bluuebunny/crossref_metadata_2025 huggingface dataset:
Game Design for Classical AI
Authors: Horswill Ian
Published: October 2022

Reasoning using expressive symbolic representations is a central theme of AI research, yet there are surprisingly few deployed games, even within the AIIDE research community, that use this sort of “classical” AI. This is partly due to practical and methodological issues, but also due to fundamental mismatches between current game genres and classical AI systems. I will argue that if we want to build games that leverage high-end classical AI techniques like commonsense reasoning and natural language processing, we will also have to develop new game genres and mechanics that better exploit those capabilities. I will also present a design sketch of a game that explores potential game mechanics for classical AI.
Domain-Specific Sentiment Classification for Games-Related Tweets
Authors: Sarratt Trevor , Morgens Soja-Marie , Jhala Arnav
Published: October 2022

Sentiment classification provides information about the author's feeling toward a topic through the use of expressive words. However, words indicative of a particular sentiment class can be domain-specific. We train a text classifier for Twitter data related to games using labels inferred from emoticons. Our classifier is able to differentiate between positive and negative sentiment tweets labeled by emoticons with 75.1% accuracy. Additionally, we test the classifier on human-labeled examples with the additional case of neutral or ambiguous sentiment. Finally, we have made the data available to the community for further use and analysis.
Artik: Articulation and Dynamics from Structural Pattern Analysis
Authors: Handelman Eliot , Sigler Andie
Published: October 2022

Automatic arrangement is the problem of transforming an input score into a full orchestration, e.g. realized with samples. A subproblem, on which arrangement depends, is to establish the dynamic levels of individual parts, their articulations, and how parts are to align. A new kind of structural analysis is described: the interpretation of music means the delineation of existing structure, so that the performance synchronizes with the music. These ideas are realized in a system that creates fully articulated string orchestra renditions of concerti by Bach and Vivaldi. While still at an early stage, the system demonstrates how structural analysis can be deployed to solve complex musical tasks. We conclude with some speculations about the future of musical practices as suggested by this research.
Investigating Collaborative Creativity via Machine-Mediated Game Blending
Authors: Lopes Phil , Yannakakis Georgios
Published: October 2022

Can the creativity of humans be enhanced through mutual cooperation, or is it a detriment to their own individual creativity? Although most artists are known for their artistic individuality, some of the best creative works were achieved through mutual collaborative efforts. This paper proposes the study of a game blending system capable of combining user- and machine-generated content from multiple users and creativity facets (e.g. audio, visuals, narrative) for the creation of complete games. Supported by mixed-initiative design tools and human computation (crowdsourcing), users create facet-specific content, while getting stimulated by other creations on different facets by other users. Our research will investigate the ability for machine input into the collaborative process to yield games of higher novelty and quality for players.
Leveraging Communication for Player Modeling and Cooperative Play
Authors: Sarratt Trevor
Published: October 2022

Collaboration between agents and players within games is a ripe area for exploration. As with adversarial AI, collaborative agents are challenged to accurately model players and adapt their behavior accordingly. The task of cooperation, however, allows for communication between teammates that can prove beneficial in coordinating joint actions and plans. Furthermore, we propose extending established multi-agent communication paradigms to include transfer of information pertinent to player models. By querying goal and preference information from a player, an agent can reduce uncertainty in coordination domains, allowing for more effective planning. We discuss the challenges as well as the planned development and evaluation of the system.
Automated Decomposition of Game Maps
Authors: Halldórsson Kári , Björnsson Yngvi
Published: October 2022

Video game worlds are getting increasingly large and complex. This poses challenges to the game AI for both pathfinding and strategic decisions, not least in real-time strategy games. One way to alleviate the problem is to manually pre-label the game maps with information about regions and critical choke points, which the game AI can then take advantage of. We present a method for automatically decomposing game maps into non-uniform sized regions. The method uses a flooding algorithm at its core and has the benefit, in addition to its effectiveness, to be relatively intuitive both conceptually and in implementing. Empirical evaluation on game maps shows that the automatic decomposition results in intuitive regions of a comparable standard to human-made labeling. Furthermore, we show that our automatic decomposition, when used by a pathfinding algorithm capable of taking advantage of pre-labeled regions, significantly improves search effectiveness.
Tuning Belief Revision for Coordination with Inconsistent Teammates
Authors: Sarratt Trevor , Jhala Arnav
Published: October 2022

Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.
Multi-Level Evolution of Shooter Levels
Authors: Cachia William , Liapis Antonios , Yannakakis Georgios
Published: October 2022

This paper introduces a search-based generative process for first person shooter levels. Genetic algorithms evolve the level's architecture and the placement of powerups and player spawnpoints, generating levels with one floor or two floors. The evaluation of generated levels combines metrics collected from simulations of artificial agents competing in the level and theory-based heuristics targeting general level design patterns. Both simulation-based and theory-driven evaluations target player balance and exploration, while resulting levels emergently exhibit several popular design patters of shooter levels.
A Data-Driven Approach for Computationally Modeling Players' Avatar Customization Behaviors
Authors: Lim Chong-U , Harrell D. Fox
Published: October 2022

Avatar customization systems enable players to represent themselves virtually in many ways. Research has shown that players exhibit different preferences and motivations in how they customize their avatars. In this paper, we present a data-driven analytical approach to modeling player behavioral patterns exhibited during the avatar customization process. We used our data mining tool \textit{AIRvatar} to analyze telemetry data obtained from 190 players using an avatar creator of our own design. Using non-negative matrix factorization (NMF) and N-gram models, we demonstrate how our approach computationally models behavioral patterns exhibited by players such as "regular shopping," "engaged shopping," or "bored browsing". Our models obtained significant effect sizes (0.12 <= R^2 <= 0.54) when validated with multiple linear regressions for players' time spent engaging in activities within the avatar creator. The NMF model had comparably high performance and ease of interpretation compared to control models.
A Benchmark for StarCraft Intelligent Agents
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

The problem of comparing the performance of different Real-Time Strategy (RTS) Intelligent Agents (IA) is non-trivial. And often different research groups employ different testing methodologies designed to test specific aspects of the agents. However, the lack of a standard process to evaluate and compare different methods in the same context makes progress assessment difficult. In order to address this problem, this paper presents a set of benchmark scenarios and metrics aimed at evaluating the performance of different techniques or agents for the RTS game StarCraft. We used these scenarios to compare the performance of a collection of bots participating in recent StarCraft AI (Artificial Intelligence) competitions to illustrate the usefulness of our proposed benchmarks.
Would You Look At That! Vision-Driven Procedural Level Design
Authors: Cook Michael
Published: October 2022

In this paper we present a technique for procedurally generating sections of 3D level geometry using computational evolution and guided by the visibility of certain game objects or areas during play. We show that certain level design goals can be achieved in the resulting levels, such as encouraging or dissuading player sightings of certain objects or locations. We also give details of a simple study of players on the generated levels, and discuss how this might be expanded to incorporate more complex problems related to level design.
The Marginal: A Game for Modeling Players' Perceptions of Gradient Membership in Avatar Categories
Authors: Lim Chong-U , Harrell D. Fox
Published: October 2022

We encounter the results of category formation every day, from demographic categories like race and gender, to role-playing-game classes like "fighter" or "mage". Category membership is often not simply based on the possession of discrete properties but instead constructed from and reflect the highly nuanced relationships (gradience) between members and best-example individuals called "prototypes". In this paper, we present The Marginal, an artificial intelligence (AI)-driven game that (1) computationally models the cognitive categories that players develop when customizing videogame avatars and (2) generates challenges for players to use their perception of visual, textual, and numerical data to progress in a game created using these models. We use archetypal analysis, an AI clustering approach for identifying boundary points in data, to generate tasks in The Marginal for its gameplay. It shows how AI can be combined with games to model and evaluate cognitive  categorization phenomena.
Social Play in Non-Player Character Dialog
Authors: Treanor Mike , McCoy Josh , Sullivan Anne
Published: October 2022

Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.
Modeling Leadership Behavior of Players in Virtual Worlds
Authors: Shaikh Samira , Strzalkowski Tomek , Stromer-Galley Jennifer , Broadwell George , Liu Ting , Mikeal Martey Rosa
Published: October 2022

