bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning for Speech Accent Detection in Videogames
Authors: Ensslin Astrid , Goorimoorthee Tejasvi , Carleton Shelby , Bulitko Vadim , Poo Hernandez Sergio
Published: October 2022

In video games, a wide range of characters make up the world players inhabit. These characters, NPCs, have traits, such as their appearance and speech accent, that determine certain things about them, including moral inclination, levels of trustworthiness, social class, levels of education, and ethnic background. But what does an accent say about a character in a video game? We use deep learning to train a neural network to detect speech accents and establish the degree to which machines can be used to recognize these accents. This research aims to help sociolinguists and discourse analysts establish critical study and content analytical findings for instance about stereotypical uses of speech accents, to better analyze who has what accent in video games, and what kind of language ideologies and social value judgments the use of accents in games construct and perpetuate. This paper presents the results of the first deep learning experiments, which were conducted on Standard North American, British Received Pronunciation, and Spanish English. We discuss our methodological considerations and some early deep learning results, which show relatively low levels of accuracy (61%). We discuss possibilities of improving our method, and of enriching our training datasets.
Poetic Sound Similarity Vectors Using Phonetic Features
Authors: Parrish Allison
Published: October 2022

A procedure that uses phonetic transcriptions of words to produce a continuous vector-space model of phonetic sound similarity is presented. The vector dimensions of words in the model are calculated using interleaved phonetic feature bigrams, a novel method that captures similarities in sound that are difficult to model with orthographic or phonemic information alone. Measurements of similarity between items in the resulting vector space are shown to perform well on established tests for predicting phonetic similarity. Additionally, a number of applications of vector arithmetic and nearest-neighbor search are presented, demonstrating potential uses of the vector space in experimental poetry and procedural content generation.
A Generative Framework of Generativity
Authors: Compton Kate , Mateas Michael
Published: October 2022

Several frameworks exist to describe how procedural content can be understood, or how it can be used in games. In this paper, we present a framework that considers generativity as a pipeline of successive data transformations, with each transformation either generating, transforming, or pruning away information. This framework has been iterated through repeated engagement and education interactions with the game development and generative art communities. In its most recent refinement, it has been physically instantiated into a deck of cards, which can be used to analyze existing examples of generativity or design new generative systems. This Generative Framework of Generativity aims to constructively define the design space of generative pipelines.
"Press Space to Fire": Automatic Video Game Tutorial Generation
Authors: Green Michael , Khalifa Ahmed , Barros Gabriella , Togellius Julian
Published: October 2022

We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.
Towards Positively Surprising Non-Player Characters in Video Games
Authors: Bulitko Vadim , Carleton Shelby , Cormier Delia , Sigurdson Devon , Simpson John
Published: October 2022

Video games often populate their in-game world with numerous ambient non-playable characters. Manually crafting interesting behaviors for such characters can be prohibitively expensive. As scripted AI gets re-used across multiple characters, they can appear overly similar, shallow and generally uninteresting for the player to interact with. In this paper we propose to evolve interesting behaviors in a simulated evolutionary environment. Since only some evolution runs may give rise to such behaviors, we propose to train deep neural networks to detect such behaviors. The paper presents work in progress in this direction.
Testing the Effectiveness of Narrative Archetypes for Archive Exploration
Authors: Damiano Rossana , Lieto Antonio , Lombardo Vincenzo
Published: October 2022

In this paper, we propose the use of narrative archetypes for supporting the creation of effective exploratory search tools for archives. For example, a path can be created through items that display characters of the same story, or the same story events. By embedding the archetypes in a semantic sys- tem for archive navigation, we tested their effectiveness with users, and collected the users’ preferences about the use of the system. Participants belonged to two different groups: pro- fessional users (editors, journalists, etc.) and causal users. A small focus group was conducted with professional users. The results show a good acceptance of the approach, with higher scores by professional users.
Generating Stories that Include Failed Actions by Modeling False Character Beliefs
Authors: Thorne Brandon , Young R. Michael
Published: October 2022

