bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Narrative Structure with MMORPG Quest Stories
Authors: Tomai Emmett , Martinez Eric , Silcox Lucian
Published: October 2022

In this paper we present a corpus of quest stories taken from a popular commercial Massively Multiplayer Online Role-Playing Game (MMORPG). These stories are open-ended narrative, but anchored to formal, in-game actions and entities, providing valuable constraint as a narrative corpus. We present two preliminary experiments establishing baselines for evaluating similar patterns and content across the corpus.
Architectural Issues for Compositional Dialog in Games
Authors: Horswill Ian
Published: October 2022

Making a game that supports generative conversation with NPCs involves a number of technical and design challenges, ranging from designing new game mechanics to making a Prolog interpreter run under Unity.  I discuss the software architecture of a game, scheduled for initial release in spring 2015, that attempts to solve some of these problems.
Generating Structure – Towards Large-Scale Formal Generation
Authors: Eigenfeldt Arne
Published: October 2022

Metacreative systems have been very successful at generating event-to-event musical details and generating short forms. However, large-scale formal decisions have tended to remain in the hands of a human, either through an operator guiding an improvisational system along in performance, or the assemblage of shorter generated sections into longer forms. This paper will describe the open problem of large-scale generative form, and the author’s attempts at delegating such decisions to a metacreative system by describing several of his generative systems and their approach to structure. The author will describe in greater detail his latest system — The Indifference Engine — and how it negotiates between agent intentions and performance information derived from a live performer.
Studying the Effect of Metre Perception on Rhythm and Melody Modelling with LSTMs
Authors: Lambert Andrew , Weyde Tillman , Armstrong Newton
Published: October 2022

In this paper we take a connectionist machine learning approach to the problem of metre perception and melody learning in musical signals. We present a two-layered network consisting of a nonlinear oscillator network and a recurrent neural network. The oscillator network acts as an entrained resonant filter to the musical signal. It `perceives' metre by resonating nonlinearly to the inherent periodicities within the signal, creating a hierarchy of strong and weak periods. The neural network learns the long-term temporal structures present in this signal. We show that this network outperforms our previous approach of a single layer recurrent neural network in a melody and rhythm prediction task. We hypothesise that our system is enabled to make use of the relatively long temporal resonance in the oscillator network output, and therefore model more coherent long-term structures. A system such as this could be used in a multitude of analytic and generative scenarios, including live performance applications.
Preface
Authors: Pasquier Philippe , Eigenfeldt Arne , Bown Oliver
Published: October 2022

The Musical Metacreation workshop aims to bring together artists, practitioners and researchers interested in developing software and systems that autonomously (or interactively) recognize, learn, represent, complete, accompany, compose or interpret music.
Targeting Horror via Level and Soundscape Generation
Authors: Lopes Phil , Liapis Antonios , Yannakakis Georgios
Published: October 2022

Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.
An Empirical Evaluation of Evaluation Metrics of Procedurally Generated Mario Levels
Authors: Mariño Julian , Reis Willian , Lelis Levi
Published: October 2022

There are several approaches in the literature for automatically generating Infinite Mario Bros levels. The evaluation of such approaches is often performed solely with computational metrics such as leniency and linearity. While these metrics are important for an initial exploratory evaluation of the content generated, it is not clear whether they are able to capture the player's perception of the content generated. In this paper we evaluate several of the commonly used computational metrics. Namely, we perform a systematic user study with procedural content generation systems and compare the insights gained from our user study with those gained from analyzing the computational metric values. The results of our experiment suggest that current computational metrics should not be used in lieu of user studies for evaluating content generated by computer programs.
Predicting Purchase Decisions in Mobile Free-to-Play Games
Authors: Sifa Rafet , Hadiji Fabian , Runge Julian , Drachen Anders , Kersting Kristian , Bauckhage Christian
Published: October 2022

Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.
Evaluating the Pairwise Event Salience Hypothesis in Indexter
Authors: Kives Christopher , Ware Stephen , Baker Lewis
Published: October 2022

