We compare the levels sampled when using multi-layer representation against those sampled using the single-layer representation; we compare using both the constrained sampling algorithm and the standard sampling algorithm.
Answer Set Programming in Proofdoku
Authors: Smith Adam
Published: October 2022

Proofdoku is an AI-based game design that extends Sudoku. In addition to playing by the rules of the traditional logic puzzle, players must explain their reasoning. An AI system checks this reasoning and provides hints that guide the player to discover new reasoning patterns for themselves. Co-developing the game design and the AI system, implemented using the technology of Answer Set Programming (ASP), guided us to include features that depend on high-complexity combinatorial search and optimization. We developed Proofdoku to better understand the implications of designing and deploying game systems that depend on ASP for live interaction. This paper offers design tradeoffs and makes suggestions for future deployments of ASP-backed game designs.
A General Level Design Editor for Co-Creative Level Design
Authors: Guzdial Matthew , Chen Jonathan , Chen Shao-Yu , Riedl Mark
Published: October 2022

In this paper we describe a level design editor designed as an interface to allow different AI agents to creatively collaborate on level design problems with human designers. We intend to investigate the comparative impacts of different AI techniques on user experience in this context.
Planning Graphs for Efficient Generation of Desirable Narrative Trajectories
Authors: Amos-Binks Adam , Potts Colin , Young R.
Published: October 2022

A goal of Experience Managers (EM) is to guide users through a space of narrative trajectories, or story branches, in an Interactive Narrative (IN). When a user performs an action that deviates from the intended trajectory, the EM uses a mediation strategy called accommodation to transition the user to a new desirable trajectory. However, generating the trajectory options then selecting the appropriate one is computationally expensive and at odds with the low-latency needs of an IN. We define three desirable properties (exemplar trajectories, narrative-theoretic comparison, and efficiency) that general solutions would possess and demonstrate how our plan-based Intention Dependency Graph addresses them.
Unsupervised Text Classification for Natural Language Interactive Narratives
Authors: Bellassai Jenna , Gordon Andrew , Roemmele Melissa , Cychosz Margaret , Odimegwu Obiageli , Connolly Olivia
Published: October 2022

Natural language interactive narratives are a variant of traditional branching storylines where player actions are expressed in natural language rather than by selecting among choices. Previous efforts have handled the richness of natural language input using machine learning technologies for text classification, bootstrapping supervised machine learning approaches with human-in-the-loop data acquisition or by using expected player input as fake training data. This paper explores a third alternative, where unsupervised text classifiers are used to automatically route player input to the most appropriate storyline branch. We describe the Data-driven Interactive Narrative Engine (DINE), a web-based tool for authoring and deploying natural language interactive narratives. To compare the performance of different algorithms for unsupervised text classification, we collected thousands of user inputs from hundreds of crowdsourced participants playing 25 different scenarios, and hand-annotated them to create a gold-standard test set. Through comparative evaluations, we identified an unsupervised algorithm for narrative text classification that approaches the performance of supervised text classification algorithms. We discuss how this technology supports authors in the rapid creation and deployment of interactive narrative experiences, with authorial burdens similar to that of traditional branching storylines.
"The Long Walk" From Linear Film to Interactive Narrative
Authors: Prasad Keshav , Briët Kayla , Odimegwu Obiageli , Connolly Olivia , Gonzalez Diego , Gordon Andrew
Published: October 2022

Advances in hardware and software for virtual reality and 360-degree video afford new opportunities for immersive digital storytelling, but also pose new challenges as players seek an increased sense of meaningful agency in fictional story-worlds. In this paper, we explore the interaction designs afforded by voice-controlled interactive narratives, where players speak their intended actions when prompted at choice points in branching storylines. We describe seven interaction design patterns that balance the player's need for meaningful agency with an author's goal to present an intended storyline. We argue that these structural designs are orthogonal to the content of a story, such that any particular story may be effectively restructured to use different patterns. By way of demonstration, we describe our efforts to remix and restructure a 360-degree film entitled "The Long Walk", transforming it from a largely linear narrative with minimal interactivity into a voice-controlled interactive narrative with meaningful player agency.
Leveraging Cognitive Models in Planning to Assist Narrative Authoring
Authors: Sanghrajka Rushit
Published: October 2022