In this article, we describe our method of modeling sociolinguistic behaviors of players in massively multi-player online games. The focus of this paper is leadership, as it is manifested by the participants engaged in discussion, and the automated modeling of this complex behavior in virtual worlds. We first approach the research question of modeling from a social science perspective, and ground our models in theories from human communication literature. We then adapt a two-tiered algorithmic model that derives certain mid-level sociolinguistic behaviors--such as Task Control, Topic Control and Disagreement from discourse linguistic indicators--and combines these in a weighted model to reveal the complex role of Leadership. The algorithm is evaluated by comparing its prediction of leaders against ground truth – the participants’ own ratings of leadership of themselves and their conversation peers. We find the algorithm performance to be considerably better than baseline.
Comparing Player Skill, Game Variants, and Learning Rates Using Survival Analysis
Authors: Isaksen Aaron , Nealen Andy
Published: October 2022

Game designers can use computer-aided game design methods to quantitatively compare player skill levels, different game variants, and learning rates, for the purpose of modeling how players will likely experience a game. We use Monte-Carlo simulation, hazard functions, and survival analysis to show how difficulty will quantitatively change throughout a game level as we vary skill, game parameters, and learning rates. We give a mathematical overview of survival analysis, present empirical data analyses of our player models for each game variant, and provide theoretical probability distributions for each game. This analysis shows the quantitative reasons why balancing a game for a wide range of player skill can be difficult; our player modeling provides tools for tuning this game balance. We also analyze the score distribution of over 175 million play sessions of a popular online Flappy Bird variant to demonstrate how learning effects can impact scores, implying that learning is crucial aspect of player modeling.
A Factor-Based Exploration of Player's Continuation Desire in Free-to-Play Mobile Games
Authors: Stankevicius Deividas , Jady Hawraa Amira , Drachen Anders , Schoenau-Fog Henrik
Published: October 2022

This paper explores the concept of Continuation Desire further by investigating the behavioral intent of players’ desire to keep playing. User experience is a complex, multifaceted topic, which is commonly studied through different aspects namely engagement, continuation desire, immersion, flow experience, motivation and enjoyment — yet it is difficult to measure. These concepts were conceptualized into different factors and thereby it was identified which of them are related. This resulted in a synthesized model that was based on the Theory of Planned Behavior model. This model takes into account the perceived user experience factors relevant for Continuation Desire and then attempts to predict players’ intention to continue playing. Structural Equation Modeling analysis was performed to validate the model and to predict the intention of continuation desire. At the same time, exploring why people continue playing, based on experiments using Candy Crush Saga, one of the most popular Free-to-Play mobile games worldwide. The findings indicate that motivation is an important factor of Continuation Desire in Free-to-Play mobile games, with engagement, enjoyment and flow being less important. This paper contributes an early work of a factor-based exploration of measuring user experience and their continuation desire.
PlotShot: Generating Discourse-Constrained Stories Around Photos
Authors: Cardona-Rivera Rogelio , Li Boyang
Published: October 2022

Story generators typically adopt a pipelined model of generation wherein fabula structure is decided independently and prior to discourse structure. In this paper, we propose a novel story generator, PlotShot, capable of reasoning over discourse materials during fabula generation such that these materials meaningfully constrain the development of a causally and intentionally coherent story. PlotShot incorporates user-supplied photographs as optional story states through an oversubscription planning paradigm. Further, to leverage existing work on planning-based models of generation, we present a technique to compile the photo story planning problem to classical narrative planning. Our system attempts to maximize quality of an illustrated story by analyzing the affinity between a photo and the action it is meant to depict. An evaluation of generated artifacts shows advantage over heuristic baseline techniques.
Co-Creative Drawing Agent with Object Recognition
Authors: Davis Nicholas , Hsiao Chih-Pin , Singh Kunwar Yashraj , Magerko Brian
Published: October 2022

This paper describes an updated version of a co-creative drawing system called the Drawing Apprentice. The system collaborates with users by analyzing their drawn input and responding in a real time dialogical and improvisational interaction. The current system includes an object recognition module that employs deep learning to classify sketched objects. The system architecture and implementation are described along with its evaluation during a public demonstration during which artists, non-artists, and designers provided feedback about the experience interacting with the system.
Demonstration-Based Training of Non-Player Character Tactical Behaviors
Authors: Drake John , Safonova Alla , Likhachev Maxim
Published: October 2022