Indexter is a plan-based computational model of narrative discourse which leverages cognitive scientific theories of how events are stored in memory during online comprehension. These discourse models are valuable for static and interactive narrative generation systems because they allow the author to reason about the audience's understanding and attention as they experience a story. A pair of Indexter events can share up to five indices: protagonist, time, space, causality, and intentionality. We present the first in a planned series of evaluations that will explore increasingly nuanced methods of using these indices to predict salience. The Pairwise Event Salience Hypothesis states that when a past event shares one or more indices with the most recently narrated event, that past event is more salient than one which shares no indices with the most recently narrated event. A crowd-sourced (n=200) study of 24 short text stories that control for content, text, and length supports this hypothesis. While this is encouraging, we believe it also motivates the development of a richer model that accounts for intervening events, narrative complexity, and episodic memory decay.
Generating Relaxed, Obvious, and Dilemma Choices with Dunyazad
Authors: Mawhorter Peter , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

Dunyazad is a system which creates narrative choices à la Choose-Your-Own-Adventure books. It attempts to generate choices that achieve specific poetic effects. This paper demonstrates Dunyazad’s ability to manage player expectations by having it generate three distinct choice structures: obvious choices, relaxed choices, and dilemmas. Using answer set programming, Dunyazad’s choice generation system directly encodes a theory of choice poetics, so flaws in its output can inform both the system and the theory itself. Survey data presented here thus not only validate that players’ perceptions match Dunyazad’s intentions, but also have implications for the theory of choice poetics. Statistical analysis of our data indicates that Dunyazad can successfully construct obvious choices, relaxed choices, and dilemmas.
Playable Experiences at AIIDE 2015
Authors: Cook Michael , Eiserloh Squirrel , Robertson Justus , Young R. , Thompson Tommy , Churchill David , Cerny Martin , Hernandez Sergio , Bulitko Vadim
Published: October 2022

This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part of AIIDE.
A Hierarchical MdMC Approach to 2D Video Game Map Generation
Authors: Snodgrass Sam , Ontanon Santiago
Published: October 2022

In this paper we describe a hierarchical method for procedurally generating 2D game maps using multi-dimensional Markov chains (MdMCs). Our method takes a collection of 2D game maps, breaks them into small chunks and performs clustering to find a set of chunks that correspond to high-level structures (high-level tiles) in the training maps. This set of high-level tiles is then used to re-represent the training maps, and to fit two sets of MdMC models: a high-level model captures the distribution of high-level tiles in the map, and a set of low-level models capture the internal structure of each high-level tile. These two sets of models can then be used to hierarchically generate new maps. We test our approach using two classic games, Super Mario Bros. and Loderunner, and compare the results against other existing map generators.
Playspecs: Regular Expressions for Game Play Traces
Authors: Osborn Joseph , Samuel Ben , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

We introduce Playspecs, an application of omega-regular expressions to specifying play traces (sequences of game states or events unfolding over time). This connects the automated analysis and model checking of games to the literature on formal software verification via Bu ̈chi automata. We show how to define desirable or undesirable sequences of game events with Playspecs and how associated algorithms can find examples (or prove the impossibility) of such sequences. Playspecs have two main benefits over existing techniques for specifying the behaviors of a game over time. First, they offer a scalable commitment to formal modeling: the same Playspecs can filter existing traces gathered by telemetry, search for satisfying traces using existing game code, or drive formal verification when paired with a logical model of a game. Second, Playspecs' syntax can be customized for the game engine or game in question so designers may write specifications using their game's native vocabulary. We define Playspecs' syntax and semantics (modulo gamespecific customizations) and outline algorithms for each of the applications mentioned above, providing examples from the social simulation game Prom Week and the puzzle game engine PuzzleScript.
Bayesian Clustering of Player Styles for Multiplayer Games
Authors: Normoyle Aline , Jensen Shane
Published: October 2022