My research aims to contribute to research in the narrative authoring domain by using cognitive models in narrative plan generation. These cognitive models determine how actions and events in narrative affect the audience. My research intends to leverage these models in narrative planning and use them to provide intelligent narrative plans that are structured to invoke specific responses from audiences when they experience the narrative. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning.
POMCP with Human Preferences in Settlers of Catan
Authors: Dobre Mihai , Lascarides Alex
Published: October 2022

We present a suite of techniques for extending the Partially Observable Monte Carlo Planning algorithm to handle complex multi-agent games. We design the planning algorithm to exploit the inherent structure of the game. When game rules naturally cluster the actions into sets called types, these can be leveraged to extract characteristics and high-level strategies from a sparse corpus of human play. Another key insight is to account for action legality both when extracting policies from game play and when these are used to inform the forward sampling method. We evaluate our algorithm against other baselines and versus ablated versions of itself in the well-known board game Settlers of Catan.
Keeping the Story Straight: A Comparison of Commitment Strategies for a Social Deduction Game
Authors: Eger Markus , Martens Chris
Published: October 2022

Social deduction games present a unique challenge for AI agents, because communication plays a central role in most of them, and deception plays a key role in game play. To be successful in such games, players need to come up with convincing stories, but also discern the truth of statements of other players and adapt to the information learned from them. In this paper we present an approach for virtual agents that have to determine how long to stick to their story in the light of information obtained from other players. We apply this approach to a particular social deduction game, One Night Ultimate Werewolf, and demonstrate the effect of different levels of commitment to an agent's story.
Automated Game Design via Conceptual Expansion
Authors: Guzdial Matthew , Riedl Mark
Published: October 2022

Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion.Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion.We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.
Evolutionary MCTS with Flexible Search Horizon
Authors: Baier Hendrik , Cowling Peter
Published: October 2022

In turn-based multi-action adversarial games each player turn consists of several atomic actions, resulting in an extremely high branching factor. Many strategy board, card, and video games fall into this category, which is currently best played by Evolutionary MCTS (EMCTS) -- searching a tree with nodes representing action sequences as genomes, and edges representing mutations of those genomes. However, regular EMCTS is unable to search beyond the current player's turn, leading to strategic short-sightedness. In this paper, we extend EMCTS to search to any given search depth beyond the current turn, using simple models of its own and the opponent's behavior. Experiments on the game Hero Academy show that this Flexible-Horizon EMCTS (FH-EMCTS) convincingly outperforms several baselines including regular EMCTS, Online Evolutionary Planning (OEP), and vanilla MCTS, at all tested numbers of atomic actions per turn. Additionally, the separate contributions of the behavior models and the flexible search horizon are analyzed.
CatSAT: A Practical, Embedded, SAT Language for Runtime PCG
Authors: Horswill Ian
Published: October 2022

Answer-set programming (ASP), a family of SAT-based logic programming systems, is attractive for procedural content generation. Unfortunately, current solvers present significant barriers to runtime use in games. In this paper, I discuss some of the issues involved, and present CatSAT, a solver designed to better fit the run-time resource constraints of modern games. Although intended only for small problems, it allows designers to compactly specify simple PCG problems such as NPC generation, solve them in a few tens of microseconds, and to adapt solutions dynamically based on the changing needs of gameplay. We hope that by making adoption as convenient as possible, we can increase the uptake of declarative techniques among developers.
CADI — A Conversational Assistive Design Interface for Discovering Pong Variants
Authors: Mobramaein Afshin , Behrooz Morteza , Whitehead Jim
Published: October 2022

Mixed-initiative PCG systems provide a way to leverage the expressive power of algorithmic techniques for content generation in a manner that lowers the technical barrier for content creators. While these tools are a proof of concept of how PCG systems can aide aspiring designers reach their vision, there are issues pertaining capturing designer intent, and interface complexity. In this paper we introduce CADI (Conversational Assistive Design Interface) a mixed initiative PCG system for creating variations of the game Pong that utilizes natural language input through a natural language interface to explore the design space of Pong variations. We provide a motivation for the creation of CADI and discuss the implementation and design decisions taken to address the issues of designer intent and interface complexity in mixed-initiative PCG systems.
Like a DNA String: Sequence-Based Player Profiling in Tom Clancy’s The Division
Authors: Canossa Alessandro , Makarovych Sasha , Togelius Julian , Drachenn Anders
Published: October 2022