State of the art methods for generating non-player character (NPC) tactical behaviors typically depend on hard-coding actions or minimizing a given objective function. In many games however, it is hard to foresee how the NPC should behave to appear intelligent or to accommodate human player preferences for NPC tactics. In this paper we consider an alternative approach, by training NPC tactical behavior via demonstrations. We propose a heuristic search-based planning method based on previously-developed Experience Graphs, which facilitates the use of behavior demonstration data to plan goal-oriented NPC behavior. Our method provides a principled solution to the problem which tolerates some amount of differences in between the training demonstration and the actual problem and yet still grants guarantees on the quality of the solution output.
A Computational Model Based on Symmetry for Generating Visually Pleasing Maps of Platform Games
Authors: Mariño Julian , Lelis Levi
Published: October 2022

In this paper we introduce a computational model based on the concept of symmetry to generate visually pleasing maps of platform games. We cast the problem of generating symmetrical maps as an optimization task and propose a heuristic search algorithm to solve it. A user study using a platform game shows the advantage of our method over other approaches in terms of visual aesthetics and enjoyment. Another user study shows that our method is able to generate maps as visually pleasing as maps created by professional designers.
Implementation of an Automated Fire Support Planner
Authors: Harder Byron , Balogh Imre , Darken Chris
Published: October 2022

Although the employment of fire support is a staple of modern military doctrine, today's constructive combat simulations depend on meticulous human input to generate any appropriate fire support plans. This status quo can be improved through AI techniques. We implement models of tactical risk, reduction of risk, and suppression effects in a representative combat simulation, as well as a greedy fire support planning algorithm that leverages these concepts. The algorithm is theoretically non-optimal, but testing shows that the resulting fire support plans are effective at improving simulated combat results and have some realistic emergent properties. The practical running time of the planner is less than 20 seconds for a company-sized unit, including navigation graph setup. The planner's best-first approach scales naturally in more time-constrained environments.
Procedural Generation of Levels for Angry Birds Style Physics Games
Authors: Stephenson Matthew , Renz Jochen
Published: October 2022

This paper presents a procedural generation algorithm for levels in physics-based puzzle games similar to Angry Birds. The proposed algorithm creates levels consisting of various self-contained structures placed throughout a 2D area. Each structure can be placed either on the ground or atop floating platforms within the available level space. These structures are created using a variety of different block types and do not require predefined substructures or composite elements. Target object locations are determined based on a combination of factors, including structural protection, occupancy estimation and overall dispersion. Experiments were performed in order to determine the ideal input parameters for generating desirable levels. The expressivity of the generator was also evaluated and the results show that the proposed method can generate a wide variety of interesting levels.
Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search
Authors: Soo Von-Wun , Lee Chi-Mou , Chen Tai-Hsun
Published: October 2022

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.
Characters Who Speak Their Minds: Dialogue Generation in Talk of the Town
Authors: Ryan James , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town. Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols — the conventional way of generating from a CFG — we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.
Proceduralist Readings, Procedurally
Authors: Martens Chris , Summerville Adam , Mateas Michael , Osborn Joseph , Harmon Sarah , Wardrip-Fruin Noah , Jhala Arnav
Published: October 2022

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.
Draft-Analysis of the Ancients: Predicting Draft Picks in DotA 2 using Machine Learning
Authors: Summerville Adam , Cook Michael , Steenhuisen Ben
Published: October 2022

Analysing strategic decision-making in eSports is an increasingly important problem -- for players, for teams, for commentators, for viewers and for broadcasters. Such analysis is extremely difficult, however, because of the comparatively small quantities of data, the ever-shifting state of competitive play, and the huge complexity of the game. In this paper we describe a system for predicting drafting decisions in DOTA 2, and evaluate both how the system performs compared to human experts, as well as the new kinds of analysis made possible by automation.
Computatrum Personae: Toward a Role-Based Taxonomy of (Computationally Assisted) Performance
Authors: Samuel Ben , Ryan James , Summerville Adam , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

