bluuebunny/crossref_metadata_2025 huggingface dataset:
Ferin Martino: A Small Piano Algorithm and Its Lessons on Creativity, Interaction, and Expression
Authors: Morris Jeffrey
Published: October 2022

The process of developing and making music with Ferin Martino, a piano-playing software algorithm has opened up a number of inquiries surrounding modern performance and creativity. Although it is relatively simple program, it is capable of much unique, expressive output that maintains a consistent style. Instead of constructing musical passages note by note, its musical character arises from its structure, independent of content. Recombination of previously-played material ensures that the first randomly generated notes end up yielding musically and stylistically coherent passages without compromising the variety of musical material produced. A flow of pseudorandom numbers is dis- turbed by motion in front of a camera, making the music responsive to its surroundings. Human-machine collaboration with this system has been effective in a variety of applications ranging from background music to computer-aided composition and live performance. A brief explanation of the algorithm is followed by ex- plorations of its resulting character; strengths and challenges; and perspectives it inspires on the relationships between humans and machines and on twenty-first century musicianship.
Leveraging Parallel Architectures in AI Search Algorithms for Games
Authors: Barriga Nicolas
Published: October 2022

This document contains a summary of research performed by the author on the topic of search algorithms for games. An outline of the problems being addressed is provided, along with the progress already made, and planned future work. The specific subjects studied are: parallelizing UCT search on GPUs, the development of a hierarchical search framework for Real-Time Strategy (RTS) games and the building placement problem in RTS games. We propose to take advantage of different parallel architectures to help solve these problems.
Organizer
Authors: Shaker Noor
Published: October 2022

List of organizers of the 2014 AIIDE Doctoral Consortium.
Automatic Real-Time Music Generation for Games
Authors: Engels Steve , Tong Tiffany , Chan Fabian
Published: October 2022

Music composition can be a challenge for many small- to medium-sized game companies, largely due to the expense and difficulty in creating original music for each level of a game. To address this, we developed a tool that automatically generates original music, by training a music generator on pieces whose style the game designer wishes to imitate. The generator then creates original music in that style in real-time, and switches between styles when signaled by the game. This software has been refined to produce music that is coherent and imitates a composer’s larger music structure.
Using Lanchester Attrition Laws for Combat Prediction in StarCraft
Authors: Stanescu Marius , Barriga Nicolas , Buro Michael
Published: October 2022

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. Winning battles is crucial in RTS games, and while humans can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately.A few existing models address this problem in the game of StarCraft but present many restrictions, such as not modeling injured units, supporting only a small number of unit types, or being able to predict the winner of a fight but not the remaining army. Prediction using simulations is a popular method, but generally slow and requires extensive coding to model the game engine accurately.This paper introduces a model based on Lanchester's attrition laws which addresses the mentioned limitations while being faster than running simulations. Unit strength values are learned using maximum likelihood estimation from past recorded battles. We present experiments that use a StarCraft simulator for generating battles for both training and testing, and show that the model is capable of making accurate predictions. Furthermore, we implemented our method in a StarCraft bot that uses either this or traditional simulations to decide when to attack or to retreat. We present tournament results (against top bots from 2014 AIIDE competition) comparing the performances of the two versions, and show increased winning percentages for our method.
Path Planning with Inventory-Driven Jump-Point-Search
Authors: Aversa Davide , Sardina Sebastian , Vassos Stavros
Published: October 2022

In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in videogames, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present InvJPS, an “inventory-driven” pathfinding approach based on the highly successful grid-based Jump-Point-Search (JPS) algorithm. We show, formally and experimentally, that the InvJPS preserves JPS’s optimality guarantees and its symmetry breaking advantages in inventory-based variants of game maps.
Width-Based Planning for General Video-Game Playing
Authors: Geffner Tomas , Geffner Hector
Published: October 2022

IW(1) is a simple search algorithm that assumes that states can be characterized in terms of a set of boolean features or atoms. IW(1) consists of a standard breadth-first search with one variation: a newly generated state is pruned if it does not make a new atom true. Thus, while a breadth-first search runs in time that is exponential in the number of atoms, IW(1) runs in linear time. Variations of the algorithm have been shown to yield state-of-the-art results in classical planning and more recently in the Atari video games. In this paper, we use the algorithm for selecting actions in the games of the general video-game AI competition (GVG-AI) which, unlike classical planning problems and the Atari games, are stochastic. We evaluate a variation of the algorithm over 30 games under different time windows using the number of wins as the performance measure. We find that IW(1) does better than the sample MCTS and OLMCTS controllers for all time windows with the performance gap growing with the window size. The exception are the puzzle-like games where all the algorithms do poorly. For such problems, we show that much better results can be obtained with the IW(2) algorithm, which is like IW(1), except that states are pruned in the breadth-first search when they fail to make true a new pair of atoms.
Maximizing Flow as a Metacontrol in Angband
Authors: Mariusdottir Thorey , Bulitko Vadim , Brown Matthew
Published: October 2022