With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player’s choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method which has several advantages: the number of clusters do not need to be specified a priori; the technique can work with a very compact representation of each match (e.g. consisting primarily of indicator variables for player choices); a player can belong to multiple clusters and hence can have a hybrid style; and the resulting clusterings often have a straight-forward interpretation. To demonstrate the approach, we apply our method to multiplayer match logs from Battlefield 3 consisting of over 1200 players and 500,000 matches.
HeapCraft: Quantifying and Predicting Collaboration in Minecraft
Authors: Müller Stephan , Frey Seth , Kapadia Mubbasir , Klingler Severin , Mann Richard , Solenthaler Barbara , Sumner Robert , Gross Markus
Published: October 2022

We present Heapcraft: an open-source suite of tools for monitoring and improving collaboration in Minecraft. At the core of our system is a data collection and analysis framework for recording gameplay. We collected over 3451 player-hours of game behavior from 908 different players, and performed a general study of online collaboration. To make our game analytics easily accessible, we developed interactive information visualization tools and an analysis framework for players, administrators, and researchers to explore graphs, maps and timelines of live server activity. As part of our research, we introduce the collaboration index, a metric which allows server administrators and researchers to quantify, predict, and improve collaboration on Minecraft servers. Our analysis reveals several possible predictors of collaboration which can be used to improve collaboration on Minecraft servers. Heapcraft is designed to be general, and has the potential to be used for other shared online virtual worlds.
StarCraft Unit Motion: Analysis and Search Enhancements
Authors: Schneider Douglas , Buro Michael
Published: October 2022

Real-time strategy (RTS) games pose challenges to AI research on many levels, ranging from selecting targets in unit combat situations, over efficient multi-unit pathfinding, to high-level economic decisions. Due to the complexity of RTS games, writing competitive AI systems for these games requires high speed adaptive algorithms and simplified models of the game world. In this paper we focus on motion prediction and motion planning in StarCraft — a popular RTS game for which a C++ API exists that allows us to write AI systems to play the game. We explore our existing unit motion model of StarCraft and find and fix some inconsistencies to improve the model by accounting for systematic command execution delays and unit acceleration. We then investigate ways to improve existing combat motion planning systems that are based on discrete unit motion sets, and show that search-based algorithms and scripts can benefit from using a new direction set that considers moves towards the closest enemy unit, away from it, and perpendicular to both directions.
MCMCTS PCG 4 SMB: Monte Carlo Tree Search to Guide Platformer Level Generation
Authors: Summerville Adam , Philip Shweta , Mateas Michael
Published: October 2022

Markov chains are an enticing option for machine learned generation of platformer levels, but offer poor control for designers and are likely to produce unplayable levels. In this paper we present a method for guiding Markov chain generation using Monte Carlo Tree Search that we call Markov Chain Monte Carlo Tree Search (MCMCTS). We demonstrate an example use for this technique by creating levels trained on a corpus of levels from Super Mario Bros. We then present a player modeling study that was run with the hopes of using the data to better inform the generation of levels in future work.
Intelligent Content Generation via Abstraction, Evolution and Reinforcement
Authors: LeBaron Dean , Mitchell Logan , Ventura Dan
Published: October 2022

We present a system for autonomously generating puzzles in the form of a 2D, tile-based world.  Puzzle design is entirely dependent on tile characteristics, which are implemented as abstract classes that can be modified by the system.  Thus, the system controls not only the base-level puzzle design but also (to some extent) the meta-level component design.  The result is a rich space of possible puzzles that the system explores with a combination of evolutionary computation and Q-learning.  The system autonomously produces a variety of puzzles of varying difficulty to create a game called Loki's Castle.  The system is almost completely autonomous, requiring only a minimal description of what a puzzle should include, and the abstraction allows extensibility so that future versions can invent entirely new classes of tiles.  Several puzzle examples are presented to demonstrate the system's capability.
An Algorithmic Approach to Decorative Content Placement
Authors: Tremblay Jonathan , Verbrugge Clark
Published: October 2022

