bluuebunny/crossref_metadata_2025 huggingface dataset:
PaTux: An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization
Authors: Abou-Zleikha Mohamed , Shaker Noor
Published: October 2022

We present a demonstration of PaTux, an authoring tool for designing levels in SuperTux game through combining patterns. PaTux allows game designers to specify the design of their levels using patterns extracted from training level samples. The Non-negative Matrix Factorisation (NMF) method is utilised to approximate pattern and weight matrices from the training data. The patterns are visualised for designers to choose from and the changes made on the level structure are visualised in realtime. The designer can also specify the weight of each pattern permitting exploration of a wider variety. The data used to train the model can also be specified by the designer resulting in learning a new set of patterns. The system also suggests variations for a given design. When the designer is satisfied with the design, the system allows loading the resultant level in the game to be played.
Exploring Abductive Event Binding for Opportunistic Storytelling
Authors: Tomai Emmett
Published: October 2022

We present a prototype game system using opportunistic storytelling, a particular commitment in the space of interactive narrative. It uses abductive event binding to deliver authored stories about the actions that the player is already taking to achieve game play goals. We show results of a simulation experiment that characterizes efficiency in delivering story event content, in terms of parameterized player motivation to follow storylines and the constraints placed on the events. Results show the value of two reasoning features in the system, incentive and look-ahead.
Organizers
Authors: Buro Michael , Ontañón Santi
Published: October 2022

List of Organizers of the Artificial Intelligence in Adversarial Real-Time Games AIIDE 2014 Workshop.
Preface
Authors: Buro Michael , Ontañón Santi
Published: October 2022

It is our pleasure to present to you the three papers that were accepted for presentation at this second AIIDE workshop on AI for adversarial real-time games, covering building placement optimization, sequential pattern mining for achieving short and long-term goals, and high-level representation for search in RTS games.
Towards the Automatic Generation of Fictional Ideas for Games
Authors: Llano Maria , Cook Michael , Guckelsberger Christian , Colton Simon , Hepworth Rose
Published: October 2022

The invention of fictional ideas is often a central process in the creative production of artefacts such as poems, music, paintings and games. Currently, fictional ideation is being studied by the Computational Creativity community within the WHIM European project. The aim of WHIM is to develop the What-If Machine, a software system capable of inventing, evaluating and presenting fictional ideas with cultural value. In this paper we explore the potential applications of the What-If Machine in the context of games. Specifically, we propose ways in which the What-If Machine can be used as an assistant for the design of games, by providing ideas about characters, the environment, etc., as well as a creative system during gameplay, through interesting interactions with the player.
A Rogue Dream: Automatically Generating Meaningful Content For Games
Authors: Cook Michael , Colton Simon
Published: October 2022

Procedural content generation is often seen simply as a means to generate stuff, elaborating on or rearranging abstract data types that describe levels or modular pieces of gameplay. Generating content which is situated in an understanding of the real-world is a much harder task; it requires access to large amounts of knowledge, and a good technique for parsing and using that knowledge. In this paper we describe A Rogue Dream, a game prototype which can generate new visual content and change its design based on an input word from the player at the start of the game. We describe the game and the tools it makes use of to do this, and use the game to discuss ways in which such techniques might enable unique kinds of gameplay or new directions for intelligent design tools.
The Future of Procedural Content Generation in Games
Authors: Smith Gillian
Published: October 2022

The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.
A Hierarchical System for Autonomous Musical Creation
Authors: Reimer M. , Garnett Guy
Published: October 2022

We describe work in progress on the development of a new hierarchical model of machine creativity operating in the domain of music. Similar to the way human brains work, our system separates low-level components associated with pattern recognition and analysis from the high-level creative components in two extensible layers. Separating this functionality in different layers of our system provides better visibility into the behavior of the creative component. This increased visibility has led to many improvements over previous iterations including the reward calculation for the creative component. Additionally, the design of an abstract input feature layer allows for greater flexibility in the number and combination of low-level features that can be used within our system.
Genomic: Combining Genetic Algorithms and Corpora to Evolve Sound Treatments
Authors: Stoll Thomas
Published: October 2022

Genomic is Python software that evolves sound treatments and produce novel sounds. It offers features that have the potential to serve sound designers and composers, aiding them in their search for new and interesting sounds. This paper lays out the rationale and some design decisions made for Genomic, and proposes several intuitive ways of both using the software and thinking about the techniques that it enables for the modification and design of sound.
Organizers
Authors: Pasquier Philippe
Published: October 2022