Flow is a psychological state that is reported to improve people’s performance. Flow can emerge when the person’s skills and the challenges of their activity match. This paper applies this concept to artificial intelligence agents. We equip a decision-making agent with a metacontrol policy that guides the agent to activities where the agent’s skills match the activity difficulty. Consequently, we expect the agent’s performance to improve. We implement and evaluate this approach in the role-playing game of Angband.
Large-Scale Cross-Game Player Behavior Analysis on Steam
Authors: Sifa Rafet , Drachen Anders , Bauckhage Christian
Published: October 2022

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.
Path Planning on Grids: The Effect of Vertex Placement on Path Length
Authors: Bailey James , Tovey Craig , Uras Tansel , Koenig Sven , Nash Alex
Published: October 2022

Video-game designers often tessellate continuous 2-dimensional terrain into a grid of blocked and unblocked square cells. The three main ways to calculate short paths on such a grid are to determine truly shortest paths, shortest vertex paths and shortest grid paths, listed here in decreasing  order of computation time and increasing order of resulting path length. We show that, for both vertex and grid paths on both 4-neighbor and 8-neighbor grids, placing vertices at cell corners rather than at cell centers tends to result in shorter paths. We quantify the advantage of cell corners over cell centers theoretically with tight worst-case bounds on the ratios of path lengths, and empirically on a large set of benchmark test cases. We also quantify the advantage of 8-neighbor grids over 4-neighbor grids.
Map Sketch Generation as a Service
Authors: Liapis Antonios
Published: October 2022

This paper describes the structure of a web service able to generate simple game levels via constrained evolutionary optimization. The provided web service allows users to generate playable game levels without needing to understand the underlying process and without having to allocate computational resources for doing so; combined with the highly expressive and customizable generator, a broad range of levels for different genres and purposes can meet many user needs.
Fiascomatic: A Framework for Automated Fiasco Playsets
Authors: Horswill Ian
Published: October 2022

We present Fiascomatic, a mixed initiative system for generating consistent scenarios for the indie storytelling RPG Fiasco. Players can repeatedly generate scenarios, locking down aspects of a scenario they like and regenerating aspects they don’t, until they arrive at a scenario they find entertaining.  It is not a story generation system; it generates scenarios from which players then generate stories.  Nor is it intended to generate optimal scenarios; it generates random scenarios which the players can then curate according to their taste. Fiascomatic presents an interesting intermediate point between non-automated table-top RPGs and fully automated systems such as story generators or autonomous characters.  It is a tool that can be used by Fiasco players to speed the generation of game setups while preserving creative input on the part of the players, and by Fiasco playset authors to make automated playsets.
Toward Characters Who Observe, Tell, Misremember, and Lie
Authors: Ryan James , Summerville Adam , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

Knowledge and its attendant phenomena are central to human storytelling and to the human experience more generally, but we find very few games that revolve around these concerns. This works to preclude a whole class of narrative experiences in games, and it also damages character believability. In this paper, we present an AI framework that supports gameplay with non-player characters who observe and form knowledge about the world, propagate knowledge to other characters, misremember and forget knowledge, and lie. We outline this framework through the lens of a gameplay experience that is intended to showcase it, called Talk of the Town, which we are currently developing. From a review of earlier projects, we find that our system has a novel combination of features found only independently across other systems, and that it is among the first to support character memory fallibility.
Cinematic, Ambient, Inhabitable Narrative Environments: Story Systems in Search of an Artificial Intelligence Engine
Authors: Wingate Steven
Published: October 2022

Cinematic, Ambient, Inhabitable Narrative Environments (CAINEs) are conceptual AI-driven interactive story systems combining text, audio, and visual imagery that are scalable and adaptable to a wide range of storytelling needs and interactor inputs. Conceived by at artist outside the AI community, they represent an opportunity to use AI in a nontraditional and immersive narrative fashion that relies not on the goal-based arrangement of story elements, but on the accretion and association of those elements in the minds of interactors. This paper represents the initial phase of the project’s development.
Modelling Social Network Interactions in Games
Authors: Cebolledo Gutierrez Edgar Omar , De Troyer Olga
Published: October 2022