Placing decorative content in any virtual environment is not a trivial task, as the location of even non-critical content influences player movement choices through the level, as well as the general game aesthetic. In this work we use path visibility properties to define and investigate three methods for content placement in a game level, aimed at generating shared, unique, and never seen content. We compare and evaluate the three approaches using a human study where each player was given a level layout based on our three methods, showing our directed process has a non-trivial and controllable impact on player movement.
Towards Generating Novel Games Using Conceptual Blending
Authors: Gow Jeremy , Corneli Joseph
Published: October 2022

We sketch the process of creating a novel video game by blending two video games specified in the Video Game Description Language (VGDL), following the COINVENT computational model of conceptual blending. We highlight the choices that need to be made in this process, and discuss the prospects for a computational game designer based on blending.
Implementing Injunctive Social Norms Using Defeasible Reasoning
Authors: Blass Joseph , Horswill Ian
Published: October 2022

Believability requires video game characters to consider their actions within the context of social norms. Social norms involve a broad range of behavioral defaults, obligations, and injunctions unrelated to strictly causal reasoning.  Defeasible reasoning involves rationally compelling but deductively invalid arguments, such as reasoning with rules that allow exceptions. This paper investigates having video game characters use defeasible reasoning to consider injunctive social norms when selecting and planning actions.
Interactive Narrative Intervention Alibis through Domain Revision
Authors: Robertson Justus , Young R. Michael
Published: October 2022

Interactive narrative systems produce branching story experiences for a human user using an interactive world. A class of interactive narrative systems, called strong story systems, manage a user's experience by manipulating the interactive world and its characters according to a formal story model. In these systems, a human user may place the world into a state such that the formal story model can no longer control interaction. One solution to this problem, called intervention, is to exchange the undesirable outcomes of a player's action for a set that do not violate the story model. However, the player may become aware that their intended action is being intervened against by a context-sensitive, meta-narrative process. In this paper we describe a method of ensuring game world alibis for interventions through domain modification of world mechanics.
Plotter: Operationalizing the Master Book of All Plots
Authors: Eger Markus , Potts Colin , Barot Camille , Young R. Michael
Published: October 2022

Pulp fiction author William Wallace Cook published Plotto: The Master Book of All Plots in 1928, which contains almost 2000 plot fragments and relatively formal instructions on how human authors could combine them to produce plots behind novels. In this paper we show one way that the methods in this book can be used to computationally generate plots from the fragments. We also show sample plots generated by our system called Plotter that uses this method. Finally we use them to discuss idiosyncrasies and limitations of the book.
Merits of a Temporal Modal Logic for Narrative Discourse Generation
Authors: Eger Markus , Barot Camille , Young R.
Published: October 2022

Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a temporal, modal logic provides more expressivity than languages historically associated with story generation, while still affording reasoning capabilities. We show that these properties enable certain aspects of narrative discourse generation by using two examples from different genres, and claim that this generalizes to a broader class of problems.
Toward Natural Language Generation by Humans
Authors: Ryan James , Fisher Andrew , Owen-Milner Taylor , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

Natural language generation (NLG) has been featured in at most a handful of shipped games and interactive stories. This is certainly due to it being a very specialized practice, but another contributing factor is that the state of the art today, in terms of content quality, is simply inadequate. The major benefits of NLG are its alleviation of authorial burden and the capability it gives to a system of generating state-bespoke content, but we believe we can have these benefits without actually employing a full NLG pipeline. In this paper, we present the preliminary design of Expressionist, an in-development mixed-initiative authoring tool that instantiates an authoring scheme residing somewhere between conventional NLG and conventional human content authoring. In this scheme, a human author plays the part of an NLG module in that she starts from a set of deep representations constructed for the game or story domain and proceeds to specify dialogic content that may express those representations. Rather than authoring static dialogue, the author defines a probabilistic context-free grammar that yields templated dialogue. This allows a human author to still harness a computer's generativity, but in a capacity in which it can be trusted: operating over probabilities and treelike control structures. Additional features of Expressionist's design include arbitrary markup and realtime feedback showing currently valid derivations.
Toward an Automated Measure of Narrative Complexity
Authors: Harmon Sarah , Jhala Arnav
Published: October 2022