In this paper we present an approach to using sequence analysis to model player behavior. This approach is designed to work in game development contexts, integrating production teams and delivering profiles that inform game design. We demonstrate the method via a case study of the game Tom Clancy’s The Division, which with its 20 million players represents a major current commercial title. The approach presented provides a mixed-methods framework, combining qualitative knowledge elicitation and workshops with large-scale telemetry analysis, using sequence mining and clustering to develop detailed player profiles showing the core game-play loops of The Division’s players.
"Superstition" in the Network: Deep Reinforcement Learning Plays Deceptive Games
Authors: Bontrager Philip , Khalifa Ahmed , Anderson Damien , Stephenson Matthew , Salge Christoph , Togelius Julian
Published: October 2022

Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning.
Levels from Sketches with Example-Driven Binary Space Partition
Authors: Snodgrass Sam
Published: October 2022

Procedural content generation via machine learning (PCGML) has been demonstrating its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. In this paper we present an example-driven adaptation of a classic PCG approach, binary space partition (BSP), that takes a structural template or sketch of a level and fills in the details from examples. We show that this example-driven adaptation can generate a diverse set of levels from a single structural template. We evaluate the levels generated in terms of difference between paths through the levels, amount of the level copied from the examples, and other common PCG level evaluation metrics. Furthermore, we compare this method to a Markov chain approach and show that our BSP approach matches the training level distribution better while generating a greater range of interesting features.
Knowledge-Powered Inference of Crowd Behaviors in Semantically Rich Environments
Authors: Zhang Xun , Schaumann Davide , Faloutsos Petros , Kapadia Mubbasir
Published: October 2022

Interactive authoring of collaborative, context-dependent virtual agent behaviors can be challenging. Current approaches often rely heavily on users’ input, leading to cumbersome behavior authoring experiences and biased results, which do not reflect realistic space-people interactions in virtual settings. To address these issues, we generate an ontology graph from commonsense knowledge corpus and use it to automatically infer behavior distributions that determine agents’ context-dependent interactions with the built environment. By means of a natural-language interface, users can interactively refine a building’s design by adding semantic labels to spaces and populating rooms with equipment following suggestions that the system provides based on commonsense knowledge. Based on the chosen setup, an authoring system automatically populates the environment and allocates agents to specific behaviors while satisfying a behavior distribution inferred from the ontology graph. This approach holds promise to help architects, engineers, and game designers interactively author plausible agent behaviors that reveal the mutual interactions between people and the spaces they inhabit.
Towards More Believable Characters Using Personality and Emotion
Authors: Shirvani Alireza
Published: October 2022

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. So far, there have been considerable advances in 3D and AI design to enhance virtual environments. Virtual characters are one of the crucial aspects of interactive narratives. The interaction of rich virtual characters can produce interesting narrative and as a result, enhance the experience of virtual environments. As one of those characters, the user would feel immersed and engaged when interacting with compelling characters. There are many characteristics that improve believable behavior generation, including beliefs, goals, desires, affect, and personality. My goal is to propose an affective model of personality for multi-agent narrative planning systems that is domain independent and thus minimizes authorial burden. I aim to combine existing models into a unified framework. Although the framework may oversimply those models, it would not overlook their key ingredients.
Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs
Authors: Cardona-Rivera Rogelio
Published: October 2022

This paper presents the foundations of a computational science of game design—a model of abstraction: the outcome of a situated design process that conceptualizes something in terms of something else for a specific purpose. The outcome of abstraction is modeled via an abstraction scheme, a step toward modeling the process of game design abstraction from a human-centered perspective. A scheme’s purpose is couched relative to a scheme’s properties, also defined herein; collectively, the properties represent the space of tradeoffs designers must navigate for engineering virtual worlds. This model’s analytical traction is evidenced by applying it to the design of pathfinding, a core behavior in artificial intelligence for games. More broadly, it is a foundation for shared progress because it affords directly comparing particular abstractions of concepts and phenomena across the gamut of research on intelligent systems in entertainment.
Chaos Cards: Creating Novel Digital Card Games through Grammatical Content Generation and Meta-Based Card Evaluation
Authors: Chen Tiannan , Guy Stephen
Published: October 2022