Previous work on story planning has lacked a knowledge representation for characters that make mistakes in the execution of their actions. In particular, characters' execution mistakes that arise from errors in belief have not been modeled. In this paper, we describe a state-space planning system and its belief model, together called HeadSpace, that generates stories that track and manipulates characters' belief about the story world around them. This model is used to produce actions in stories that are attempted but that fail. We show an example story plan that contains failed-action content that cannot be generated by typical planning-based approaches to story creation.
Narrative Mediation as Probabilistic Planning
Authors: Robertson Justus , Young R.
Published: October 2022

Narrative mediation is a plan-based process that converts a planning problem into a mediation tree that models an interactive story. Probabilistic planning relaxes the classical planning constraint of deterministic action outcomes by containing domain operators with nondeterministic effects. This paper shows narrative mediation can be viewed as a probabilistic planning problem where the outcomes of user actions are modeled with nondeterministic effects in the domain. The paper gives a process for automatically converting a deterministic mediation planning problem into a probabilistic interactive narrative planning problem.
Mutual Implicit Question Answering for Shared Authorship: A Pilot Study on Player Expectations
Authors: Ware Stephen
Published: October 2022

The typical goal of an experience manager in an interactive narrative is to create a sense of shared authorship that lends the player freedom to personalize the experience while still meeting the author's constraints on structure. This can be difficult when the player and author only communicate with one another through their actions. Each new action causes new questions to arise, assumptions to be made, and old questions to be answered. In this paper, I propose a technique called Mutual Implicit Question Answering, or MIQA, designed to allow an experience manager to both perceive and influence the momentum of an interactive story. It combines a generative model of narrative planning with analytical models of question answering and salience. I also present the results of a small, qualitative study of how people construct interactive narratives that lends insight for the eventual evaluation of a MIQA experience manager.
Games as Co-Creative Cooperative Systems
Authors: Canaan Rodrigo
Published: October 2022

Many modern creative industrial processes rely on the collaboration between multiple humans, assisted by one or more computational systems, in a complex environment. However, most traditional systems lack the adaptability required to contribute in a flexible, co-creative manner, instead executing a fixed set of tasks in a preset time schedule. We believe games, especially cooperative games offer an ideal platform to conduct research in co-creativity. We present our motivation, preliminary work and future goals to study, build and measure game-inspired co-creative AI systems.
SHRDLU: A Game Prototype Inspired by Winograd’s Natural Language Understanding Work
Authors: Ontanon Santiago
Published: October 2022

This paper describes a game prototype called "SHRDLU" that explores the concept of designing a game around the ideas behind Winograd's original SHRDLU system. We briefly describe the main gameplay, as well as the natural language and inference architecture used by game NPCs.
Evolving Behaviors for an Interactive Cube-Based Artifact
Authors: Oliveira Victor , Do Nascimento Hugo , Soares Fabrizzio , Rocha Cleomar
Published: October 2022

In the present paper we explore the idea of combining computation power and the availability of ordinary art spectators in order to produce new interactive art works. This is investigated for a particular application, which consists of producing new behaviors for a programmable art apparatus named C3 Cubes. Given the nature of the problem and some difficult challenges to be dealt with, an Interactive Evolutionary Computation (IEC) approach was devised. Furthermore, it was necessary to adopt a surrogate function method for approximating the user's preferences and to implement a Web-based virtual simulation environment for speeding up the generation and the evaluation of C3 Cubes projects. The integration of all these elements is crucial for producing new user-guided cube projects with interesting behaviors. The main approaches experimented in this research and the proposed design solutions are useful to solving similar problems in other domain areas, for example, in the context of game design.
Nested-Greedy Search for Adversarial Real-Time Games
Authors: Moraes Rubens , Mariño Julian , Lelis Levi
Published: October 2022