Social Networks, like Facebook, have become an important part of life for millions of people, with many people using them on a daily basis. While there are games that simulate life-to-life interactions, there are not as many that simulate social network interactions, and even fewer where social network interactions are an important part of the gameplay. Having models for the simulation of interactions on social networks is the first step needed to be able to create games where a social network is an important component of the gameplay. In this paper we present a model to create dy-namic and believable interactions in social networks and in-dicate how it was used in a serious game about cyberbullying. The model is based on personality models and Berne’s so-called Social Games.
Towards an Accessible Interface for Story World Building
Authors: Poulakos Steven , Kapadia Mubbasir , Schüpfer Andrea , Zünd Fabio , Sumner Robert , Gross Markus
Published: October 2022

In order to use computational intelligence for automated narrative synthesis, domain knowledge of the story world must be defined, a task which is currently confined to experts. This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds. In an effort to democratize story world creation, we present an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis. We demonstrate the potential of our system by authoring a simple bank robbery story world, and integrate it with existing solutions for event-centric planning to synthesize example digital stories.
Designing Story-Centric Games for Player Emotion: A Theoretical Perspective
Authors: Harley Jason , Rowe Jonathan , Lester James , Frasson Claude
Published: October 2022

Narratives are powerful because of their impact on our emotional experiences. Recent years have witnessed significant advances in affective computing and intelligent interaction, presenting a broad range of opportunities for enhancing the design, implementation, and adaptivity of interactive narratives. This paper presents preliminary work examining story-centric games and interactive narratives from the perspective of psychological theories of emotion, with a particular focus on player affect. We examine the sources and duration of player emotion, social facets of emotion, players’ individual differences in emotion, and meta-emotions. Recommendations and future directions for research on player emotion in interactive narratives are discussed.
Aesthetic Interleaving of Character Performance Requests
Authors: Shapiro Daniel , LeBron Larry , Stern Andrew , Mateas Michael
Published: October 2022

We have constructed a system that supports unscripted social interaction between a player and virtual characters, where the participants pursue internal agendas and respond to one another in real-time.  Our emphasis on unscripted interaction means that the characters must accept dynamically generated performance requests, while our concern with social interaction implies that the characters must interleave performances with an attention to natural flow that encourages social engagement. We present initial work on a performance management mechanism that produces this interleaving.   It initiates and suspends character performances by allocating animation resources to requests via a utility function representing aesthetic concerns.  That function weighs extrinsic factors reflecting the purpose of taking an action against intrinsic ones that concern features of a given performance.  We show, via multiple short videos, that the features are individually material to the aesthetic quality of the result and that the mechanism can produce aesthetically pleasing performances on par with the best hand-generated prioritization scheme. We argue, anecdotally, that the parameters of the model are easy to identify, suggesting that the feature vocabulary is both intuitive and useful for shaping character performances.
Mystical Tutor: A Magic: The Gathering Design Assistant via Denoising Sequence-to-Sequence Learning
Authors: Summerville Adam , Mateas Michael
Published: October 2022

Procedural Content Generation (PCG) has seen heavy focus on the generation of levels for video games, aesthetic content, and on rule creation, but has seen little use in other domains. Recently, the ready availability of Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) has seen a rise in text based procedural generation, including card designs for Collectible Card Games (CCGs) like Hearthstone or Magic: The Gathering. In this work we present a mixed-initiative design tool, Mystical Tutor, that allows a user to type in a partial specification for a card and receive a full card design. This is achieved by using sequence-to-sequence learning as a denoising sequence autoencoder, allowing Mystical Tutor to learn how to translate from partial specifications to full.
Predicting Proppian Narrative Functions from Stories in Natural Language
Authors: Valls-Vargas Josep , Zhu Jichen , Ontañón Santiago
Published: October 2022