List of organizers of the Musical Metacreation 2014 AIIDE Workshop.
Unsupervised Learning of HTNs in Complex Adversarial Domains
Authors: Leece Michael
Published: October 2022

While Hierarchical Task Networks are frequently cited as flexible and powerful planning models, they are often ignored due to the intensive labor cost for experts/programmers, due to the need to create and refine the model by hand. While recent work has begun to address this issue by working towards learning aspects of an HTN model from demonstration, or even the whole framework, the focus so far has been on simple toy domains, which lack many of the challenges faced in the real world such as imperfect information and continuous environments. I plan to extend this work using the domain of real-time strategy (RTS) games, which have gained recent popularity as a challenging and complex domain for AI research.
Exploring Player Trace Segmentation for Dynamic Play Style Prediction
Authors: Valls-Vargas Josep , Ontañón Santiago , Zhu Jichen
Published: October 2022

Existing work on player modeling often assumes that the play style of players is static. However, our recent work shows evidence that players regularly change their play style over time. In this paper we propose a novel player modeling framework to capture this change by using episodic information and sequential machine learning techniques. In particular, we experiment with different trace segmentation strategies for play style prediction. We evaluate this new framework on gameplay data gathered from a game-based interactive learning environment. Our results show that sequential machine learning techniques that incorporate predictions from previous segments outperform non-sequential techniques. Our results also show that too fine (minute-by-minute) or too coarse (whole trace) segmentation of traces decreases performance.
Keeping the Player on an Emotional Trajectory in Interactive Storytelling
Authors: Poo Hernandez Sergio , Bulitko Vadim , Spetch Marcia
Published: October 2022

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player's experience in an interactive narrative. Such AI experience managers can generate and adapt narrative dynamically, often in response to the player's in-game actions. We implement and evaluate a recently proposed AI experience manager, PACE, which predicts the player's emotional response to a narrative event and uses such predictions to shape the narrative to keep the player on an author-supplied target emotional curve.
Ceptre: A Language for Modeling Generative Interactive Systems
Authors: Martens Chris
Published: October 2022

We present a rule specification language called Ceptre,intended to enable rapid prototyping for experimental game mechanics, especially in domains that depend on procedural generation and multi-agent simulation. Ceptre can be viewed as an explication of a new methodology for understanding games based on linear logic, a formal logic concerned with resource usage. We present a correspondence between gameplay and proof search in linear logic, building on prior work on generating narratives. In Ceptre, we introduce the ability to add interactivity selectively into a generative model, enabling inspection of intermediate states for debugging and exploration as well as a means of play. We claim that this methodology can support game designers and researchers in designing, anaylzing, and debugging the core systems of their work in generative, multi-agent gameplay. To support this claim, we provide two case studies implemented in Ceptre, one from interactive narrative and one from a strategy-like domain.
Capturing the Essence: Towards the Automated Generation of Transparent Behavior Models
Authors: Schwab Patrick , Hlavacs Helmut
Published: October 2022

Hand-coded finite-state machines and behavior trees are the go-to techniques for artificial intelligence (AI) developers that want full control over their character's bearing. However, manually crafting behaviors for computer-controlled agents is a tedious and parameter-dependent task. From a high-level view, the process of designing agent AI by hand usually starts with the determination of a suitable set of action sequences. Once the AI developer has identified these sequences he merges them into a complete behavior by specifying appropriate transitions between them. Automated techniques, such as learning, tree search and planning, are on the other end of the AI toolset's spectrum. They do not require the manual definition of action sequences and adapt to parameter changes automatically. Yet AI developers are reluctant to incorporate them in games because of their performance footprint and lack of immediate designer control. We propose a method that, given the symbolic definition of a problem domain, can automatically extract a transparent behavior model from Goal-Oriented Action Planning (GOAP). The method first observes the behavior exhibited by GOAP in a Monte-Carlo simulation and then evolves a suitable behavior tree using a genetic algorithm. The generated behavior trees are comprehensible, refinable and as performant as hand-crafted ones.
Refining the Paradigm of Sketching in AI-Based Level Design
Authors: Liapis Antonios , Yannakakis Georgios
Published: October 2022