We present a novel framework to procedurally generate executable cards for Hearthstone-like digital card games, along with an approach for evaluating card strengths using an evolved match environment. Here we introduce Chaos Cards, a digital card game in the style of Hearthstone, but designed to support the procedural generation of cards, including their diverse effects, via a grammatical model. To understand the potential performance of procedurally generated cards in actual games, we integrate a simulation-based approach to evaluate card strengths, and train a neural network model for fast card strength prediction. Because the strength of a card is most meaningful when considered in the context of the pool of competitive decks (know as the meta) it plays in and against, we propose an evolutionary evaluation approach which simultaneously evaluates card strength and refines the environment in which cards are tested. We showcase some example cards generated by our framework, along with their strength evaluations. Additionally, we conduct tests between evaluations from meta game environments and random game environments to show the importance of the environment in evaluating card strengths. Lastly, we show our neural network is able to learn the strength of important cards in a meta environments with largely positive correlation.
Tribes: A New Turn-Based Strategy Game for AI Research
Authors: Perez-Liebana Diego , Hsu Yu-Jhen , Emmanouilidis Stavros , Khaleque Bobby , Gaina Raluca
Published: October 2022

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.
Deep Learning Bot for League of Legends
Authors: Lohokare Aishwarya , Shah Aayush , Zyda Michael
Published: October 2022

In this paper, we take the first step towards building comprehensive bots capable of playing a simplified version of League of Legends, a popular 5v5 online multiplayer game. We implement two different agents, one using Reinforcement Learning and the other via Supervised Long Short Term Memory Network. League of Legends provides a partially observable game environment with an action space much larger than games like Chess or Go. Source code and demonstrations can be found at https://github.com/csci-599-applied-ml-for-games/league-of-legends-bot.
A Demonstration of Mechanic Maker: An AI for Mechanics Co-Creation
Authors: Saini Vardan , Guzdial Matthew
Published: October 2022

Developing games is challenging as it requires expertise in programming skills and game design. This process is costly and consumes a great deal of time. In this paper we describe a tool we developed to help decrease this burden: Mechanic Maker. Mechanic Maker is designed to allow users to make 2D games without programming. A user interacts with a machine learning AI agent, which learns game mechanics by demonstration.
Towards Transferrable Affective Models for Educational Play
Authors: Spaulding Samuel
Published: October 2022

Modern computational agents in adaptive educational systems primarily rely on cognitive (i.e. curricular performance) data, while ignoring important multimodal affect cues which human tutors use to personalize their interactions with students. Students’ affective responses are highly idiosyncratic, noisy, and dependent on interactive context, challenges which defy many standard assumptions of computational player modeling. As a result, recent research efforts to model student affective response have focused on specific, single-task interactions, limiting the amount and variety of affective input from an individual player. For my thesis research, I plan to address these limitations in two ways. First, by developing a new paradigm for modeling student affective data, not as a scalar reward signal, but as a policy label, i.e., feedback on an agent’s recent behavior, and additionally by developing transfer learning methods to apply this policy feedback data across multiple game tasks. Together, these two advances may lead to more data-efficient learning and more flexible and generalizable affective models of players
Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain
Authors: Humenhuk João Felipe , Martins Kummer Luiz Bernardo , Cabrera Paraiso Emerson
Published: October 2022

Churn can be interpreted as customer defection and can be considered one of the most critical challenges in the Game Analytics domain because of its impact on the game industry's profit. When predicting churn, the first step is defining what is considered churn, which can change depending on the players' behaviors and approaches. This work studied related works and revealed two recurrent issues in the labeling process: limitations on the adopted labeling approaches (1) and the static definition of churn (2). To mitigate the first issue, an individualized labeling approach was deployed. To address the second one, a novel evaluation method, based on the impact of a change in the churn definition, was proposed. This method allowed the proposition of two new labeling approaches, which were included in the analysis. By comparing the labeling approaches in two games using a profit perspective, it was identified that the new ones present statistically significant benefits compared to the traditional ones. Regarding the evaluation method, its usage can justify when the redefinition of churn and the classifier's retraining should happen to improve profit. The results are valuable for the game context, potentially extended to other contexts by delivering more reliable labels and more validated classification performance.
A Force Dynamic Model of Narrative Agents
Authors: Kybartas Quinn , Verbrugge Clark , Lessard Jonathan
Published: October 2022