For young children, adults learning English, or individuals with language disorders, complex narratives are difficult to create and understand.  While narratives can easily be assessed in terms of their lexical and syntactic difficulty, automatically measuring the level of narrative complexity is a challenging problem.  We present and evaluate a preliminary system for assessing narrative complexity, which should help identify suitable texts for readers and assist in narrative skill evaluation.
Comparing Clustering Approaches for Modeling Players' Values through Avatar Construction
Authors: Lim Chong-U , Harrell D. Fox
Published: October 2022

Videogame avatars provide an expressive avenue for players to represent themselves virtually. Research has shown that these avatars, while virtual, can reveal aspects of players' identities, along with physical, social, and cultural values of the real-world. In this paper, we present an approach for modeling player values through their avatars using artificial intelligence (AI) clustering techniques. In a study with 191 participants who created avatars using our system, we provide a thorough comparison of the techniques across numerical, textual, and visual data. Our findings showed that these data structures can effectively reveal players' values and preferences, such as conforming to stereotypes of character roles using statistical attributes, modeling nuances in text descriptions of avatars, and identifying "best-example" (prototypical) avatar appearances that players can be quantitatively shown to conform to. Our findings suggest that AI clustering approaches can be used to model players to yield insight into implicitly held values in a data-driven manner through virtual avatars.
Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions
Authors: Chen Zhengxing , Seif El Nasr Magy , Canossa Alessandro , Badler Jeremy , Tignor Stefanie , Colvin Randy
Published: October 2022

There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.
Procedural Level Generation for Augmented Reality Games
Authors: Azad Sasha , Saldanha Carl , Gan Cheng-Hann , Riedl Mark
Published: October 2022

Mixed reality games are those in which virtual graphical assets are overlaid on the physical world. We explore the use of procedural content generation to enhance the gameplay experience in a prototype mixed reality game. Procedural content generation is used to design levels that make use of the affordances in the player’s physical environment. Levels are tailored to gameplay difficulty and to affect how the player moves their physical body in the real world.
Game Level Generation from Gameplay Videos
Authors: Guzdial Matthew , Riedl Mark
Published: October 2022

We present an unsupervised process to generate full video game levels from a model trained on gameplay video. The model represents probabilistic relationships between shapes properties, and relates the relationships to stylistic variance within a domain. We utilize the classic platformer game Super Mario Bros. to evaluate this process due to its highly-regarded level design. We evaluate the output in comparison to other data-driven level generation techniques via a user study and demonstrate its ability to produce novel output more stylistically similar to exemplar input.
Portfolio Online Evolution in StarCraft
Authors: Wang Che , Chen Pan , Li Yuanda , Holmgård Christoffer , Togelius Julian
Published: October 2022

Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions, and (2) Online Evolution, which uses evolution rather than tree search to effectively play games where multiple actions per turn lead to enormous branching factors. The combination of both ideas lead to the use of evolution to search the space of which script/heuristic is assigned to which unit. In this paper, we introduce the ideas of Portfolio Online Evolution and apply it to StarCraft micro, or individual battles. It is shown to outperform all other tested methods in battles of moderate to large size.
Recognizing Actions in Motion Trajectories Using Deep Neural Networks
Authors: Singh Kunwar , Davis Nicholas , Hsiao Chih-Pin , Jacob Mikhail , Patel Krunal , Magerko Brian
Published: October 2022

This paper reports on the progress of a co-creative pretend play agent designed to interact with users by recognizing and responding to playful actions in a 2D virtual environment. In particular, we describe the design and evaluation of a classifier that recognizes 2D motion trajectories from the user’s actions. The performance of the classifier is evaluated using a publicly available dataset of labeled actions highly relevant to the domain of pretend play. We show that deep convolutional neural networks perform significantly better in recognizing these actions than previously employed methods. We also describe the plan for implementing a virtual play environment using the classifier in which the users and agent can collaboratively construct narratives during improvisational pretend play.
Per-Map Algorithm Selection in Real-Time Heuristic Search
Authors: Bulitko Vadim
Published: October 2022