Churchill and Buro (2013) launched a line of research through Portfolio Greedy Search (PGS), an algorithm for adversarial real-time planning that uses scripts to simplify the problem's action space. In this paper we present a problem in PGS's search scheme that has hitherto been overlooked. Namely, even under the strong assumption that PGS is able to evaluate all actions available to the player, PGS might fail to return the best action. We then describe an idealized algorithm that is guaranteed to return the best action and present an approximation of such algorithm, which we call Nested-Greedy Search (NGS). Empirical results on MicroRTS show that NGS is able to outperform PGS as well as state-of-the-art methods in matches played in small to medium-sized maps.
Exploratory Automated Analysis of Structural Features of Interactive Narrative
Authors: Partlan Nathan , Carstensdottir Elin , Snodgrass Sam , Kleinman Erica , Smith Gillian , Harteveld Casper , Seif El-Nasr Magy
Published: October 2022

Analysis of interactive narrative is a complex undertaking, requiring understanding of the narrative's design, its affordances, and its impact on players. Analysis is often performed by an expert, but this is expensive and difficult for complex interactive narratives. Automated analysis of structure, the organization of interaction elements, could help augment an expert's analysis. For this purpose we developed a model consisting of a set of metrics to analyze interactive narrative structure, enabled by a novel multi-graph representation. We implemented this model for an interactive scenario authoring tool called StudyCrafter and analyzed 20 student-designed scenarios. We show that the model illuminates the structures and groupings of the scenarios. This work provides insight for manual analysis of attributes of interactive narratives and a starting point for automated design assistance.
Player Experience Extraction from Gameplay Video
Authors: Luo Zijin , Guzdial Matthew , Liao Nicholas , Riedl Mark
Published: October 2022

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.
GIGL: A Domain Specific Language for Procedural Content Generation with Grammatical Representations
Authors: Chen Tiannan , Guy Stephen
Published: October 2022

We introduce a domain specific language for procedural content generation (PCG) called Grammatical Item Generation Language (GIGL). GIGL supports a compact representation of PCG with stochastic grammars where generated objects maintain grammatical structures. Advanced features in GIGL allow flexible customizations of the stochastic generation process. GIGL is designed and implemented to have direct interface with C++, in order to be capable of integration into production games. We showcase the expressiveness and flexibility of GIGL on several representative problem domains in grammatical PCG, and show that the GIGL-based implementations run as fast as comparable C++ implementation and with less code.
Modelling Player Understanding of Non-Player Character Paths
Authors: Zhang Mengxi , Verbrugge Clark
Published: October 2022

Modelling a player's understanding of NPC movements can be useful for adapting gameplay to different play styles. For stealth games, what a player knows or suspects of enemy movements is important to how they will navigate towards a solution. In this work, we build a uniform abstraction of potential player path knowledge based on their partial observations. We use this representation to compute different path estimates according to different player expectations. We augment our work with a user study that validates what kinds of NPC behaviour a player may expect, and develop a tool that can build and explore appropriate (expected) paths. We find that players prefer short simple paths over long or complex paths with looping or backtracking behaviour.
Combining Intentionality and Belief: Revisiting Believable Character Plans
Authors: Shirvani Alireza , Farrell Rachelyn , Ware Stephen
Published: October 2022

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.
A Design Pattern Approach for Multi-Game Level Generation
Authors: Beaupre Spencer , Wiles Thomas , Briggs Sean , Smith Gillian
Published: October 2022

Existing approaches to multi-game level generation rely upon level structure to emerge organically via level fitness. In this paper, we present a method for generating levels for games in the GVGAI framework using a design pattern-based approach, where design patterns are derived from an analysis of the existing corpus of GVGAI game levels. We created two new generators: one constructive, and one search-based, and compared them to a prior existing search-based generator. Results show that our generator is comparable, even preferred, over the prior generator, especially among players with existing game experience. Our search-based generator also outperforms our constructive generator in terms of player preference.
A Hybrid Approach to Planning and Execution in Dynamic Environments Through Hierarchical Task Networks and Behavior Trees
Authors: Neufeld Xenija , Mostaghim Sanaz , Brand Sandy
Published: October 2022