Computational narrative systems usually require knowledge about the story world and narrative theory to be encoded in some form of structured knowledge representation formalism, a notoriously time-consuming task requiring expertise in both storytelling and knowledge engineering. In this paper we present an approach that combines supervised machine learning with narrative domain knowledge toward automatically extracting such knowledge from natural language stories, focusing specifically on predicting Proppian narrative functions. Our experiments on a dataset of Russian fairy tales show that our system outperforms an informed baseline and that combining top-down narrative theory and bottom-up statistical models inferred from an annotated dataset increases prediction accuracy with respect to using them in isolation.
Rapid Prediction of Player Retention in Free-to-Play Mobile Games
Authors: Drachen Anders , Lundquist Eric , Kung Yungjen , Rao Pranav , Sifa Rafet , Runge Julian , Klabjan Diego
Published: October 2022

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.
Analyzing Stealth Games with Distractions
Authors: Borodovski Alexander , Verbrugge Clark
Published: October 2022

The ability to distract opponents is a key mechanic in many stealth games.  Existing search-based approaches to stealth analysis, however, focus entirely on solving the non-detection problem, for which they rely on static, ahead-of-time models of guard movements that do not depend on player interaction.  In this work we extend and optimize an approach based on heuristic search of stealth games to model variation in guard paths as dynamically triggered by player actions.  Our design is expressive, accommodating different distraction designs, including remote activation and time delays.  Using a Unity3D implementation, we show our enhanced search can solve distraction puzzles found in real games, as well as more complex, multiple-distraction level designs.  Our work shows how heuristic search can be applied to dynamically determined contexts, and significantly extends the ability to model and solve stealth games.
Intelligent Physiotherapy Through Procedural Content Generation
Authors: Esfahlani Shabnam , Thompson Tommy
Published: October 2022

This paper describes an avenue for artificial and computational intelligence techniques applied within games research to be deployed for purposes of physical therapy. We provide an overview of prototypical research focussed on the application of motion sensor input devices and virtual reality equipment for rehabilitation of motor impairment: an issue typical of patients of traumatic brain injuries. We highlight how advances in procedural content generation and player modelling can stimulate development in this area by improving quality of rehabilitation programmes and measuring patient performance.
Predicting Resource Locations in Game Maps Using Deep Convolutional Neural Networks
Authors: Lee Scott , Isaksen Aaron , Holmgård Christoffer , Togelius Julian
Published: October 2022

We describe an application of neural networks to predict the placements of resources in StarCraft II maps. Networks are trained on existing maps taken from databases of maps actively used in online competitions and tested on unseen maps with resources (minerals and vespene gas) removed. This method is potentially useful for AI-assisted game design tools, allowing the suggestion of resource and base placements consonant with implicit StarCraft II design principles for fully or partially sketched heightmaps. By varying the thresholds for the placement of resources, more or fewer resources can be created consistently with the pattern of a single map. We further propose that these networks can be used to help understand the design principles of StarCraft II maps, and by extension other, similar types of game content.
ClueGen: An Exploration of Procedural Storytelling in the Format of Murder Mystery Games
Authors: Stockdale Andrew
Published: October 2022

In this paper I present ClueGen, a murder mystery game that generates its own narrative. I discuss the genre’s suitability for combining gameplay and story using procedural generation techniques, before explaining the implementation and rationale behind ClueGen, utilizing the genre’s common structure as an opportunity to define the scope of the generation requirements. In particular I detail a novel system for telegraphing deceptive characters in procedurally generated games with text-based dialogue, by using audible cues.
Juke Joint: Characters Who Are Moved By Music
Authors: Ryan James , Brothers Tyler , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

We present Juke Joint, a small work of interactive storytelling that demonstrates an extension to the Talk of the Town framework by which characters form thoughts, expressed in natural language, that are elicited by environmental stimuli. Juke Joint takes place in a procedurally generated American small town, in a bar with a haunted jukebox and two patrons facing personal dilemmas; the player is a ghost whose only action is to select which song from the jukebox will play. As the lyrics of the song emanate from the machine, thoughts are elicited in the minds of the patrons, constituting streams of consciousness that may eventually lead them to resolutions of their respective dilemmas. In this paper, we outline the game and also the AI architecture that makes it possible; the latter combines a light simulation of stimulus processing with a novel approach to natural language generation.
Play With Me? Understanding and Measuring the Social Aspect of Casual Gaming
Authors: Alsén Adam , Runge Julian , Drachen Anders , Klapper Daniel
Published: October 2022