Computationally assisted performance is a burgeoning area for AI applications, and an important stepping stone toward the dream of generative and personalized narrative experiences. As more pieces of computationally assisted performance are developed, it will become ever more important to develop a vocabulary with which to describe them. Inspired by previous work in creating taxonomies for other related domains, this paper outlines a taxonomy for performance-based experiences, drawn from digital games, traditional theatre, and the hybrid of the two. Having such a taxonomy not only creates a common language with which to discuss such experiences, but reveals unexplored design space in the field, and the particular applications of artificial intelligence necessary to realize them.
What Does Bach Have in Common with World 1-1: Automatic Platformer Gestalt Analysis
Authors: Pagnutti Johnathan
Published: October 2022

Platformer level generation has often used a beat metaphor to relate to how players interact with level geometry. However, this conceptualization of beats is different from the musical concept of `beat', limiting the utility of theories and tools developed in music analysis for platformer levels. A gameplay gestalt, a pattern of interaction that the player enacts or performs in order to make progress in a game, may fit the beat metaphor. By taking a very similar lens and viewing players playing platformer levels as enacting a series of gameplay gestalts through time, gestalt music analysis (GMA) does fit into the platformer domain. This paper details work on transforming a GMA model to work with the Platformer Experience Dataset (PED), and some promising first results of the transformed model.
Presenting Believable Choices
Authors: Robertson Justus , Young R. Michael
Published: October 2022

Interactive narratives (IN) are stories that branch and change based on the actions of a participant. A class of automated systems generate INs where all story branches conform to a set of constraints predefined by an author. Participants in these systems may create invalid branches by navigating the story world outside the bounds of the author's constraints. Two existing methods, choice removal and intervention, are designed to mitigate these situations. However, these methods are expected to lower invisibility, being recognized as system manipulations by the participant. In this paper we present an evaluation of a new method, domain revision, that is designed to have no negative effect on invisibility. We measure invisibility by asking survey participants how believable a choice's options and outcomes are in the context of a Choose Your Own Adventure story. We find that domain revision is more believable than choice removal when applied to a choice's options. We also find that domain revision is equally believable as intervention on a choice's outcomes because intervention does not cause a drop in invisibility.
Dream Machine: A Creative and Autonomous Machine for Creating Stories and Worlds
Authors: Escarce Junior Mário
Published: October 2022

Video games presents an immense dissonance between its narrative and interactive sections. These two elements are commonly presented in a format where each occurs at a certain time, but rarely simultaneously. Besides, other elements, such as the background music, are also usually pre-defined, denying for the users the condition to establish new forms of creative expression within the system. This work, therefore, intends to propose new systems and algorithms to fit this paradigm.
Algorithm Selection in Zero-Sum Computer Games
Authors: Tavares Anderson
Published: October 2022

Competitive computer games are challenging domains for artificial intelligence techniques. In such games, human players often resort to strategies, or game-playing policies, to guide their low-level actions. In this research, we propose a computational version of this behavior, by modeling game playing as an algorithm selection problem: agents must map game states to algorithms to maximize their performance. By reasoning over algorithms instead of low-level actions, we reduce the complexity of decision making in computer games. With further simplifications on the state space of a game, we were able to discuss game-theoretic concepts over aspects of real-time strategy games, as well as generating a game-playing agent that successfully learns how to select algorithms in AI tournaments. We plan to further extend the approach to handle incomplete-information settings, where we do not know the possible behaviors of the opponent.
A Skill-Based Framework for the Generation and Presentation of Educational Videogame Content
Authors: Horn Britton
Published: October 2022

We regularly encounter complex activities consisting of basic skills— both conscious and subconscious. Adequately performing these complex activities involves mastering the individual basic skills and having the ability to seamlessly integrate them together. Games are one such example of a complex activity that is difficult to break down into the basic skills required, but engagement in games relies on designers introducing challenges proportionate to a player's skill. Procedurally generated levels cause additional problems since it is hard to estimate level difficulty for a particular player. This proposal suggests a framework for determining the skills necessary to successfully complete a game, creating AI-based bots with those skills to reflect players with the same skills, and identifying and generating optimal orderings of levels to promote learning each skill of a game. The proposed framework will be implemented in three citizen science games—Paradox, Foldit, and Nanocrafter — and one computer science educational game called GrACE.
Cooperative Creativity Support Tools for Computational Crafting
Authors: Grow April
Published: October 2022

