bluuebunny/crossref_metadata_2025 huggingface dataset:
Teología y pastoral de la mortificación cristiana
Authors: Rovira Germán
Published: October 2022

1. Mortificación y tarea mesiánica de los cristianos. 2. Penitencia y Santo Temor de Dios. 3. Reparación y principio de «sustitución» o «representación». 4. Ascética humana y ascética cristiana. 5. Formas de la mortificación corporal. 6. Constructivismos e individualismos. 7. Conclusión.
Misterio Trinitario, Misterio de Amor
Authors: Aranda Antonio
Published: October 2022

I. INTRODUCCIÓN: FACTORES DE NUESTRA REFLEXIÓN. II. REVELACIÓN TRINITARIA y TEOLOGÍA TRINITARIA: 1. Conocimiento del misterio revelado de Dios Trino: el misterio trinitario como objeto de conocimiento. 2. La finalidad del misterio revelado: el misterio trinitario como fin. 3. Contenido del misterio revelado de Dios Trino. III. ALGUNAS CONSIDERACIONES SOBRE LA HISTORIA DE LA TEOLOGÍA TRINITARIA. 1. Sobre los presupuestos históricos y doctrinales. 2. Sobre la fijación del dogma en Nicea y Constantinopla. 3. Sobre algunos desarrollos teológicos posteriores. IV. LA VIDA TRINITARIA: VIVIR EN DONACIÓN.
Los trabajos de la edición crítica
Authors: Rodríguez Pedro
Published: October 2022

SUMARIO: I. PLANTEAMIENTO Y LINEAS DE TRABAJO. 1. La idea de la edición crítica. 2. Los materiales de la edición crítica. II. ESTRUCTURA DE LA EDICIÓN CRITICA: 1. El «texto» del Catecismo Romano. 2. Documentación crítica del texto. 3. Historia literaria y teológica del texto. 4. La recepción teológica pastoral del Catecismo Romano. III. EL EQUIPO DE TRABAJO. IV. CUADROS SINOPTICOS: 1. Paginación de los manuscritos del Catecismo Romano. 2. Proceso redaccional del Catecismo Romano. 3. Datos técnicos sobre la documentación manuscrita.
La personalidad histórica del Fundador del Opus Dei
Authors: Orlandis José
Published: October 2022

SUMARIO: 1. Diez años después. 2. Escogido por Dios. 3. ¿Fundador?, ¿fundación? 4. Instrumento de Dios. 5. La llamada universal a la santidad. 6. La dignidad de la persona humana. 7. El sentido de la filiación divina. 8. Trabajo santificado y santificante. 9. Consecuencias sociales de la santidad. 10. Mensaje espiritual y fenómeno pastoral.
Ciencia, finalidad y existencia de Dios
Authors: Artigas Mariano
Published: October 2022

SUMARIO: 1. La «prueba del orden» en las obras de Santo Tomás: a) Orden natural y gobierno divino. b) La finalidad natural y la creación. c) La finalidad natural y el gobierno divino. d) Los argumentos sobre la finalidad natural. e) La «quinta vía». 2. El progreso científico y la «prueba del orden». 3. El «naturalismo metodológico»: a) las raíces griegas del naturalismo. b) El positivismo. c) Ecos actuales del positivismo. d) El «naturalismo científico». e) Los presupuestos de la ciencia experimental. f) El método científico y la «prueba del orden». 4. Evolucionismo y finalidad: a) El darwinismo como revolución naturalista. b) ¿Evolución dirigida? c) Ontogénesis y el filogénesis. d) Azar y necesidad. e) Evolución o creación: un falso dilema. 5. La racionalidad de la naturaleza.
La vocación en el Antiguo Testamento
Authors: Morales José
Published: October 2022

I. ELECCIÓN y VOCACIÓN: 1. El pueblo elegido. 2. Tradiciones de elección. 3. Llamadas ejemplares. 4. Llamamiento y misión. a) Vocación para una misión; b) Individuo y comunidad; c) Fuente de gracias; d) La santidad del elegido. II. LA VOCACIÓN DE LOS PROFETAS DE ISRAEL: 1. La llamada por la Palabra de Yahveh. a) Los relatos de vocaciones; b) La experiencia de la llamada; c) Una vocación singular e irresistible; d) La Palabra de Yahveh. 2. La libertad de los elegidos. a) Una personalidad entera; b) Acciones libres; c) El diálogo con Dios. 3. El Resto de Israel. a) El tema bíblico del Resto; b) Apostasía y castigo del pueblo elegido; c) Significado religioso de la idea del Resto de Israel.
La visión beatífica como realidad trinitaria
Authors: González-Alió José Luis
Published: October 2022