Emergent narratives are dependent on the behaviour modelling of agents in the storyworld. Typical approaches to behaviour modelling, however, overly encourage individual goals and needs, ignoring the dynamic interpersonal relations needed to create rising and falling conflicts. In this paper, we present a force dynamic agent model, in which relations and goals are modelled as forces, with the state of an agent treated as a physical object within an abstract space representing narrative tension. Building off previous work, we implement a test-bed and scenario in the Unity Game Engine. We show, through experimentation, that the dynamic force-based model causes the emergence of conflict curves mirroring traditional narrative perspectives. We conclude that an abstract physical modelling of narrative agents is able to embed several desirable properties for emergent narratives, namely, continuous conflict, dynamic interpersonal relations and temporal and strength modelling of the impact of individual actions.
MappyLand: Fast, Accurate Mapping for Console Games
Authors: Osborn Joseph C. , Summerville Adam , Dailey Nathan , Lim Soksamnang
Published: October 2022

We present MappyLand, a rewrite and enhancement of the earlier Mappy automatic game mapping system, which leverages instrumentation of a game console emulator to produce, from a sequence of game inputs, accurate annotations for action/adventure games including object detection and tracking, in-game camera movement, grid-based tile maps, and links between identified disparate spaces. The overhead of generating these annotations is on the order of one millisecond per observed frame.

We also show a higher latency (but still online) algorithm for merging together previous observations of distinct game maps for the purposes of agent localization across a long period of time. Specifically, our system can determine a consistent graph of game rooms from a set of strings of game rooms, capturing behaviors like backtracking and synthesizing observations from multiple play sessions.

Altogether, this fast, accurate approach to mapping yields new and useful knowledge representations and expedient ways to produce new datasets given just a game and some example play.
Effects of Deep Neural Networks on the Perceived Creative Autonomy of a Generative Musical System
Authors: Smith Jason , Freeman Jason
Published: October 2022

Collaborative AI agents allow for human-computer collaboration in interactive software. In creative spaces such as musical performance, they are able to exhibit creative autonomy through independent actions and decision-making. These systems, called co-creative systems, autonomously control some aspects of the creative process while a human musician manages others. When users perceive a co-creative system to be more autonomous, they may be willing to cede more creative control to it, leading to an experience that users may find more expressive and engaging.

This paper describes the design and implementation of a co-creative musical system that captures gestural motion and uses that motion to filter pre-existing audio content. The system hosts two neural network architectures, enabling comparison of their use as a collaborative musical agent. This paper also presents a preliminary study in which subjects recorded short musical performances using this software while alternating between deep and shallow models. The analysis includes a comparison of users' perceptions of the two models' creative roles and the models' impact on the subjects' sense of self-expression.
Hierarchical Dual Attention-Based Recurrent Neural Networks for Individual and Group Activity Recognition in Games
Authors: Ahmad Sabbir , Seif El-Nasr Magy , Elhamifar Ehsan
Published: October 2022

We study the problem of simultaneously recognizing complex individual and group activities from spatiotemporal data in games. Recognizing complex player activities is particularly important to understand game dynamics and user behavior having a wide range of applications in game development. To do so, we propose a novel framework by developing a hierarchical dual attention RNN-based method that leverages feature and temporal attention mechanisms in a hierarchical setting for effective discovery of activities using interactions among individuals. We argue that certain activities have dependency on certain features as well as on temporal aspects of the data which can be leveraged by our dual-attention model for recognition. To the best of our knowledge, this work is the first to address activity recognition using spatiotemporal data in games. In addition, we propose using game data as a rich source of obtaining complex group interactions. In this paper, we present two contributions: (1) two annotated game datasets that consist of individual and group activities, (2) our proposed framework improves the state-of-the-art recognition algorithms for spatiotemporal data by experiments on these datasets.
Search-Based Exploration and Diagnosis of TOAD-GAN
Authors: Edwards Maria , Jiang Ming , Togelius Julian
Published: October 2022