Real-time heuristic search is suitable for time-sensitive pathfinding and planning tasks when an AI-controlled non-playable character must interleave its planning and plan execution. Since its inception in the early 90s, numerous real-time heuristic search algorithms have been proposed. Many of the algorithms also have control parameters leaving a practitioner with a bewildering array of choices. Recent work treated the task of algorithm and parameter selection as a search problem in itself. Such automatically found algorithms outperformed previously known manually designed algorithms on the standard video-game pathfinding benchmarks. In this paper we follow up by selecting an algorithm and parameters automatically per map. Our sampling-based approach is efficient on the standard video-game pathfinding benchmarks. We also apply the approach to per-problem algorithm selection and while it is effective there as well, it is not practical. We offer suggestions on making it so.
Building Helpful Virtual Agents Using Plan Recognition and Planning
Authors: Geib Christopher , Weerasinghe Janith , Matskevich Sergey , Kantharaju Pavan , Craenen Bart , Petrick Ronald
Published: October 2022

This paper presents a new model of cooperative behavior based on the interaction of plan recognition and automated planning.  Based on observations of the actions of an "initiator" agent, a  "supporter" agent uses plan recognition to hypothesize the plans  and goals of the initiator.  The supporter agent then proposes and  plans for a set of subgoals it will achieve to help the initiator.  The approach is demonstrated in an open-source, virtual robot  platform.
A Generic Approach for Player Modeling Using Event-Trait Mapping and Feature Weighting
Authors: Gunes M. , Solak Gokhan , Akin Ugur , Erden Omer , Sariel Sanem
Published: October 2022

There are a wide variety of studies on player modeling. However, most of these studies target a specific game or genre. In some of these works, the number of in-game actions is used as a feature for modeling a player. However, using this feature leads to a complex model, and the model may miss some high-level relations among actions. In this paper, we propose a generic player modeling method that uses action-trait mapping relations which reveal correlations among actions. Mapping from the action-space to a much smaller trait-space improves interpretability of models. Additionally, to use the differences of impact of actions on player models, we apply feature weighting which uses the inverse of action frequencies. Players are clustered by Expectation Maximization. We demonstrate our method on a casual mobile game, Dusk Racer. We evaluate the feature weighting method using cluster validation with internal criteria. We conclude that using traits and feature weighting improves clustering quality and usability of the player model.
Experiments on Learning Unit-Action Models from Replay Data from RTS Games
Authors: Ontanon Santiago
Published: October 2022

Recent work has shown that incorporating action probability models (models that given a game state can predict the probability with which an expert will play each move) into MCTS can lead to significant performance improvements in a variety of adversarial games, including RTS games. This paper presents a collection of experiments aimed at understanding the relation between the amount of training data, the predictive performance of the action models, the effect of these models in the branching factor of the game and the resulting performance gains in MCTS. Experiments are carried out in the context of the microRTS simulator, showing that more accurate predictive models do not necessarily result in better MCTS performance.
Learning Player Tailored Content From Observation: Platformer Level Generation from Video Traces using LSTMs
Authors: Summerville Adam , Guzdial Matthew , Mateas Michael , Riedl Mark
Published: October 2022

A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.
Dear Leader’s Happy Story Time: A Party Game Based on Automated Story Generation
Authors: Horswill Ian
Published: October 2022

Players in Dear Leader’s Happy Story Time are placed in the role of contestants in a reality TV show where they are forced to audition for roles in the upcoming film of the host, a deranged billionaire who has inexplicably been elected president.  The stories are produced by a story generator that combines stock plots and characters to produce kitsch story outlines.  The players then collaborate to improvise a camp performance of the outline.  The game design provides a context for experimenting with automatic story generation within a narrative game, as well as an opportunity for experimenting with knowledge representation schemes for expressing the tropes of popular narrative.  The story generator uses a higher-order logic for describing tropes, and an HTN planning algorithm based on Nau et al.’s SHOP.
Crowdsourced Social Media Poetry
Authors: Lamb Carolyn
Published: October 2022