Social Gaming is a pervasive phenomenon, driven by the advent of social networks and the digitization of game dis-tribution. This paper positions and defines Casual Social Games (CSGs) as a genre and platform agnostic subset of Social Games that incorporates browser, mobile, console and wearable digital games. The authors argue that – as CSGs impact the games industry, shape play patterns and audience characteristics, and proliferate to new platforms – understanding and measuring their social aspect becomes highly relevant. A randomized experiment on added social gameplay in a CSG on both mobile and Facebook serves to support this argument. Experimental results highlight that social gameplay is extremely important for engagement and monetization in casual games, even more so on mobile plat-forms. This does not only suggest that CSG developers will benefit from focusing on increased social interaction in their games, but that Game Analytics should strive to unify defi-nitions and build a common body of knowledge around the social aspect of casual gaming.
Character Focused Narrative Models for Computational Storytelling
Authors: Berov Leonid
Published: October 2022

My thesis aims at conceptualizing and implementing a computational model of narrative generation that is informed by narratological theory as well as cognitive multi-agent simulation models. It approaches this problem by taking a mimetic stance towards fictional characters and investigates how narrative phenomena related to characters can be computationally recreated from a deep character model grounded in multi agent systems. Based on such a conceptualization of narrative it explores how the generation of plot can be controlled, and how the quality of the resulting plot can be evaluated, in dependence of fictional characters. By that it contributes to research on computational creativity by implementing an evaluative storytelling system, and to narratology by proposing a generative narrative theory based on several post-structuralist descriptive theories.
Intentional Agents for Epistemic Games
Authors: Eger Markus
Published: October 2022

When humans observe other agents, one key aspect of that agent's behavior that they expect is intentionality, i.e. that the agent is working towards some goal and committed to achieving it. It is therefore desirable to develop agents that exhibit such behavior when they are supposed to interact with humans, especially if communication between the agent and the human is involved. However, as Cohen and Levesque have noted, intention can not be viewed in a vacuum, because it is tightly linked with an agent's beliefs about the world, and that effect is magnified when communication is involved. For my thesis, I am planning an agent framework that exhibits intentional behavior by modeling the agent's beliefs about the world and other agents' beliefs, in a theory of mind. My work not only focuses on having agents act with intentions, but also how they can communicate these intentions to other agents, and even deceive other agents by communicating intentions that they don't actually hold. Therefore, I will be focusing on what I call epistemic games, which are (turn-based) games in which the acquisition and exchange of knowledge is an intrinsic part of game play.
Wevva: Democratising Game Design
Authors: Powley Edward , Nelson Mark , Gaudl Swen , Colton Simon , Pérez Ferrer Blanca , Saunders Rob , Ivey Peter , Cook Michael
Published: October 2022

Fluidic games blur the line between gameplay and game design, providing playful, non-technical, easy-to-use tools for users to design their own games. We introduce Wevva, our first fluidic game for iOS, which allows users to design and share their own games based on novel mechanics, entirely on a mobile device, with no need for programming skills. The development of such software is a significant challenge in user interface design, requiring a new design approach compared to the design of more traditional creative tools, and with much scope for mixed-initiative co-creation and other assistive AI technologies. Our aim with fluidic games is to democratise game design, allowing anyone and everyone to express themselves through the artform of digital games.
Generative Design for Textiles: Opportunities and Challenges for Entertainment AI
Authors: Smith Gillian
Published: October 2022

This paper reports on two generative systems that work in the domain of textiles: the Hoopla system that generates patterns for embroidery samplers, and the Foundry system that creates foundation paper piecing patterns for quilts. Generated patterns are enacted and interpreted by the human who stitches the final product, following a long and laborious, yet entertaining and leisurely, process of stitching and sewing. The blending of digital and physical spaces, the tension between machine and human authorship, and the juxtaposition of stereotypically masculine computing with highly feminine textile crafts, leads to the opportunity for new kinds of tools, experiences, and artworks. This paper argues for the values of textiles as a domain for generative methods research, and discusses generalizable research problems that are highlighted through operating in this new domain.
Influencing User Choices in Interactive Narratives Using Indexter’s Pairwise Event Salience Hypothesis
Authors: Farrell Rachelyn , Ware Stephen
Published: October 2022