Generative Adversarial Networks (GANs) have been used with great success to generate images. They have also been applied to the task of Procedural Content Generation (PCG) in games, particularly for level generation, with various approaches taken to solving the problem of training data. One of those approaches, TOAD-GAN (Token-Based One-Shot Arbitrary Dimension Generative Adversarial Network) (Awiszus, Schubert, and Rosenhahn 2020), can generate levels based on a single training example and has been able to closely reproduce token patterns found in the training sample. While TOAD-GAN is an impressive achievement, questions remain about what exactly it has learned. Can the generator be made to produce levels that are substantially different from the level it has been trained on? Can it reproduce specific level segments? How different are the generated levels? We investigate these questions and others by using the CMA-ES algorithm for Latent Space Evolution. To make the search space feasible, we use a random projection in latent space. We propose the investigation undertaken here as a paradigm for studies into what machine-learned generators have actually learned, and also as a test of a new method for projecting from a smaller search space to a larger latent space.
Arachnophobia Exposure Therapy Using Experience-Driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)
Authors: Mahmoudi-Nejad Athar , Guzdial Matthew , Boulanger Pierre
Published: October 2022

Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. While there exist approaches to automatically adapt therapeutic content to a patient, they rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (EDPCGRL) to generate virtual spiders to match an individual patient. In this initial implementation, and due to the ongoing pandemic, we make use of virtual or artificial humans implemented based on prior arachnophobia psychology research. Our EDPCGRL method is able to more quickly adapt to these virtual humans with high accuracy in comparison to existing, search-based EDPCG approaches.
Crosston Tavern: Modulating Autonomous Characters Behaviour through Player-NPC Conversation
Authors: Oliver Elisabeth , Mateas Michael
Published: October 2022

NPCs (Non-Player Characters) are a staple of video games, filling all kinds of supporting roles. This work seeks to flip that paradigm and place the player in the role of support for the goals of a small collection of NPCs enabling a new kind of AI-driven gameplay. Built on a world simulation where NPCs can take action according to their goals and knowledge of the world state and a conversation space in which the NPC is able to report their actions and exchange information with the player, this prototype AI-based game design explores a new player-NPC interaction in which player conversational actions indirectly influence the NPC simulation. In this paper we discuss the architecture, provide a design postmortem, and report the results of play testing.
Revealing Game Dynamics via Word Embeddings of Gameplay Data
Authors: Rabii Younès , Cook Michael
Published: October 2022

In this paper we show how word embeddings, a technique used most commonly for natural language processing, can be repurposed to analyse gameplay data. Using a large study of chess games and applying the popular Word2Vec algorithm, we show that the resulting vector representation can reveal both common knowledge and subtle details about the game, including relative piece values and the natural spatial flow of chess play. Our results suggest that word embeddings are a cheap and simple technique that can provide a broad overview of a game’s dynamics, helping designers and critics form new hypotheses about a game’s design, structure and flow.
Automated Play-Testing through RL Based Human-Like Play-Styles Generation
Authors: Le Pelletier de Woillemont Pierre , Labory Rémi , Corruble Vincent
Published: October 2022

The increasing complexity of gameplay mechanisms in modern video games is leading to the emergence of a wider range of ways to play games. The variety of possible play-styles needs to be anticipated and taken into account by designers, through automated tests. Reinforcement Learning (RL) is a promising answer to the need of automating video game testing. To that effect one needs to train an agent to play the game, while ensuring this agent will generate the same play-styles as the players in order to give meaningful feedback to the designers.
We present CARMI : a Configurable Agent with Relative Metrics as Input. An agent able to emulate the players play-styles, even on previously unseen levels. Unlike current methods it does not rely on having full trajectories, but only summary data. Moreover it only requires little human data, thus compatible with the constraints of modern video game production. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.
Step: A Highly Expressive Text Generation Language
Authors: Horswill Ian
Published: October 2022