We describe our program of PhD work in which a computer program creates topical poems out of text found on Twitter. These poems are made using a combination of natural language processing and crowdsourcing and are part of a general research plan involving the creation and evaluation of computer-generated poetry, grounded in domain-specific research on human creativity.
Pre-Learning Experiences with Co-Creative Agents in Museums
Authors: Long Duri
Published: October 2022

Co-creative agents, or artificially intelligent computer agents that can collaborate creatively in real-time with human partners, have proven successful in being both creatively engaging and fun to interact with. Prior research in museum experience design also indicates that due to their incorporation of embodied interaction, creative narrative construction, and personal identity, co-creative agents have potential to drive pre-learning experiences that motivate participants to learn more about technology in museum settings. However, many co-creative agents fall short in effectively communicating technology-related educational outcomes. My work aims to explore how museum experiences involving co-creative agents can be designed and evaluated such that they both foster creative engagement and facilitate pre-learning experiences, using two interactive installation projects (LuminAI and TuneTable) as technical probes.
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
Authors: Ma Hang , Yang Jingxing , Cohen Liron , Kumar T. K. , Koenig Sven
Published: October 2022

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPFalgorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.
Deep Learning for Real-Time Heuristic Search Algorithm Selection
Authors: Sigurdson Devon , Bulitko Vadim
Published: October 2022

Real-time heuristic search algorithms are used for creating agents that rely on local information and move in a bounded amount of time making them an excellent candidate for video games as planning time can be controlled. Path finding on video game maps has become the de facto standard for evaluating real-time heuristic search algorithms. Over the years researchers have worked to identify areas where these algorithms perform poorly in an attempt to mitigate their weaknesses. Recent work illustrates the benefits of tailoring algorithms for a given problem as performance is heavily dependent on the search space. In order to determine which algorithm to select for solving the search problems on a map the developer would have to run all the algorithms in consideration to obtain the correct choice. Our work extends the previous algorithm selection approach to use a deep learning classifier to select the algorithm to use on new maps without having to evaluate the algorithms on the map. To do so we select a portfolio of algorithms and train a classifier to predict which portfolio member to use on a unseen new map. Our empirical results show that selecting algorithms dynamically can outperform the single best algorithm from the portfolio on new maps, as well provide the lower bound for potential improvements to motivate further work on this approach.
A Possible Worlds Model of Belief for State-Space Narrative Planning
Authors: Shirvani Alireza , Ware Stephen , Farrell Rachelyn
Published: October 2022

What characters believe, how they act based on those beliefs,and how their beliefs are updated is an essential element of many stories. State-space narrative planning algorithms treat their search spaces like a set of temporally possible worlds. We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is generated. We also present the results of an experiment that demonstrates that the model meets the expectations of a human audience.
Turn-Taking with Improvisational Co-Creative Agents
Authors: Winston Lauren , Magerko Brian
Published: October 2022

Turn-taking is the ability for agents to lead or follow in social interactions. Turn-taking between humans and intelligent agents has been studied in human-robot interaction but has not been applied to improvisational, dance-based interactions. User understanding and experience of turn-taking in an improvisational, dance-based system known as LuminAI was investigated in a preliminary study of 11 participants. The results showed a trend towards users understanding the difference between turn-taking and non-turn-taking versions of LuminAI but reduced user experience in the turn-taking version.
A Recommender System for Hero Line-Ups in MOBA Games
Authors: Hanke Lucas , Chaimowicz Luiz
Published: October 2022

MOBA games are currently one the most popular online game genres. In their basic gameplay, two teams of multiple players compete against each other to destroy the enemy's base, controlling a powerful unit known as "hero". Each hero has different abilities, roles and strengths. Thus, choosing a good combination of heroes is fundamental for the success in the game. In this paper we propose a recommendation system for selecting heroes in a MOBA game. We develop a mechanism based on association rules that suggests the more suitable heroes for composing a team, using data collected from a large number of DOTA 2 matches. For evaluating the efficacy of the line-up, we trained a neural network capable of predicting the winner team with a 88.63% accuracy. The results of the recommendation system were very satisfactory with up to 74.9% success rate.
Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny
Authors: Norouzzadeh Ravari Yaser , Spronck Pieter , Sifa Rafet , Drachen Anders
Published: October 2022

Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multi-player competitive games with mixed genres is addressed.The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99%in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.
Merits of Hierarchical Story and Discourse Planning with Merged Languages
Authors: Winer David , Young R. Michael
Published: October 2022

A hierarchical, bipartite model can characterize many complex narrative phenomena associated with coordinating plot and communication in storytelling (e.g., cinematography), but the predominant pipeline-based strategy for generating narratives has inadvertently limited the expressiveness of storytelling systems. We introduce computational steps for merging story and discourse languages in plan-based storytelling systems with hierarchical knowledge which avoids this problem and motivates more expressive narrative discourse reasoning.
Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games
Authors: Padovani Rafael , Ferreira Lucas , Lelis Levi
Published: October 2022

In this paper we introduce Bardo, a real-time intelligent system to automatically select the background music for tabletop role-playing games. Bardo uses an off-the-shelf speech recognition system to transform into text what the players say during a game session, and a supervised learning algorithm to classify the text into an emotion. Bardo then selects and plays as background music a song representing the classified emotion. We evaluate Bardo with a Dungeons and Dragons (D&D) campaign available on YouTube. Accuracy experiments show that a simple Naive Bayes classifier is able to obtain good prediction accuracy in our classification task. A user study in which people evaluated edited versions of the D&D videos suggests that Bardo's selections can be better than those used in the original videos of the campaign.
Simulating Player Behavior for Data-Driven Interactive Narrative Personalization
Authors: Wang Pengcheng , Rowe Jonathan , Min Wookhee , Mott Bradford , Lester James
Published: October 2022

Data-driven approaches to interactive narrative personalization show significant promise for applications in entertainment, training, and education. A common feature of data-driven interactive narrative planning methods is that an enormous amount of training data is required, which is rarely available and expensive to collect from observations of human players. An alternative approach to obtaining data is to generate synthetic data from simulated players. In this paper, we present a long short-term memory (LSTM) neural network framework for simulating players to train data-driven interactive narrative planners. By leveraging a small amount of previously collected human player interaction data, we devise a generative player simulation model. A multi-task neural network architecture is proposed to estimate player actions and experiential outcomes from a single model. Empirical results demonstrate that the bipartite LSTM network produces the better-performing player action prediction models than several baseline techniques, and the multi-task LSTM derives comparable player outcome prediction models within a shorter training time. We also find that synthetic data from the player simulation model contributes to training more effective interactive narrative planners than raw human player data alone.
An Analysis of Model-Based Heuristic Search Techniques for StarCraft Combat Scenarios
Authors: Churchill David , Lin Zeming , Synnaeve Gabriel
Published: October 2022

Real-Time Strategy games have become a popular test-bed for modern AI system due to their real-time computational constraints, complex multi-unit control problems, and imperfect information. One of the most important aspects of any RTS AI system is the efficient control of units in complex combat scenarios, also known as micromanagement. Recently, a model-based heuristic search technique called Portfolio Greedy Search (PGS) has shown promisingpaper we present the first integration of PGS into the StarCraft game engine, and compare its performance to the current state-of-the-art deep reinforcement learning method in several benchmark combat scenarios. We then perform theperformance for providing real-time decision making in RTS combat scenarios, but has so far only been tested in SparCraft: an RTS combat simulator. In this same experiments within the SparCraft simulator in order to investigate any differences between PGS performance in the simulator and in the actual game. Lastly, we investigate how varying parameters of the SparCraft simulator affect the performance of PGS in the StarCraft game engine. We demonstrate that the performance of PGS relies heavily on the accuracy of the underlying model, outperforming other techniques only for scenarios where the SparCraft simulation model more accurately matches the StarCraft game engine.