Intelligent autonomous agents that are acting in dynamic environmentsin real-time are often required to follow long-termstrategies while also remaining reactive and being able to actdeliberately. In order to create intelligent behaviors for videogame characters, there are two common approaches – plannersare used for long-term strategical planning, whereas BehaviorTrees allow for reactive acting. Although both methodologieshave their advantages, when used on their own, theyfail to fully achieve both requirements described above. Inthis work, we propose a hybrid approach combining a HierarchicalTask Network planner for high-level planning whiledelegating low-level decision making and acting to BehaviorTrees. Furthermore, we compare this approach with a pureplanner in a multi-agent environment.
Story Quality as a Matter of Perception: Using Word Embeddings to Estimate Cognitive Interest
Authors: Behrooz Morteza , Robertson Justus , Jhala Arnav
Published: October 2022

Storytelling is a capable tool for interactive agents and better stories can enable better interactions. Many existing automated evaluation techniques are either focused on textual features that are not necessarily reflective of perceived interestingness (e.g. coherence), or are domain-specific, relying on a priori semantics models (e.g. in a game). However, the effectiveness of storytelling depends both on its versatility to adapt to new domains and the perceived interestingness of its generated stories. In this paper, drawing from cognitive science literature, we propose and evaluate a method for estimating cognitive interest in stories based on the level of predictive inference they cause during perception.
Automatic Abstraction and Refinement for Simulations with Adaptive Level of Detail
Authors: Diamanti Michelangelo , Thue David
Published: October 2022

Optimizing the level of detail of an interactive simulation involves maximizing its perceived scope while minimizing the computational resources that are required to maintain it. Using varying levels of detail is common in computer graphics, but the challenges of doing so in simulations remain substantially less explored. The interactive simulations of video games often govern the behaviour of intelligent agents in the environment, and such behaviours can take substantial computational resources to maintain. As the ambitions of designers and players demand larger and more complex simulations, new strategies are needed to disassociate the perceived scope of a simulation with its computational needs. To this end, we propose a way to automatically adjust between different levels of detail in an interactive, narrative planning context, while simultaneously identifying and visualizing the elements that can currently be perceived.
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman
Authors: Gao Chao , Kartal Bilal , Hernandez-Leal Pablo , Taylor Matthew E.
Published: October 2022

How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for RL — past work has shown that model-free RL algorithms fail to achieve significant learning without artificially reducing the environment’s complexity. In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman. While model-free random exploration is typically futile, we develop a model-based automatic reasoning module that can be used for safer exploration by pruning actions that will surely lead the agent to death. We empirically demonstrate that this module can significantly improve learning.
Analysis of Statistical Forward Planning Methods in Pommerman
Authors: Perez-Liebana Diego , Gaina Raluca D. , Drageset Olve , İlhan Ercüment , Balla Martin , Lucas Simon M.
Published: October 2022

Pommerman is a complex multi-player and partially observable game where agents try to be the last standing to win. This game poses very interesting challenges to AI, such as collaboration, learning and planning. In this paper, we compare two Statistical Forward Planning algorithms, Monte Carlo Tree Search (MCTS) and Rolling Horizon Evolutionary Algorithm (RHEA) in Pommerman. We provide insights on how the agents actually play the game, inspecting their behaviours to explain their performance. Results show that MCTS outperforms RHEA in several game settings, but leaving room for multiple avenues of future work: tuning these methods, improving opponent modelling, identifying trap moves and introducing of assumptions for partial observability settings.
Pathfinding and Abstraction with Dynamic Terrain Costs
Authors: Sturtevant Nathan R. , Sigurdson Devon , Taylor Bjorn , Gibson Tim
Published: October 2022

Abstraction and refinement is a common approach used in games to improve the speed of pathfinding by planning in an abstract space and then refining abstract paths to traversable paths. While there are many variants of this approach that have been developed and studied, research on this problem has largely ignored the problem of pathfinding with terrain types, terrain costs, and dynamic terrain. This paper studies the problem of pathfinding in domains with terrain costs and proposes an abstraction approach that is built around handling terrain costs and dynamic terrain. The resulting approach is able to handle costs in a way that existing approaches do not, and provides a good balance between memory usage, path quality, and pathfinding speed.
SCOPE: Selective Cross-Validation over Parameters for Elo
Authors: Bisberg Alexander J. , Cardona-Rivera Rogelio E.
Published: October 2022