Games often generate text from human-authored templates and adapt that text to relevant context. Many systems have been developed to aid this process using tech-niques such as context-free grammars, randomization, logic programming, global state, and HTN planning.
In this paper, I present Step, a novel programming language for text generation. So far as I can determine, previous techniques can all be implemented within Step using a few lines of code. This allows designers to mix-and-match features as needed, without having to write an interpreter for a new language.
While extremely expressive as a programming language, Step is also intended to allow writers to add new text to an existing system with minimal markup and little knowledge of programming. In a head-to-head comparison, the Step implementation was more compact than, and used less markup than, a Prolog implementation of the same generator.
A Hybrid Approach to Co-creative Story Authoring Using Grammars and Language Models
Authors: Riddle Adam
Published: October 2022

Large language models are powerful tools for story generation, but are difficult to control. Story grammars are a more controllable tool for story generation, but require a large amount of upfront work and tend to create predictable results. We present a story generation tool that combines the positives of both methods using language models to aid in writing a grammar, and using the output of that grammar to generate controlled text from language models. Our approach combines the structure of a grammar with the unexpectedness of a large language model.
FarmQuest: A Demonstration of an AI Director Video Game Test Bed
Authors: Yu Kristen K. , Guzdial Matthew , Sturtevant Nathan R.
Published: October 2022

In AI director research, it is not straightforward for researchers to understand how each algorithm affects the player experience. This demo introduces PWR, which is a new fully developed video game test bed to evaluate AI directors. This demo includes 3 different AI director algorithms in order to help researchers improve their intuition for understanding the differences between potential algorithms, and also provides insight on the framework required to author a new AI director. This test bed can support future AI director research by allowing for direct comparisons of new algorithms.
The Nonsense Laboratory
Authors: Parrish Allison , Goldstick Jenny , Szetela Tim
Published: October 2022

We present the Nonsense Laboratory, a series of playful web-based tools for manipulating the way words sound and the way words are spelled. The Laboratory's various playful interfaces give users an opportunity to adjust, poke at, mangle, curate, rearrange and elaborate the sounds of words in written text. The project makes use of Pincelate, a code library and machine learning model for phoneme-to-grapheme and grapheme-to-phoneme tasks in English, trained on the CMU Pronouncing Dictionary. The goal of the project is twofold: first, to make possible new ways of manipulating spelling (akin to playing a musical instrument or modeling with clay); and second, to demystify machine learning by providing an intuitive, friendly interface to a machine learning model. We briefly describe the design and implementation of the Nonsense Laboratory's five interfaces, and situate the project in our own research concerning design, literary studies, linguistics, and machine learning.
Preface
Authors: Ware Stephen G. , Eger Markus
Published: October 2022

Proceedings of the Eighteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2022) preface by Stephen G. Ware and Markus Eger
Applying Constraint Weighting to Autonomous Camera Control
Authors: Bourne Owen , Sattar Abdul
Published: October 2022

Automating camera control for third-person perspective computer games is a difficult and time-consuming task. One of the challenges games developers confront is how to manage the trade-off between implementation complexity and system usability. In this study, we investigate the application of constraint weighting techniques to the autonomous camera control problem. We demonstrate that this technique can significantly simplify autonomous camera control and reduce the gap between implementation and usability requirements. We describe the use of weighting profiles to control the behaviour of the camera and specialized heuristics for efficiently searching for the solution. We also describe a novel integrated visibility maintenance method. As part of the experimental study, we implemented a 3D game engine that supports dynamic environments; and demonstrate the effectiveness of the use of constraint solving techniques for autonomous camera control problems.
Retaining Learned Behavior During Real-Time Neuroevolution
Authors: D’Silva Thomas , Janik Roy , Chrien Michael , Stanley Kenneth , Miikkulainen Risto
Published: October 2022