Indexter is a plan-based model of narrative that incorporates cognitive scientific theories about the salience — or prominence in memory — of narrative events. A pair of Indexter events can share up to five indices with one another: protagonist, time, space, causality, and intentionality. The pairwise event salience hypothesis states that a past event is more salient if it shares one or more of these indices with the most recently narrated event. In a previous study we used this model to predict users’ choices in an interactive story based on the indices of prior events. We now show that we can use the same method to influence them to make certain choices. In this study, participants read an interactive story with two possible endings. We influenced them to choose a particular ending by manipulating the salience of story events. We showed that users significantly favored the targeted ending.
Multimodal Goal Recognition in Open-World Digital Games
Authors: Min Wookhee , Mott Bradford , Rowe Jonathan , Taylor Robert , Wiebe Eric , Boyer Kristy , Lester James
Published: October 2022

Recent years have seen a growing interest in player modeling to create player-adaptive digital games. As a core player-modeling task, goal recognition aims to recognize players’ latent, high-level intentions in a non-invasive fashion to deliver goal-driven, tailored game experiences. This paper reports on an investigation of multimodal data streams that provide rich evidence about players’ goals. Two data streams, game event traces and player gaze traces, are utilized to devise goal recognition models from a corpus collected from an open-world serious game for science education. Empirical evaluations of 140 players’ trace data suggest that multimodal LSTM-based goal recognition models outperform competitive baselines, including unimodal LSTMs as well as multimodal and unimodal CRFs, with respect to predictive accuracy and early prediction. The results demonstrate that player gaze traces have the potential to significantly enhance goal recognition models’ performance.
Playable Experiences at AIIDE 2017
Authors: Treanor Mike , Warren Nicholas , Reed Mason , Smith Adam , Ortiz Pablo , Coney Laurel , Sherman Loren , Carré Elizabeth , Vivatvisha Nadya , Harrell D. , Mardo Paola , Gordon Andrew , Dormans Joris , Robison Barrie , Gomez Spencer , Heck Samantha , Wright Landon , Soule Terence
Published: October 2022

This paper describes the accepted entries to the fifth Playable Experiences track to be held at the AIIDE conference. The Playable Experiences track showcases complete works that make use of artificial intelligence techniques as an integral part of the player experience.
Robustness of Real-Time Heuristic Search Algorithms to Read/Write Error in Externally Stored Heuristics
Authors: Abdi Oskouie Mina , Bulitko Vadim
Published: October 2022

Real-time heuristic search algorithms follow the agent-centered search paradigm wherein the agent has access only to information local to the agent’s current position in the environment. This allows agents with constant-bounded computational faculties (e.g., memory) to take on search problems of progressively increasing sizes. As the agent’s memory does not scale with the size of the search problem, the heuristic must necessarily be stored externally, in the environment. Storing the heuristic in the environment brings the extra challenge of read/write errors. In video games, introducing error artificially to the heuristics can make the non-player characters (NPC) behave more naturally. In this paper, we evaluate effects of such errors on real-time heuristic search algorithms. In particular, we empirically study the effects of heuristic read redundancy on algorithm performance and compare its effects to the existing technique of using weights in heuristic learning. Finally, we evaluate a recently proposed technique of correcting the heuristic with a one-step error term in the presence of read/write error.
Towards Adaptability of Demonstration-Based Training of NPC Behavior
Authors: Drake John , Safonova Alla , Likhachev Maxim
Published: October 2022

Using demonstration to guide behavior generation for non-player characters (NPCs) is a challenging problem. Particularly, as new situations are encountered, demonstration records often do not closely correspond with the task at hand. Open-world games such as The Elder Scrolls V: Skyrim or Borderlands often reuse locations within the game world for multiple quests. In each new quest at each location, the particular configuration of game elements such as health packs, weapons, and enemies changes. In this paper, we present an approach that utilizes user demonstrations for generating NPC behaviors while accommodating such variations in the game configuration across quests.
An Empirical Evaluation of a Generative Method for the Expression of Personality Traits through Action Choice
Authors: Bahamon Julio , Young R.
Published: October 2022

The presence of interesting and compelling characters is an essential component of effective narrative. Well-developed characters have features that enable them to significantly enhance the believability and quality of a story. In this paper, we describe the results of an experiment to evaluate a planning-based narrative generation system that focuses on the generation of stories that express character. The system is designed to automatically produce narratives that show character personality traits through the choices characters make when selecting the means by which they achieve their goals. Results from our study support the hypothesis that an audience presented with stories generated by Mask will attribute personality traits to the story characters that have significant correlation with the computational model of personality used to drive the characters' choices.
Dynamic Epistemic Logic in Game Design
Authors: Torres Javier
Published: October 2022