It is crucial to develop reusable methods for explaining and evaluating esports given their popularity and diversity. Quantifying skill in an esport has the potential to improve win prediction, matchmaking, and storytelling for these games. Arpad Elo’s skill modeling system for chess has been adapted to many games and sports. In each instance, the modeler is challenged with tuning parameters to optimize for some metric, usually accuracy. Often these approaches are one-off and lack consistency. We propose SCOPE, a framework that uses grid search cross-validation to select optimal parameters for Elo based on accuracy, calibration, or log loss. We demonstrate this method on a season of Call of Duty World League, a first-person shooter esport, and we demonstrate comparable performance to other more complex, state-of-the-art methods.
Role Identification for Accurate Analysis in Dota 2
Authors: Demediuk Simon , York Peter , Drachen Anders , Walker James Alfred , Block Florian
Published: October 2022

Esports is an organised form of video games played competitively. The esports industry has grown rapidly in recent years, with global audiences estimated at the hundreds of millions. One of the most popular esports formats is the Multi-Player Online Battle Arena (MOBA), which sees two teams of players competing. In MOBAs and other team-based games, individual players take on different roles or functions to help achieve victory for their team. MOBA characters can be played in different ways to align with team roles. However, most current esports analytics systems do not separate the data, such that each role is analysed separately. This is a problem because it is difficult to evaluate the performance of different roles with the same metrics. For example in football goals scored is a great metric for striker performance, but a poor one for goalkeeper performance. Using Dota 2 as a case study, we propose a method using ensemble clustering to classify and label individual roles for each hero in Dota 2. Rather than focusing on pre-existing roles defined by expert knowledge, we allow unsupervised learning to identify roles which each hero can play in an unbiased way. This work enables the separation of historical data for each hero, enabling a more accurate analysis to be performed by analytical tools.
A Study of AI Agent Commitment in One Night Ultimate Werewolf with Human Players
Authors: Eger Markus , Martens Chris
Published: October 2022

Social deduction games are a genre of board games in which a group of players is secretly assigned roles and each player tries to determine the other players’ roles. However, some roles have an incentive to not be found, and the games typically allow players to lie freely. Playing such games is a challenging task for AI agents, because they need to not only determine the probability that each statement made by the other players is truthful, but also come up with convincing lies themselves. In this paper, we present AI agents designed to play one particular such game, One Night Ultimate Werewolf, with human players. We discuss the different deliberation strategies our agents use to determine what they should say, and when they should change their plan. To determine how these different deliberation strategies are perceived by human players, we performed an experiment in which participants played a Unity implementation of the game with each of the three deliberation strategies. We present the results of this experiment, which show that commitment to plans has a measurable effect on player perception and provide a trade-off between consistency and potential for high performance of the agent.
Evaluation of a Recommender System for Assisting Novice Game Designers
Authors: Machado Tiago , Gopstein Daniel , Wang Angela , Nov Oded , Nealen Andrew , Togelius Julian
Published: October 2022

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase user’s productivity. We conducted quantitative studies that showed the recommender system increases users’ levels of accuracy and computational affect, and decreases their levels of workload.
Alignment of Player and Non-Player Character Assertiveness Levels
Authors: Pacheco António C. , Martinho Carlos
Published: October 2022

With the intent to broaden videogames’ audiences, and based on the Media Equation study on the law of similarity–attraction, we propose aligning the NPCs’ assertiveness level to the player’s own assertiveness level. We developed a testbed game, a 2D puzzle platformer with a companion NPC, with two versions of the NPC’s behavior, one for each end of the assertiveness scale. We conducted a 2x2, between-subjects experiment (n=48), in which Assertive and Non-Assertive subjects were randomly matched with one of the NPCs. Subjects recognized the NPC’s personality type, giving a significantly higher assertiveness score to the NPC endowed with assertive characteristics. Non-Assertive players reported significantly higher Tension scores when interacting with the Assertive NPC than when interacting with the Non-Assertive NPC. However, based on assertiveness level alignment, there was neither a significant difference in the enjoyment of the experience nor in the player’s affinity for the NPC.
A Plan-Based Personality Model for Story Characters
Authors: Shirvani Alireza , Ware Stephen G.
Published: October 2022