This paper describes computational processes which can simulate how human designers sketch and then iteratively refine their work. The paper uses the concept of a map sketch as an initial, low-resolution and low-fidelity prototype of a game level, and suggests how such map sketches can be refined computationally. Different case studies with map sketches of different genres showcase how refinement can be achieved via increasing the resolution of the game level, increasing the fidelity of the function which evaluates it, or a combination of the two. While these case studies use genetic algorithms to automatically generate levels at different degrees of refinement, the general method described in this paper can be used with most procedural generation methods, as well as for AI-assisted design alongside a human creator.
A Lightweight Algorithm for Procedural Generation of Emotionally Affected Behavior and Appearance
Authors: Manavalan Yathirajan , Bulitko Vadim , Spetch Marcia
Published: October 2022

Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labour intense and greatly reduces the scope of emotions displayed and emotionally affected behavior in virtual characters. As a result, only a few virtual characters can display believable emotions and only in pre-scripted encounters. In this paper we implement and evaluate a lightweight algorithm for procedurally controlling both emotionally affected behavior and emotional appearance of a virtual character. The algorithm is based on two psychological models of emotions: conservation of resources and appraisal. The former component controls emotionally affected behavior of a virtual character whereas the latter generates explicit numeric descriptors of the character's emotions which can be used to drive the character's appearance. We implement the algorithm in a simple testbed and compare it to two baseline approaches via a user study. Human participants judged the emotions displayed by the algorithm to be more believable than those of the baselines.
Towards Generic Models of Player Experience
Authors: Shaker Noor , Shaker Mohammad , Abou-Zleikha Mohamed
Published: October 2022

Context personalisation is a flourishing area of research with many applications. Context personalisation systems usually employ a user model to predict the appeal of the context to a particular user given a history of interactions. Most of the models used are context-dependent and their applicability is usually limited to the system and the data used for model construction. Establishing models of user experience that are highly scalable while maintaing the performance constitutes an important research direction. In this paper, we propose generic models of user experience in the computer games domain. We employ two datasets collected from players interactions with two games from different genres where accurate models of players experience were previously built. We take the approach one step further by investigating the modelling mechanism ability to generalise over the two datasets. We further examine whether generic features of player behaviour can be defined and used to boost the modelling performance. The accuracies obtained in both experiments indicate a promise for the proposed approach and suggest that game-independent player experience models can be built.
Robustness and Flexibility of GHOST
Authors: Fradin Julien , Richoux Florian
Published: October 2022

GHOST is a framework to help game developers to model and implement their own optimization problems, or to simply instantiate a problem already encoded in GHOST. Previous works show that GHOST leads to high-quality solutions in some tens of milliseconds for three RTS-related problems: build order, wall-in placement  and target selection. In this paper, we show the robustness of the framework, having very good results for a problem it is not designed for (pathfinding), as well as its flexibility, where it is easy to propose different models of the same problem (resource allocation problem). The goal of the paper is not to improve the state-of-the-art on these problems, but to use them as benchmarks to test GHOST properties.
Planning in RTS Games with Incomplete Action Definitions via Answer Set Programming
Authors: Balduccini Marcello , Uriarte Alberto , Ontañón Santiago
Published: October 2022

Standard game tree search algorithms, such as minimax or Monte Carlo Tree Search, assume the existence of an accurate forward model that simulates the effects of actions in the game. Creating such model, however, is a challenge in itself.One cause of the complexity of the task is the gap in level of abstraction between the informal specification of the model and its implementation language. To overcome this issue, we propose a technique for the implementation of forward models that relies on the Answer Set Programming paradigm and on well-established knowledge representation techniques from defeasible reasoning and reasoning about actions and change. We evaluate our approach in the context of Real-Time Strategy games using a collection of StarCraft scenarios.
Sampling Hyrule: Multi-Technique Probabilistic Level Generation for Action Role Playing Games
Authors: Summerville Adam , Mateas Michael
Published: October 2022

Procedural Content Generation (PCG) using machine learning is a fast growing area of research. Action Role Playing Game (ARPG) levels represent an interesting challenge for PCG due to their multi-tiered structure and nonlinearity. Previous work has used Bayes Nets (BN) to learn properties of the topological structure of levels from The Legend of Zelda. In this paper we describe a method for sampling these learned distributions to generate valid, playable level topologies. We carry this deeper and learn a sampleable representation of the individual rooms using Principal Component Analysis. We combine the two techniques and present a multi-scale machine learned technique for procedurally generating ARPG levels from a corpus of levels from The Legend of Zelda.
Increasing the Engagement of Conversational Agents through Co-Constructed Storytelling
Authors: Battaglino Cristina , Bickmore Timothy
Published: October 2022