Dynamic Epistemic Logics are a set of multimodal logics that deal with knowledge and change. We argue that the theory for this formalism is mature enough to start a practical implementation, while at the same time having a sizable amount of theoretical expansions. We also claim that these two factors make it an attractive formalism for new features in games featuring an agent’s internal model, like planning for gathering information, acting without revealing one’s goals or feeding possibly false information to a set of agents to influence their beliefs, augmenting already existing mechanisms.
Designing Stronger AI Personalities
Authors: Short Tanya
Published: October 2022

In this invited industry case study, Tanya X.Short introduces eight mechanisms that designers can utilize to better harness procedural character personalities in games. Tanya is co-founder and captain of Kitfox Games, a Montreal-based independent game studio, and a veteran developer and designer known for her expertise in procedural generation and systems-driven game design. With Tarn Adams, she edited the 2017 volume Procedural Generation in Game Design. As Tanya explains in this paper, there is an emerging pattern in game design that utilizes character personality as a central gameplay system. Using a number of examples spanning her experiences as both developer and player, in this paper Tanya distills her collected design knowledgeinto an actionable recipe for building better character personality systems.
A Sandbox for Modeling Social AI
Authors: Robison Ethan
Published: October 2022

We present a system for quickly prototyping AI code for modeling social interaction among the simulated residents of a skyscraper. The system is built on top of a commercially released indie game but replaces its character AI with a general-purpose logic programming language. These two features together simplify the more tedious parts of creating prototype AI code from scratch, enabling more effort to be focused on the meatier parts of research.
Character Beliefs in Story Generation
Authors: Eger Markus , Martens Chris
Published: October 2022

Character beliefs play a central role in many narratives, but are often not represented in planning-based author-centered narrative generation systems, or only represented in an ad hoc way. In this paper we will discuss how actions in Dynamic Epistemic Logic that affect the story world as well as the characters' beliefs, can be used in the context of narrative generation. By using this logical foundation we can ensure that the actors' beliefs are maintained in a logically consistent way, but we will also discuss how our system supports writing these complex logical operations in a concise way. We will also show how our system, which has limited planning capabilities, can come up with simple stories that cause characters to have beliefs desired by the author, and how our approach can be integrated with other work in the field.
Towards Generating Narratives for the Real World
Authors: Azad Sasha
Published: October 2022

An immersive, interactive environment and the non-player characters populating it often play a key role in interactive narrative experiences. We posit that if a procedurally generated narrative is better able to reflect real-world attributes of the player's surroundings, then the experience would be more transportive for the player. With this comes the problem of generating believable narratives and characters for an open, complex, real world. Simulating such a society within the constraints of the real environment, and allowing for virtual characters to more accurately mimic human behavior could increase the believability of the agents. The interactions amongst these agents sharing their cultural views, biases, and histories based on their real-world geolocation could inform the study of audience modeling and machine enculturation, allowing computers to learn or reason about social norms in regions. Finally, we posit this research would afford better applications in the field of entertainment or computational social science.
Modular Architecture for StarCraft II with Deep Reinforcement Learning
Authors: Lee Dennis , Tang Haoran , Zhang Jeffrey , Xu Huazhe , Darrell Trevor , Abbeel Pieter
Published: October 2022

We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as buildorder selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We present the first result of applying deep reinforcement learning techniques to training a modular agent with selfplay, achieving 92% or 86% win rates against the ”Harder” (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Engaging Turn-Based Combat in the Children of the Galaxy Videogame
Authors: Šmejkal Pavel , Gemrot Jakub
Published: October 2022

In this paper we tackle a problem of tile-based combat in the turn-based strategy (space 4X) video game Children of the Galaxy (CotG). We propose an improved version of Monte Carlo tree search (MCTS) called MCTS considering hit points (MCTS_HP). We show MCTS_HP is superior to Portfolio greedy search (PGS), MCTS and NOKAV reactive agent in small to medium combat scenarios. MCTS_HP performance is shown to be stable when compared to PGS, while it is also more time-efficient than regular MCTS. In smaller scenarios, the performance of MCTS_HP with 100 millisecond time limit is comparable to MCTS with 2 seconds time limit. This fact is crucial for CotG as the combat outcome assessment is precursor to many strategical decisions in CotG game. Finally, if we fix the amount of search time given to the combat agent, we show that different techniques dominate different scales of combat situations. As the result, if search-based techniques are to be deployed in commercial products, a combat agent will need to be implemented with portfolio of techniques it can choose from given the complexity of situation it is dealing with to smooth gameplay experience for human players.
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Authors: Kartal Bilal , Hernandez-Leal Pablo , Taylor Matthew E.
Published: October 2022

Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency. In this paper, we contribute a novel self-supervised auxiliary task, i.e., Terminal Prediction (TP), estimating temporal closeness to terminal states for episodic tasks. The intuition is to help representation learning by letting the agent predict how close it is to a terminal state, while learning its control policy. Although TP could be integrated with multiple algorithms, this paper focuses on Asynchronous Advantage Actor-Critic (A3C) and demonstrating the advantages of A3C-TP. Our extensive evaluation includes: a set of Atari games, the BipedalWalker domain, and a mini version of the recently proposed multi-agent Pommerman game. Our results on Atari games and the BipedalWalker domain suggest that A3C-TP outperforms standard A3C in most of the tested domains and in others it has similar performance. In Pommerman, our proposed method provides significant improvement both in learning efficiency and converging to better policies against different opponents.
Towards Usable Level PCG
Authors: Lang Eric
Published: October 2022

My proposed dissertation work attempts to make procedural content generation (PCG) for game levels easier to use and more expressive for designers. This can be split into three rough layers: improving the expressive range of a dungeon PCG genetic algorithm by using multiobjective optimization, providing a more intuitive way for designers to create fitness variables, and letting them train the PCG system to give them the output they expect. While this work will focus on PCG algorithms for generating game levels, ideally much of the evaluation concept in this paper will be applicable to other forms of PCG. I will hopefully also be able to show that having the designer interact with an expressive PCG system as their ”apprentice” is effective.
TwitchChat: A Dataset for Exploring Livestream Chat
Authors: Ringer Charles , Nicolaou Mihalis , Walker James
Published: October 2022

Most natural language processing research focuses on modelling and understanding text formed of complete sentences with correct spelling and grammar. However, livestream chat is drastically different. Viewers are typically writing short messages while responding to in-stream events, often with incorrect grammar and many repeated tokens. Additionally, tokens that are commonly used in livestream chat are unknown to traditional language understanding efforts that focus on prosaic text. To advance and encourage further research in terms of livestream chat understanding, in this work, we present a large-scale dataset of video game livestream chat, consisting of over 60 million tokens. As livestreaming becomes more popular it is also increasingly pertinent to study, though chat analysis, the way in which the audience is engaging with the stream. However, this is not a straightforward task, livestream chat is a rich and complex domain, far removed from often studied prosaic text. Additionally. we provide a case study analysis of word vector methods applied to the dataset, showing that the vector space is strangely shaped but clusterable and that the resulting clusters correlate with features such as streamer popularity. Furthermore, human relatedness tests highlight the difference that this domain poses with respect to prosaic text. It is hoped the livestream chat dataset, the discussion of its unique features, and the challenges highlighted for future work will invigorate the research community into further study of livestream chat.
Geometry of Hiding: Generating Visibility Manifolds
Authors: Koretski Adrian , Clark Clark
Published: October 2022

Understanding what a character observes in a game as they move through the space is useful for game analysis and design. In this work we describe a tool that generates visibility manifolds given a game terrain and game agent path. Our tool accommodates multiple kinds of game agent motion and considers the impact of visual occlusion in order to efficiently generate a closed 3D mesh representation of the area seen over time. The resulting shape demonstrates unintuitive properties of game agent observations, and an efficient Unity implementation allows the constructed shape to be used in interactive game design.
Principles for AI Co-Creative Game Design Assistants
Authors: Elton-Pym Alex
Published: October 2022

While human-AI co-creative game design assistants are a relatively new technology, clear paradigms for their design are emerging. Our research aims to investigate the AI techniques, heuristics, and design conventions that are key to effective co-creative game design assistants. We propose that now, during their infancy, a series of principles for the formulation of these systems is a timely contribution to the literature.
Personalized Procedural Content Generation for Increased Player Agency
Authors: Yu Kristen
Published: October 2022

My interest is in the intersection between AI directors, player modeling, and procedural content generation. Using a combination of these tools, I believe that we can expand the space of tellable stories to create a story that is completely driven by the player’s actions in the game. There are two use cases for this work. The first is adding additional content to an open-world game by letting the player influence the world after the main story is completed, which would help address the problem of replayability. The second is to allow the player to create a completely personalized story from the beginning of the game, using the starting situation of the game as if it was the start of a simulation.