Believable virtual characters enhance the experience of interactive virtual worlds. BDI-based state-space narrative planners make their characters more compelling by granting them specific goals to pursue and different (possibly wrong) beliefs about the state of the world. However, such systems do not account for character individuality. We extend work on narrative planning by proposing a personality model based on the Big Five. This paper discusses how each factor of the Big Five is represented by narrative planning features. We also present a study to evaluate whether human readers can perceive the modeled personality traits through simple action patterns in short stories.
Adaptive Game Soundtrack Generation Based on Music Transcription
Authors: Makhmutov Munir
Published: October 2022

This research aims to bring new prospects for adaptive game soundtrack generation, which theoretically can increase the involvement of players into the gaming process. To investigate the problem of adaptive soundtrack generation, human-centered evaluation has to be done. Furthermore, an important part of this research is a collaborative soundtrack generation with the participation of composers and artificial intelligence, which is able to decrease expenses of game developing companies. For soundtrack customization, this work suggests a joint generation of music by human and artificial intelligence and its further intuitive modification during the game based on in-game actions.
Interactive Narrative Authoring Using Cognitive Models in Narrative Planning
Authors: Sanghrajka Rushit
Published: October 2022

My research aims to allow for human authors to collaborate with a computational system to work on narrative generation. This will be done with the help of work done in the domains of narrative understanding, narrative planning and reasoning based on cognitive models. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning.
Generating Explorable Narrative Spaces with Answer Set Programming
Authors: Dabral Chinmaya , Martens Chris
Published: October 2022

Previous approaches to narrative generation have required a new planner implementation for each set of constraints deemed relevant to the narrative domain, each consisting of thousands of lines of code and supporting one primary mode of interaction: fully specifying a domain and problem, and receiving a plan as output. We present a lightweight, flexible narrative planner written with Answer Set Programming, designed specifically to support constraint-based narrative generation, show how it generalizes previous approaches, and show how it can be easily extended with notions of thematic plot schema such as “betrayal.” Finally, we demonstrate how the ASP model can be explored through interactive question answering, where answers take the form of generated narratives. In the long term, we intend this work to support understanding of complex rule systems through interactive exploration.
A Declarative PCG Tool for Casual Users
Authors: Horswill Ian
Published: October 2022

Declarative programming allows designers to create procedural content generators by providing descriptions of desired artifacts rather than bespoke algorithms to generate them.  Unfortunately, these systems are notoriously inaccessible, requiring considerable sophistication with formal systems, and detailed understanding of the impact of equivalent formalizations on the system performance. Imaginarium is a declarative PCG system for tabletop role playing games (TTRPGs).  Following Compton’s work on casual creators, it trades expressiveness for accessibility.  As with Nelson’s Inform 7, its source language is a highly structured subset of English.  A single sentence, such as children are parented by at least one adult can be used to simultaneously introduce the predicates child, adult, and parent, along with a cardinality constraint over the parent relation.  We describe the system, its knowledge representation language, and the issues in their design.  Together, they allow users with minimal STEM background to engage in playful experimentation.
Germinate: A Mixed-Initiative Casual Creator for Rhetorical Games
Authors: Kreminski Max , Dickinson Melanie , Osborn Joseph , Summerville Adam , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

Digital games are hindered as an artform by significant technical barriers to entry, which exclude many would-be game developers from participating in this medium of expression. Casual creators for game design attempt to mitigate these barriers, but—like conventional game development tools—often require users to “work their way up” from low-level mechanics to high-level rhetorical or expressive goals, rather than allowing them to start with high-level rhetorical goals and “work their way down.” The constraint-based game generator Gemini is well-suited to the generation of games that meet high-level expressive goals, but is difficult for casual users to work with. We present Germinate, a mixed-initiative casual creator for rhetorical games that extends Gemini with a more approachable graphical user interface. A preliminary expert evaluation revealed that Germinate affords a playful approach to rhetorical game design by generating games that successfully meet user intent in surprising and novel ways.
A Formalization of Emotional Planning for Strong-Story Systems
Authors: Shirvani Alireza , Ware Stephen
Published: October 2022