Creating software-controlled agents in videogames who can learn and adapt to player behavior is a difficult task. Using the real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real-time has been shown to be an effective way of achieving behaviors beyond simple scripted character behavior. In NERO, a videogame built to showcase the features of rtNEAT, agents are trained in various tasks, including shooting enemies, avoiding enemies, and navigating around obstacles. Training the neural networks to perform a series of distinct tasks can be problematic: the longer they train in a new task, the more likely it is that they will forget their skills. This paper investigates a technique for increasing the probability that a population will remember old skills as they learn new ones. By setting aside the most fit individuals at a time when a skill has been learned and then occasionally introducing their offspring into the population, the skill is retained. How large to make this milestone pool of individuals and how often to insert the offspring of the milestone pool into the general population is the primary focus of this paper.
Hierarchical Plan Representations for Encoding Strategic Game AI
Authors: Hoang Hai , Lee-Urban Stephen , Muñoz-Avila Héctor
Published: October 2022

In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations.
Particle-Based Communication Among Game Agents
Authors: Klaas Mike , Southey Tristram , Cheung Warren
Published: October 2022

One approach to creating realistic game AI is to create autonomous agents that can perform effectively with no more knowledge than a human player would have in their place. In a multi-agent setting, it is also necessary to devise a means for communicating among agents in collaborative game scenarios (such as a group of controlled agents that are searching for the player), since agents no longer have access to global knowledge. We present a method for communication using particle filters in the setting of game state estimation. Particle filters are an efficient, nonparametric means of performing inference in complex environments. Their use in game AI is particularly compelling, as they provide an easy way to represent nonlinear, non-Gaussian inferences about the state space, while exhibiting computational thrift. We demonstrate that communication among a group of agents — using particle filters to reason about the state space — can be accomplished in a natural way by sharing particles among the agents' filters. We also show how a criterion for deciding when to communicate naturally falls out of this framework. We apply this model in the setting of coordinated target detection, and find that agents of heterogenous types and complexities can nevertheless coordinate effectively
Unscripted Narrative for Affectively Driven Characters
Authors: Louchart Sandy , Aylett Ruth , Dias Joao , Paiva Ana
Published: October 2022

We discuss work carried out at the University of Salford (UK) and INESC-id (Portugal) on the design and implementation of interactive dramas in 3D graphics environments. We present detailed requirements and methods for the design of unscripted (emergent) dramas based on research into role-playing games. We describe the affectively driven intelligent autonomous characters implemented and discuss both theoretical and technical issues relating to its computational implementation. Finally we consider the implications for game design and new genres of game.
Structuring Content in the Façade Interactive Drama Architecture
Authors: Mateas Michael , Stern Andrew
Published: October 2022

The process of building Façade, a first-person, real-time, one-act interactive drama, has involved three major research efforts: designing ways to deconstruct a dramatic narrative into a hierarchy of story and behavior pieces; engineering an AI system that responds to and integrates the player's moment-by-moment interactions to reconstruct a real-time dramatic performance from those pieces; and understanding how to write an engaging, compelling story within this new organizational framework. This paper provides an overview of the process of bringing our interactive drama to life as a coherent, engaging, high agency experience, including the design and programming of thousands of joint dialog behaviors in the reactive planning language ABL, and their higher level organization into a collection of story beats sequenced by a drama manager. The process of iteratively developing the architecture, its languages, authorial idioms, and varieties of story content structures are described. These content structures are designed to intermix to offer players a high degree of responsiveness and narrative agency. We conclude with design and implementation lessons learned and future directions for creating more generative architectures.
Hierarchical Parallel Markov Models of Interaction
Authors: Zubek Robert , Horswill Ian
Published: October 2022

Finite state techniques are popular in entertainment software production, but they complicate the modeling of certain aspects of social engagement. In this paper we examine the problem of building probabilistic finite-state interaction models that allow both hierarchical composition of behaviors, and their parallel engagement. Finally, we propose an extension that resolves the difficulties for a class of common cases.
Demonstration: The Interactive Drama Façade
Authors: Mateas Michael , Stern Andrew
Published: October 2022

Façade 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. You, the player, using your own name and gender, play the character of a longtime friend of Grace and Trip, an attractive and materially successful couple in their early thirties. During an evening get-together at their apartment that quickly turns ugly, you become entangled in the high-conflict dissolution of Grace and Trip's marriage. No one is safe as the accusations fly, sides are taken and irreversible decisions are forced to be made. By the end of this intense one-act play you will have changed the course of Grace and Trip's lives — motivating you to re-play the drama to find out how your interaction could make things turn out differently the next time.