Storytelling can be used by conversational agents in a wide variety of domains to maintain user engagement, both within a single interaction and over dozens or hun- dreds of interactions over time. The majority of agents designed with this ability to date deliver their stories as monologues without user input. However, people rarely tell stories in conversations this way, and instead rely on listener contributions to guide the storytelling process. Corpus-based studies of human-human conversational storytelling have demonstrated greater engagement, in the form of longer stories, when listeners co-construct stories this way. We describe a research framework for the generation and evaluation of co-constructed social stories in the context of task-based conversations, and a study on the effects of degree of user-agent story co-construction on user engagement. We find that users are more en- gaged with storytelling agents that allow them to co- construct stories in a contentful manner by asking ques- tions, compared to co-construction through acknowl- edgments only.
Improving Monte Carlo Tree Search Policies in StarCraft via Probabilistic Models Learned from Replay Data
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

Applying game-tree search techniques to RTS games poses a significant challenge, given the large branching factors involved. This paper studies an approach to incorporate knowledge learned offline from game replays to guide the search process. Specifically, we propose to learn Naive Bayesian models predicting the probability of action execution in different game states, and use them to inform the search process of Monte Carlo Tree Search. We evaluate the effect of incorporating these models into several Multiarmed Bandit policies for MCTS in the context of StarCraft, showing a significant improvement in gameplay performance.
An Approach to Domain Transfer in Procedural Content Generation of Two-Dimensional Videogame Levels
Authors: Snodgrass Sam , Ontanon Santiago
Published: October 2022

Statistical models, such as Markov Chains, have been recently studied in the context of procedural content generation (PCG). These models can capture statistical regularities of a set of training data and use them to sample new content. However, these techniques assume the existence of sufficient training data with which to train the models. In this paper we study the setting in which we might not have enough training data from the target domain, but we have ample training data from another, similar domain. We propose an algorithm to discover a mapping between domains, so that out-of-domain training data can be used to train the statistical model. Specifically, we apply this to two-dimensional level generation, and experiment with three classic video games: Super Mario Bros., Kid Icarus and Kid Kool.
Staying Hidden: An Analysis of Hiding Strategies in a 2D Level with Occlusions
Authors: Singh Navjot , Verbrugge Clark
Published: October 2022

The need to stay hidden from opponents is a common feature of many games. Defining algorithmic strategies for hiding, however, is difficult, and thus not usually a non-player character activity outside of very simple or scripted behaviours. In this work we explore several algorithmic approaches for ensuring a character can remain hidden with respect to another, moving agent. We compare these strategies with an upper-bound solution based on a known opponent path, giving us a mechanism for evaluating both relative efficacy and for understanding the different factors that affect success. Experimental evaluation considers multiple levels, including ones adapted from commercial games, and also examines the impact of relative movement speed and different observer movements. Our analysis shows that simple cost-effective approaches to hiding are feasible, but success strongly depends on level geometry, with a large gap remaining between heuristic and optimal performance.
Data Driven Sokoban Puzzle Generation with Monte Carlo Tree Search
Authors: Kartal Bilal , Sohre Nick , Guy Stephen
Published: October 2022

In this work, we propose a Monte Carlo Tree Search (MCTS) based approach to procedurally generate Sokoban puzzles. Our method generates puzzles through simulated game play, guaranteeing solvability in all generated puzzles. We perform a user study to infer features that are efficient to compute and are highly correlated with expected puzzle difficulty. We combine several of these features into a data-driven evaluation function for MCTS puzzle creation. The resulting algorithm is efficient and can be run in an anytime manner, capable of quickly generating a variety of challenging puzzles. We perform a second user study to validate the predictive capability of our approach, showing a high correlation between increasing puzzle scores and perceived difficulty.
Playable Experiences at AIIDE 2016
Authors: Zook Alexander , Cook Michael , Butler Eric , Siu Kristin , Guzdial Matthew , Riedl Mark , Ryan James , Samuel Ben , Summerville Adam , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