Characters that are capable of expressing emotions seem more lifelike to a human audience. Interacting with emotional characters may cause a human user to feel empathy and to care for their fate. In turn, the audience may feel more engaged in the story. We take a step to address the lack of computational models of emotions for strong-story systems and propose to extend strong-story state-space narrative planners, already equipped with intentionality and belief, to reason about character emotions. Our proposed system is multi-agent, highly domain-independent, and focused on reasoning and decision making. In this paper, we evaluate to what extent our system can accurately model a set of emotions and in doing so, improve the believability of story characters.
The Unexpected Consequence of Incremental Design Changes
Authors: Sturtevant Nathan , Decroocq Nicolas , Tripodi Aaron , Guzdial Matthew
Published: October 2022

Human designers may find it difficult to anticipate the impact of small changes to some games, particularly in puzzle games. However, it is not difficult for computers to simulate all mechanical impacts of such small changes. This suggests that computers might be able to aid humans designers as they build and analyze game levels. This paper takes one step towards this larger goal by studying how Exhaustive Procedural Content Generation (EPCG) can be used for analysis of incremental changes of existing game levels. Using an incremental EPCG approach, we analyze all of the levels in the popular puzzle game Snakebird, showing that incremental variations in the level designs can significantly increase the length of the shortest possible solution. A user study on a subset of these modified levels shows that the modified levels are both interesting and challenging for humans to play. Thus, through the analysis of Snakebird, we demonstrate the broader potential for incremental applications of EPCG.
Differentia: Visualizing Incremental Game Design Changes
Authors: Chang Kenneth , Smith Adam
Published: October 2022

Small changes in the code and assets of a videogame can have drastic impacts on the player experience. The primary way to discover these impacts is with extensive human playtesting. However, the time and effort expended for playtesting is costly enough that it cannot be conducted after every incremental build of a game. We propose an information visualization technique that approximately reifies a game’s space of interactivity as a static image. Considering incremental changes to the game, our visualizations can suggest what has been effectively added, preserved, or removed from the space by those changes. These visual summary reports would ideally be used in the continuous integration (CI) software development process. We demonstrate a prototype implementation of this visualization on two commercial games, considering both minor and major design changes.
Say “Sul Sul!” to SimSim, A Sims-Inspired Platform for Sandbox Game AI
Authors: Charity Megan , Rajesh Dipika , Ombok Rachel , Soros L. B.
Published: October 2022

This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing divergent search algorithms. In this domain, which includes a minimal viability criterion, the goal is to furnish a house with objects that satisfy the physical needs of a simulated agent. Importantly, the large number of objects available to the player (whether human or automated) affords a wide variety of solutions to the underlying design problem. Empirical studies in a novel open source simulator called SimSim investigate the ability of novelty-based evolutionary algorithms to effectively generate viable environment designs.
Image-to-Level: Generation and Repair
Authors: Chen Eugene , Sydora Christoph , Burega Brad , Mahajan Anmol , Abdullah Abdullah , Gallivan Matthew , Guzdial Matthew
Published: October 2022

Procedural content generation via machine learning (PCGML) has recently gained research attention due to its ability to generate new game content with minimal user input. However, thus far those without machine learning expertise have been largely unable to use PCGML to generate content to fit their needs. This paper proposes the use of images as the input for a PCGML process to generate game levels. Intuitively, a user can submit an image, with the system returning the closest valid Super Mario Bros. game level. Our results indicate that at least for domains like Super Mario Bros. we can recreate a target level with high fidelity.
Co-Generation of Game Levels and Game-Playing Agents
Authors: Dharna Aaron , Togelius Julian , Soros L. B.
Published: October 2022

Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.
Murder Mysteries: The White Whale of Narrative Generation?
Authors: Eger Markus
Published: October 2022