The role of computers and software as tool, manufacturer, collaborator, and artist in relation to human crafters has been heavily debated philosophically and practically. A creativity support tool for a specific creative domain is interdisciplinary, crossing fields of computational creativity, creativity support tools, procedural content generation, human-computer interaction, and potentially many others. This work aims to consolidate applicable research to domains with physical craft execution, as well as present an instance of a proactive and cooperative creativity support tool for the domain of machine embroidery pattern digitization, or simply digitizing. Proprietary software tools exist for digitizers, but auto-digitizing features — features that claim to automatically convert an image to a pattern — are widely disparaged in the industry, and novices and intermediate digitizers face a steep learning curve. The primary directive of this work is to ease that learning curve by helping suggest and caution the user about both the digital and physical best practices of the craft. In order to accomplish this task, the creativity support tool must successfully model machine embroidery such that it can be executed by embroidery machines, including basic operations, expert rules-of-thumb, and physical constraints, as well as interface with the user in a helpful and productive manner.
Combining Strategic Learning with Tactical Search in Real-Time Strategy Games
Authors: Barriga Nicolas , Stanescu Marius , Buro Michael
Published: October 2022

A commonly used technique for managing AI complexity in real-time strategy (RTS) games is to use action and/or state abstractions. High-level abstractions can often lead to good strategic decision making, but tactical decision quality may suffer due to lost details. A competing method is to sample the search space which often leads to good tactical performance in simple scenarios, but poor high-level planning. We propose to use a deep convolutional neural network (CNN) to select among a limited set of abstract action choices, and to utilize the remaining computation time for game tree search to improve low level tactics. The CNN is trained by supervised learning on game states labelled by Puppet Search, a strategic search algorithm that uses action abstractions. The network is then used to select a script — an abstract action -— to produce low level actions for all units. Subsequently, the game tree search algorithm improves the tactical actions of a subset of units using a limited view of the game state only considering units close to opponent units. Experiments in the microRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art microRTS agents. To the best of our knowledge, this is the first successful application of a convolutional network to play a full RTS game on standard game maps, as previous work has focused on sub-problems, such as combat, or on very small maps.
STARDATA: A StarCraft AI Research Dataset
Authors: Lin Zeming , Gehring Jonas , Khalidov Vasil , Synnaeve Gabriel
Published: October 2022

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset.
Memory Bounded Monte Carlo Tree Search
Authors: Powley Edward , Cowling Peter , Whitehouse Daniel
Published: October 2022

Monte Carlo Tree Search (MCTS) is an effective decision making algorithm that often works well without domain knowledge, finding an increasing application in commercial mobile and video games. A promising application of MCTS is creating AI opponents for board and card games, where Information Set MCTS (ISMCTS) can provide a challenging opponent and reduces the cost of creating game-specific AI opponents. Most research to date has aimed at improving the quality of decision making by (IS)MCTS, with respect to time usage. Memory usage is also an important constraint in commercial applications, particularly on mobile platforms or when there are many AI agents. This paper presents the first systematic study of memory bounding techniques for (IS)MCTS. (IS)MCTS is well known to be an anytime algorithm. We also introduce an anyspace version of (IS)MCTS which can make effective use of any pre-specified amount of memory. This algorithm has been implemented in a commercial version of the card game Spades downloaded more than 6 million times. We find that for games of imperfect information high quality decisions can be made with rather small memory footprints, making (IS)MCTS an even more attractive algorithm for commercial game implementations.
Scheduling Live Interactive Narratives with Mixed-Integer Linear Programming
Authors: Azad Sasha , Xu Jingyang , Yu Haining , Li Boyang
Published: October 2022

A live interactive narrative (LIN) is an experience where multiple players take on fictional roles and interact with real-world objects and actors to participate in a pre-authored narrative. Temporal properties of LINs are important to its viability and aesthetic quality and hence deserve special design consideration. In this paper, we tackle the largely overlooked problem of scheduling a multiplayer interactive narrative and propose the Live Interactive Narrative Scheduling Problem (LINSP), which handles reasoning under temporal uncertainty, resource scheduling, and non-linear plot choices. We present a mixed-integer linear programming formulation of the problem and empirically evaluates its scalability over large narrative instances.
Solving for Bespoke Game Assets: Applying Style to 3D Generative Artifacts
Authors: Mazeika Jo , Whitehead Jim
Published: October 2022