The AIIDE Playable Experiences track celebrates innovations in how AI can be used in polished interactive experiences. Four 2016 accepted submissions display a diversity of approaches. Rogue Process combines techniques for medium-permanence procedurally generated hacking worlds. Elsinore applies temporal predicate logic to enable a time-traveling narrative with character simulation. A novel level generator uses conceptual blending to translate Mario Bros. design styles across levels. And Bad News uses deep simulation of a town and it's residents to ground a mixed-reality performance. Together these playable experiences showcase the opportunities for AI in interactive experiences.
Best-Fit Action-Cost Domain Model Acquisition and Its Application to Authorship in Interactive Narrative
Authors: Hayton Thomas , Gregory Peter , Lindsay Alan , Porteous Julie
Published: October 2022

Domain model acquisition is the problem of learning the structure of a state-transition system from some input data, typically example transition sequences. Recent work has shown that it is possible to learn action costs of PDDL models, given the overall costs of individual plans. In this work we have explored the extension of these ideas to narrative planning where cost can represent a variety of features (e.g. tension or relationship strength) and where exact solutions don’t exist. Hence in this paper we generalise earlier results to show that when an exact solution does not exist, a best-fit costing can be generated. This approach is of particular interest in the context of plan-based narrative generation where the input cost functions are based on subjective input. In order to demonstrate the effectiveness of the approach, we have learnt models of narratives using subjective measures of narrative tension. These were obtained with narratives (presented as video in this case) that were encoded as action traces, and then scored for subjective narrative tension by viewers. This provided a collection of input plan traces for our domain model acquisition system to learn a best-fit model. Using this learnt model we demonstrate how it can be used to generate new narratives that fit different target levels of tension.
A Cognitive-Based Model of Flashbacks for Computational Narratives
Authors: Wu Hui-Yin , Young Michael , Christie Marc
Published: October 2022

The flashback is a well-known storytelling device used to invoke surprise, suspense, or fill in missing details in a story. Film literature provides a deeper and more complex grounding of flashbacks by explaining their role to stimulate the viewer's memory in order to guide and change viewer comprehension. Yet, in adapting flashback mechanisms to AI storytelling systems, existing approaches have not fully modelled the roles of a flashback event on the viewer's comprehension and memory. To expand the scope of AI generated stories, we propose a formal definition of flashbacks based on the identification of four different impacts on the viewer's beliefs. We then establish a cognitive model that can predict how viewers would perceive a flashback event. We finally design a user-evaluation to demonstrate that our model correctly predicts the effects of different flashbacks. This opens great opportunities for creating compelling and temporally complex interactive narratives grounded on cognitive models.
Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents
Authors: Harrison Brent , Riedl Mark
Published: October 2022

In this work we introduce Quixote, a system that makes programming virtual agents more accessible to non-programmers by enabling these agents to be trained using the sociocultural knowledge present in stories. Quixote uses a corpus of exemplar stories to automatically engineer a reward function that is used to train virtual agents to exhibit desired behaviors using reinforcement learning. We show the effectiveness of our system with a case study conducted in a virtual environment called Robbery World that simulates a bank robbery scenario. In this case study, we use a corpus of stories crowdsourced from Amazon Mechanical Turk to guide learning. We evaluate Quixote under a variety of different conditions to determine the overall effectiveness of the system in Robbery World.
Contained: Using Multiplayer Online Games to Quantify Success of Collaborative Group Behavior
Authors: Debkowski Damian , Marrero Andrew , Yson Nicole , Yin Li , Yue Yichen , Frey Seth , Kapadia Mubbasir
Published: October 2022

Every day, millions of people gather on online game servers to collaborate in real time toward shared goals. What may seem like frivolous activity may, when investigated more closely, provide revolutionary opportunities to advance the science of teamwork. Teamwork is an important part of modern society, however, collaboration between people is often made difficult due to differing ideals, opinions, and personality types. We leverage a popular self-hosted multiplayer online game environment to design a framework for developing and deploying tasks that elicit different kinds of teamwork. We propose to use these to capture fine-scale details of individual and group performance across environments. The game in which we implement this system, Minecraft, is ideal because it is heavily modifiable and already enjoys a vast user base of surprising gender, age, and ethnic diversity. We heavily modify the game in order to introduce new mechanics that facilitate collaboration, monitor activity, and manipulate group composition, all to provide the groundwork for deeper quantitative insights into effective teams.
A Generalized Multidimensional Evaluation Framework for Player Goal Recognition
Authors: Min Wookhee , Baikadi Alok , Mott Bradford , Rowe Jonathan , Liu Barry , Ha Eun Young , Lester James
Published: October 2022

Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point. We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.
A Framework for Playable Social Dialogue
Authors: Treanor Mike , McCoy Josh , Sullivan Anne
Published: October 2022

Non-player characters in games generally lack believability and deep interactivity. The AI system Ensemble begins to tackle this by modeling social state and behaviors for game characters. With previous versions of Ensemble, the player initiates social exchanges and the system generates the dialogue and outcome and displays them in their entirety. In this paper we present an extension to Ensemble comprised of a model of playable social dialogue called social practices. Social practices increase the playability of character interactions and add interactivity at each stage of dialogue. Additionally, this work enables a more modular form of authoring to support the additional complexity.
StarCraft Winner Prediction
Authors: Norouzzadeh Ravari Yaser , Bakkes Snader , Spronck Pieter
Published: October 2022

In game-playing, a challenging topic is to investigate an evaluation function that accurately predicts which player will be the winner of a two-player match. Our work investigates to what extent it is possible to predict the winner of a StarCraft match, regardless of the races that are involved. We developed models for individual match types, and also general models for predicting the winner of non-symmetric matches, symmetric matches, and general matches. The contribution of this paper is (1) a generic and relatively accurate model for winner prediction in StarCraft, and (2) a detailed analysis of which features are the principal component in accurately predicting the winner in this complex game. Specially, our results show that we can predict the winner of a match with an accuracy of more than 63% in average over all time slices, regardless of the time slice and the combination of the match types. A study of which features are most important for the prediction of the match results, shows that the economic aspects of StarCraft matches are the strongest predictors for winning, followed by the use micro commands.
A Model of Superposed States
Authors: Robertson Justus , Young R.
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 an author's constraints. We approach this problem from an adversarial game perspective, where the IN system's goal is to prevent the player from creating invalid branches. From this perspective, one way an IN system can take action is to transition the game world between alternate states that are consistent with the player's observations during gameplay. In this paper we present a method of modelling and updating sets of world states consistent with player knowledge as a single superposed data structure. We discuss how this data structure can be used in an IN framework to maximize the probability that author constraints are maintained during gameplay.
Predicting Disengagement in Free-to-Play Games with Highly Biased Data
Authors: Xie Hanting , Devlin Sam , Kudenko Daniel
Published: October 2022

A vital application of game data mining is to predict player behaviour trends such as disengagement, purchase, etc.. Several works have been done by quantitative methods in the last decade. Generally, predicting player behaviour trends is a classification problem where class labels of instances are decided by predefined definitions. However, as the majority of current definitions distribute players into classes only by satisfying specific conditions, a highly biased class distribution may be led to if few (or most) players can satisfy these conditions. In this work, a new definition named trend over varying dates that can create balanced class distribution will be introduced and, as an example, disengagement prediction will be used to show how the definition works. Experiments on three commercial mobile games will show how this definition can be applied to games of various genres. Finally, the performance of this definition towards predicting disengagement will be compared with another disengagement concept called churn’. Both game-specific and event frequency based data representation (introduced in previous work) will be applied to represent the datasets for predictions. Results indicate that the definition of ‘trend over varying dates’ can improve the predictive performance by balancing the class distributions in most cases.
The Sensitivity of Retention to In-Game Advertisements: An Exploratory Analysis
Authors: Burns Zachary , Roseboom Isaac , Ross Nicholas
Published: October 2022

In-game advertisements have become a significant source of revenue for developers of freemium mobile games. Despite this, we are not aware of any published exploratory study looking into the effect of these advertisements on individual, user-level behavior. We consult existing literature on the psychology of advertising and interruption, and propose several metrics that could be used to measure user-level retention. Using a unique dataset representing 21 free-to-play mobile games, we create a simple model which attempts to determine the effect of advertisements on retention. We find only a weak relationship between advertisements and retention. More importantly we find that game-specific effects dominate all advertising effects, yielding important results for game development and design decisions. While the results presented have limitations, they provide a starting point for future research on individual level analysis.
MimicA: A Framework for Self-Learning Companion AI Behavior
Authors: Angevine Travis , Khosmood Foaad
Published: October 2022