The field of narrative generation appears to have a particular fascination with the genre of murder mysteries, perhaps because the genre’s defining characteristics include a clear, and predefined structure, a reliance on logic, and an immense popularity. However, while there have been several attempts to generate a satisfactory mystery story, most such efforts never made it beyond an initial prototype or idea. In this article we investigate what may draw researchers towards this genre, and then disseminate why the very properties that make murder mysteries such an appealing target also make it a particularly challenging domain. For the apparent rigid scaffolding structure provided by the genre, we discuss its flexibility; for the apparent hard grounding in logic we illuminate the deficiency of such; and for the seemingly clear communication maxims prescribed by written “rules” for the genre we explore which ones must be broken. Our goal is to show the actual scale of the difficulty of the problem, and which challenges have yet to be addressed. While it may seem that our focus is overly narrow, we will also explore the implications for narrative generation more broadly.
Studying General Agents in Video Games from the Perspective of Player Experience
Authors: Guerrero-Romero Cristina , Kumari Shringi , Perez-Liebana Diego , Deterding Sebastian
Published: October 2022

Research actively explores and advances the play strength of general agents, which are able to play video games without having specific knowledge about them. However, how general agents impact player experience and motivation when implemented in commercially viable games is largely unexplored. In this paper, we investigate this relationship as initial work towards linking general agent behaviour and player experience as a step towards making general agents applicable to commercial video games. Specifically, we created two versions of a simple competitive human-versus-agent game having two general Monte Carlo Tree Search (MCTS) agents with different behaviours. These agents, without having specific knowledge about the game, have two unique goals: i) maximising score; and ii) exploring (more suitable for the game we chose). We integrated these agents into a ’capture the flag’ game and conducted a study to investigate the effects on several player motivation components of the Intrinsic Motivation Inventory (IMI) and Player Experience of Need Satisfaction (PENS) scale. Enquiry in this direction opens up the possibilities to start analysing general agents from the perspective of the player’s journey.
Multimodal Player Affect Modeling with Auxiliary Classifier Generative Adversarial Networks
Authors: Henderson Nathan , Min Wookhee , Rowe Jonathan , Lester James
Published: October 2022

Accurately detecting player affect is an important component of player modeling. Multimodal approaches to player modeling have shown significant promise because of their capacity to provide a multi-dimensional perspective on player behavior. However, obtaining sufficient data for training multimodal models of player affect presents significant challenges, including the prevalence of noisy, unbalanced, or missing data generated by multimodal sensor systems. To address this problem, we introduce a multimodal player affect modeling framework that improves player affect detection by using Auxiliary Classifier Generative Adversarial Networks (AC-GANs). We demonstrate the use of a Wasserstein distance-based approach for filtering synthesized data created in a data augmentation framework, and we investigate the effectiveness of the AC-GAN discriminator as an alternative approach for detecting player affect. Results show that AC-GAN based affective modeling outperforms baseline methods while enhancing player models through synthetic data generation and improved affect detection.
Towards Action Model Learning for Player Modeling
Authors: Krishnan Abhijeet , Williams Aaron , Martens Chris
Published: October 2022

Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.
Contrast Motif Discovery in Minecraft
Authors: Saadat Samaneh , Sukthankar Gita
Published: October 2022

Understanding event sequences is an important aspect of game analytics, since it is relevant to many player modeling questions. This paper introduces a method for analyzing event sequences by detecting contrasting motifs; the aim is to discover subsequences that are significantly more similar to one set of sequences vs. other sets. Compared to existing methods, our technique is scalable and capable of handling long event sequences. We applied our proposed sequence mining approach to analyze player behavior in Minecraft, a multiplayer online game that supports many forms of player collaboration. As a sandbox game, it provides players with a large amount of flexibility in deciding how to complete tasks; this lack of goal-orientation makes the problem of analyzing Minecraft event sequences more challenging than event sequences from more structured games. Using our approach, we were able to discover contrast motifs for many player actions, despite variability in how different players accomplished the same tasks. Furthermore, we explored how the level of player collaboration affects the contrast motifs. Although this paper focuses on applications within Minecraft, our tool, which we have made publicly available along with our dataset, can be used on any set of game event sequences.
Exploring Level Blending Across Platformers via Paths and Affordances
Authors: Sarkar Anurag , Summerville Adam , Snodgrass Sam , Bentley Gerard , Osborn Joseph
Published: October 2022

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.