In this paper, we present Solus Forge, a system for designing and generating 3D Lego models from a decomposition of the model into pieces and a series of spatial constraints over those pieces. We also include a style specification, which provides a series of transformations to perform on the initial model; adding, removing or modifying various pieces. To generate the models, we use a two-stage constraint solving process in which we first solve for the layout of the final model before then solving for the specific layout of the individual Lego pieces. In this way, we provide a framework for models that incorporates a specific set of criteria but also can be modified to fit a designer's needs.
A/B-Test of Retention and Monetization Using the Cox Model
Authors: Viljanen Markus , Airola Antti , Heikkonen Jukka , Pahikkala Tapio
Published: October 2022

A/B testing is a popular tool for guiding mobile game development. The developer releases different versions of a game to different test cohorts, and observes which version has the best player retention or monetization. Correctly determining whether the differences are statistically significant is however challenging. Typically the analysis needs to be done on small and heterogeneous player cohorts, with differing follow-up times and unknown player churn. In this paper, we show for the first time how these issues can be properly addressed using the Cox model for recurrent events. The method enables a multivariate A/B-test, that allows determining which game version has the highest player retention or purchase rate, with confidence intervals provided. We demonstrate the benefits of the approach in multiple game development problems, on real-world free-to-play mobile game data.
Creating a Hyper-Agent for Solving Angry Birds Levels
Authors: Stephenson Matthew , Renz Jochen
Published: October 2022

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However, the performance of these various agents is non-transitive and varies significantly across different levels. No single agent dominates all situations presented, indicating that different procedures are better at solving certain levels than others. We therefore propose the construction of a hyper-agent that selects from a portfolio of sub-agents whichever it believes is best at solving any given level. This hyper-agent utilises key features that can be observed about a level to rank the available candidate algorithms based on their expected score.The proposed method exhibits a significant increase in performance over the individual sub-agents, and demonstrates the potential of using such an approach to solve other physics-based games or problems.
The Computational Complexity of Angry Birds and Similar Physics-Simulation Games
Authors: Stephenson Matthew , Renz Jochen , Ge Xiaoyu
Published: October 2022

This paper presents several proofs for the computational complexity of the popular physics-based puzzle game AngryBirds. By using a combination of different gadgets within this game’s environment, we can demonstrate that the problem of solving Angry Birds levels is NP-hard. Proof of NP-hardness is by reduction from a known NP-complete problem, in this case 3-SAT. In addition, we are able to show that the original version of Angry Birds is within NP and therefore alsoNP-complete. These proofs can be extended to other physics-based games with similar mechanics.
Level Difficulty and Player Skill Prediction in Human Computation Games
Authors: Sarkar Anurag , Cooper Seth
Published: October 2022

Human computation games (HCGs) often suffer from low player retention. This may be due to the constraints placed on level and game design from the real-world application of the game. Previous work has suggested using player rating systems (such as Elo, Glicko-2, or TrueSkill) as a basis for matchmaking between HCG levels and players, as a means to improve difficulty balancing and thus player retention. Such rating systems typically start incoming entities with a default rating. However, when applied to HCGs, incoming entities may have useful information associated with them, such as player behavior during tutorials and properties of the tasks underlying the levels. In this work, we examined using features derived from player behavior and level properties to predict their eventual Glicko-2 ratings in the HCG Paradox. We found that using regression produced rating estimates closer to the actual ratings than default or baseline average ratings. The use of rating systems allows a unified approach to predicting both player skill and level difficulty.
Fast Random Genetic Search for Large-Scale RTS Combat Scenarios
Authors: Clark Corey , Fleshner Anthony
Published: October 2022

This paper makes a contribution to the advancement of artificial intelligence in the context of multi-agent planning for large-scale combat scenarios in RTS games. This paper introduces Fast Random Genetic Search (FRGS), a genetic algorithm which is characterized by a small active population, a crossover technique which produces only one child, dynamic mutation rates, elitism, and restrictions on revisiting solutions. This paper demonstrates the effectiveness of FRGS against a static AI and a dynamic AI using the Portfolio Greedy Search (PGS) algorithm. In the context of the popular Real-Time Strategy (RTS) game, StarCraft, this paper shows the advantages of FRGS in combat scenarios up to the maximum size of 200 vs. 200 units under a 40 ms time constraint.
Secret Identities in Dwarf Fortress
Authors: Adams Tarn
Published: October 2022