We explore fully autonomous companion characters within the context of Real Time Strategy games. Non-player Characters that are controlled by Artificial Intelligence to some degree, have been a feature of Role Playing games for decades. But RTS games rarely have a player avatar, and thus no real companions. The universe of RTS games where both an avatar and a companion character exist is small. Most friendly RTS units are semi-autonomous at best, requiring player micromanagement of their behavior. We present MimicA, a real-time framework to govern AI companion behavior by modeling that of the current player. Built for the Unity engine, MimicA is a learn-by-demonstration framework that differs from existing practices in that the behavior is fully autonomous, does not rely on previous modeling exercises and is designed to be generalized and extensible. We analyze and discuss MimicA through a thirty person user study with our own demonstration game, Lord of Towers. We find that 22 out of 30 participants (73%) indicate they enjoyed the game, and this self-reported enjoyment was on par with “traditional tower defense games”. 63% agree that MimicA controlled NPCs are doing what the player would do while 20% disagree. Similarly, 53% realize the NPCs are learning from the player while 20% do not. We also show that NPC with underlying Decision Tree and Naive Bayes algorithms are better than KNN in making the player realize the learning nature of the NPC.
Towards Expressive Automated Storytelling Systems
Authors: Winer David
Published: October 2022

This work addresses the problem of generating narrative fiction by using a plan-based language to model schematic knowledge of storyworld mechanics (fabula) and communicative plans (discourse). The paper outlines an approach to extract fabula and discourse from screenplays as a way to overcoming an authorial bottleneck problem.
MTG: Context-Based Music Composition for Tabletop Role-Playing Games
Authors: Ferreira Lucas , Whitehead Jim
Published: October 2022

This project aims to compose background music in real-time for tabletop role-playing games. To accomplish this goal, we propose a system called MTG that listens to players' speeches in order to recognize the context of the current scene and generate background music to match the scene. A speech recognition system is used to transcribe players' speeches to text and a supervised learning algorithm detects when scene transitions take place. In its current version, a scene transition occurs whenever the emotional state of the narrative changes. Moreover, the background music is not generated, but selected based on its emotion from a library of hand-authored pieces. As future work, we plan to generate the background music considering the current scene context and the probability of scene transition. We also consider to retrieve more information from the narrative to detect scene transitions, such as the scene's location and time of the day as well as actions taken by characters.
Improvised Theatre Alongside Artificial Intelligences
Authors: Mathewson Kory , Mirowski Piotr
Published: October 2022

This study presents the first report of Artificial Improvisation, or improvisational theatre performed live, on-stage, alongside an artificial intelligence-based improvisational performer. The Artificial Improvisor is a form of artificial conversational agent, or chatbot, focused on open domain dialogue and collaborative narrative generation. Using state-of-the-art machine learning techniques spanning from natural language processing and speech recognition to reinforcement and deep learning, these chatbots have become more lifelike and harder to discern from humans. Recent work in conversational agents has been focused on goal-directed dialogue focused on closed domains such as appointment setting, bank information requests, question-answering, and movie discussion. Natural human conversations are seldom limited in scope and jump from topic to topic, they are laced with metaphor and subtext and face-to-face communication is supplemented with non-verbal cues. Live improvised performance takes natural conversation one step further with multiple actors performing in front of an audience. In improvisation the topic of the conversation is often given by the audience several times during the performance. These suggestions inspire actors to perform novel, unique, and engaging scenes. During each scene, actors must make rapid fire decisions to collaboratively generate coherent narratives. We have embarked on a journey to perform live improvised comedy alongside artificial intelligence systems. We introduce Pyggy and A.L.Ex. (Artificial Language Experiment), the first two Artificial Improvisors, each with a unique composition and embodiment. This work highlights research and development, successes and failures along the way, celebrates collaborations enabling progress, and presents discussions for future work in the space of artificial improvisation.
HyPED: Modeling and Analyzing Action Games as Hybrid Systems
Authors: Osborn Joseph , Lambrigger Brian , Mateas Michael
Published: October 2022

Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.
Reputation Systems for Non-Player Character Interactions Based on Player Actions
Authors: Brown Joseph , Lee Jooyoung , Kraev Niktia
Published: October 2022

In digital games there is an emphasis on the idea of quest completion; by completing a quest the character wins resources from the giver of the quest, they also will gain a reputation among the Non-Player Characters (NPCs) for its completion. However, this reputation currently propagates across the game world in an unrealistic manner; many NPCs will know of a completion of a quest many townships over without a narrative rationale. In this paper, we examine a method for allowing NPC interactions to spread reputation in a game world from an initial witness point of a quest completion to all other NPCs. This model is examined in a series of connected graphs: size five models, small world graphs, and graphs developed from digital games. Tests show that propagation of the information is highly dependent upon easily established properties of interactions, such as the graph regularity, average degree, and diameter. Further, real game graphs demonstrate that information generated in high population hubs can be propagated faster than that generated in smaller quests from outlying areas.
Evaluation of a Template-Based Puzzle Generator for an Educational Programming Game
Authors: Dong Yihuan , Barnes Tiffany
Published: October 2022

Although there has been much work on procedural content generation for other game genres, very few researchers have tackled automated content generation for educational games. In this paper, we present a template-based, automatic puzzle generator for an educational puzzle programming game called BOTS. Two experts created their own new puzzles and evaluated generator-generated puzzles for meeting the educational goals, the structural and visual novelty. We show that our generator can generate puzzles with expert-designed educational goals while saving experts more than 80% of creation time, and these puzzles exhibit structural and visual novelty compared to expert-created puzzles. The contribution of this work is defined and implemented the first template-based automatic puzzle generator that saves expert time while incorporating expert-designed educational goals and enhancing puzzle.
Causal Link Semantics for Narrative Planning Using Numeric Fluents
Authors: Farrell Rachelyn , Ware Stephen
Published: October 2022

Narrative planners would be able to represent richer, more realistic story domains if they could use numeric variables for certain properties of objects, such as money, age, temperature, etc. Modern state-space narrative planners make use of causal links—structures that represent causal dependencies between actions—but there is no established model of a causal link that applies to actions with numeric preconditions and effects. In order to develop a semantic definition for causal links that handles numeric fluents and is consistent with the human understanding of causality, we designed and conducted a user study to highlight how humans perceive enablement when dealing with money. Based on our evaluation, we present a causal semantics for intentional planning with numeric fluents, as well as an algorithm for generating the set of causal links identified by our model from a narrative plan.
LISA: Lexically Intelligent Story Assistant
Authors: Sanghrajka Rushit , Hidalgo Daniel , Chen Patrick , Kapadia Mubbasir
Published: October 2022

This paper serves as an introduction to building an assistive tool for story writers. Our tool, Lexically Intelligent Story Assistant (or LISA), aims to assist story writers by providing real-time feedback on lexical inconsistencies in the story. LISA analyzes the narrative and builds a knowledge base, using artificial intelligence to make inferences and point out errors in the narrative. Moreover, it also allows the user to interact with the system by querying the knowledge base in natural language form. This tool shows that it is possible to create a database for a narrative and use artificial intelligence to improve authoring of stories.
Emerging Sounds Through Implicit Cooperation: A Novel Model for Dynamic Music Generation
Authors: Escarce Junior Mário , Rossmann Martins Georgia , Soriano Marcolino Leandro , Tavares dos Passos Yuri
Published: October 2022

Normally agents cooperate when they have a joint goal or are able to get a higher payoff by doing so. We present a new perspective, where an agent cooperates with another without an explicit intention. We study this perspective in the context of Art Games, by introducing a novel algorithm where a human agent cooperates with a video game system in generating music in an emergent fashion, without needing awareness that he/she is doing so. We present a theoretical analysis of our system, and preliminary experiments with human subjects.
Single Believe State Generation for Handling Partial Observability with MCTS in StarCraft
Authors: Uriarte Alberto , Ontanon Santiago
Published: October 2022

A significant amount of work exists on handling partial observability for different game genres in the context of game tree search. However, most of those techniques do not scale up to RTS games. In this paper we present an experimental evaluation of a recently proposed technique, "single believe state generation," in the context of StarCraft. We evaluate the proposed approach in the context of a StarCraft playing bot and show that the proposed technique is enough to bring the performance of the bot close to the theoretical optimal where the bot can observe the whole game state.
The Current State of StarCraft AI Competitions and Bots
Authors: Čertický Michal , Churchill David
Published: October 2022

Real-Time Strategy (RTS) games have become an increasingly popular test-bed for modern artificial intelligence techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament (SSCAIT), the Computational Intelligence in Games (CIG) competition, and the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) competition. In this paper we will give an overview of the current state of these competitions, and the bots that compete in them.