Chairs’ Note: In this invited industry case study, Tarn Adams discusses recent extensions to Dwarf Fortress’s systems for character deception. A noted opus in the history of videogames, Dwarf Fortress is a roguelike set in procedurally generated fantasy universes. It has been shown at the Museum of Modern Art and has been featured in The New York Times, The New Yorker, Wired, and many other press publications. Currently, Tarn and his brother, Zach Adams, are roughly midway through its famous 30-year development cycle. As Tarn explains in this paper, an upcoming update centered around artifacts—and what characters know about them—has had the fun consequence of necessitating that a certain class of non-player characters cultivate secret identities. This major extension has brought both technical and design challenges, as this paper illustrates. —James Ryan
A Proposal for a Unified Agent Behaviour Framework
Authors: Torres Javier
Published: October 2022

Games have used different mechanisms along its history to provide agent behavior: FSMs, utility systems, behavior trees and planning methods. In this paper, we present an architecture that aims at incorporating all these approaches into trees of event handling nodes with behaviours as leaves, using rules for combining actions akin to utility systems. This formalism aims to develop hybrid systems in an easier way.
A Vision for Continuous Automated Game Design
Authors: Cook Michael
Published: October 2022

ANGELINA is an automated game design system which has previously been built as a single software block which designs games from start to finish. In this paper we outline a roadmap for the development of a new version of ANGELINA, designed to iterate on games in different ways to produce a continuous creative process that will improve the quality of its work, but more importantly improve the perception of the software as being an independently creative piece of software. We provide an initial report of the system's structure here as well as results from the first working module of the system.
Effects of Self-Knowledge: Once Bitten Twice Shy
Authors: Bulitko Vadim
Published: October 2022

Procedurally generating rich, naturally behaving AI-controlled video game characters is an important open problem. In this paper we focus on a particular aspect of non-playable character (NPC) behavior, long favored by science-fiction writers. Specifically, we study the effects of self-knowledge on NPC behavior. To do so we adopt the well-known framework of agent-centered real-time heuristic search applied to the standard pathfinding task on video-game maps. Such search agents normally use a heuristic function to guide them around a map to the goal state. Heuristic functions are inaccurate underestimates of the remaining distance to goal. What if the agent somehow knew how long it (the agent) would actually take to reach the goal from each state? How would using such self-knowledge in place of a heuristic function affect the agent's behavior? We show that similarly to real life, knowing of one's irrational behavior in a situation can deter the agent from getting into that situation again even if it is, in fact, a part of an optimal solution. We demonstrate the "fear" with a simple example and empirically show that the issue is common in video-game pathfinding. We then analyze the issue theoretically and suggest that "fear" induced by self-knowledge is not a bug but a feature and may potentially be used to develop more naturally behaving NPCs.
Generominos: Ideation Cards for Interactive Generativity
Authors: Compton Kate , Melcer Edward , Mateas Michael
Published: October 2022

We present Generominos, a set of design cards to model inter- active generative systems. While many ideation cards exist, Generominos attempts to model the constraints of making a transformative pipeline of data in the constraints of the cards themselves. For this paper, we contribute the design of the Generominos cards and a preliminary evaluation of perceived usefulness in an undergraduate alternative controller class.
Towards General RPG Playing
Authors: Osborn Joseph , Samuel Ben , Summerville Adam , Mateas Michael
Published: October 2022

General videogame playing has come a long way in a short period of time, but remains at the level of solving relatively short games made up of distinct and isolated episodes. Even simple console role-playing games (RPGs) are far beyond the reach of current techniques, requiring the synthesis of cultural knowledge with compositional reasoning over several interconnected sub-games. We explore how the challenges of playing these games could spark new advances in compositional analysis of games and common-sense reasoning. General RPG playing can leverage advances in episodic general game playing and in areas like text understanding, image classification, and automated game design learning. It has direct applications in design support and AI-based game design, and the techniques used to enable it could generalize to other families of games such as adventure, open-world, and simulation games. In this paper, we describe the motivation behind general RPG playing in a sub-domain of Nintendo Entertainment System (NES) RPGs, some promising approaches to some of its fundamental issues, and immediate next steps; we conclude by describing a few concrete benchmark problems on the path towards automated play of these complex games.