INTRODUCCIÓN. I. LA INFORMACIÓN INTELIGIBLE DEL INTELECTO BEATO POR LA ESENCIA DIVINA: 1. La información inteligible. 2. La Esencia divina hace de forma inteligible en la visión beatífica. 3. Todo intelecto creado es capax Dei. 4. La elevación del intelecto: el lumen gloriae. 5. El acto de visión. II. LA VISIÓN EN EL VERBO. EL CONOCIMIENTO DE LAS PERSONAS DIVINAS: 1. La función del verbo mental en la operación intelectual. 2. El Verbo de Dios y el conocimiento intratrinitario. 3. La visión beatífica como visión en el Verbo. 4. Gracia increada y gracia creada: la doble participación sobrenatural per operationem y per similitudinem. CONCLUSIÓN.
Ética, política y libertad
Authors: Colom Enrique
Published: October 2022

SUMARIO. I. DIOS Y EL CÉSAR. II. ORDEN SOCIAL Y ORDEN MORAL: 1. Dimensión ética de la actuación social. 2. El pecado, destructor del orden social. 3. Realización práctica y eficacia del orden moral. 4. Principios de la ética social. 5. Necesidad de las ciencias sociales. III. LIBERTAD Y ORDEN SOCIAL: 1. Insuficiencia de la liberación. 2. Libertad y vínculos. 3. Incorporación libre a los designios divinos. 4. La libertad como energía del progreso social. 5. La cooperación al bien. IV. LA ENSEÑANZA SOCIAL DEL MAGISTERIO: 1. La enseñanza moral del Magisterio de la Iglesia. 2. La competencia del Magisterio en el campo social. 3. Obligatoriedad de la Doctrina Social de la Iglesia. 4. Formación de la conciencia. V. DISTINCIÓN DE NIVELES: 1. Distinción de planos y libertades civiles. 2. Iglesia, Jerarquía y política. 3. Pluralismo de los fieles en materias sociopolíticas.
Aspectos teológicos del Prólogo de S. Juan
Authors: García-Moreno Antonio
Published: October 2022

SUMARIO: 1. El Logos joanneo. - 2. Origen del Prólogo. - 3. Un himno litúrgico. - 4. Substrato veterotestamentario. - 5. La gracia y la verdad. - 6. La Nueva Creación. - 7. El Verbo, la Palabra.
Dios Trino que se revela en Cristo
Authors: Izquierdo César
Published: October 2022

1. Revelación como autocomunicación de Dios.- 2 Se revela Dios que es Trinidad.- 3. Trinidad, contenido y forma de la Revelación. 3.1. Filosofía de la religión de Hegel 3.2. Filosofía de la revelación de Schelling. 3.3. La forma trinitaria de la revelación. 3.4. Contenido de la revelación.- 4. Cristo revelador y revelación de Dios. 4.1. Revelación en la historia. 4.2. Cristo-Hijo. 4.3. Cristo Verbo.- 5. Revelación y encarnación.- 6 Cristo revela el hombre al mismo hombre.- 7. Conclusión.
A Hierarchical Approach to Generating Maps Using Markov Chains
Authors: Snodgrass Sam , Ontanon Santiago
Published: October 2022

In this paper we describe a hierarchical method for procedurallygenerating maps using Markov chains. Ourmethod takes as input a collection of human-authoredtwo-dimensional maps, and splits them into high-leveltiles which capture large structures. Markov chains arethen learned from those maps to capture the structure ofboth the high-level tiles, as well as the low-level tiles.Then, the learned Markov chains are used to generatenew maps by first generating the high-level structure ofthe map using high-level tiles, and then generating thelow-level layout of the map. We validate our approachusing the game Super Mario Bros., by evaluating thequality of maps produced using different configurationsfor training and generation.
Analytics-Driven Dynamic Game Adaption for Player Retention in a 2-Dimensional Adventure Game
Authors: Harrison Brent , Roberts David
Published: October 2022

This paper shows how game analytics can be used to dynamically adapt a casual, 2-D adventure game named Sidequest: The Game (SQ:TG) in order to increase session-level retention. Our technique involves using game analytics to create an abstracted game analytic space to make the problem tractable. We then model player retention in this space and move through this space in accordance to a target distribution of game states in order to influence player behavior. Experiments performed show that the adaptive version of SQ:TG is able to better fit a target distribution of game states while also significantly reducing the quitting rate compared to the non-adaptive version of the game.
Global State Evaluation in StarCraft
Authors: Erickson Graham , Buro Michael
Published: October 2022

State evaluation and opponent modelling are important areasto consider when designing game-playing Artificial Intelligence.This paper presents a model for predicting whichplayer will win in the real-time strategy game StarCraft.Model weights are learned from replays using logistic regression.We also present some metrics for estimating player skillwhich can be used a features in the predictive model, includingusing a battle simulation as a baseline to compare playerperformance against.
High-Level Representations for Game-Tree Search in RTS Games
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we explore an approach to deploy game-tree search in RTS games by using game state abstraction, and explore the effect of using different abstractions over the game state. Different abstractions capture different parts of the game state, and result in different branching factors when used for game-tree search algorithms. We evaluate the different representations using Monte Carlo Tree Search in the context of StarCraft.
NPCs as Social Mediators in Massively Multiplayer Online Games
Authors: Crenshaw Nicole , Nardi Bonnie
Published: October 2022

Previous research demonstrates that social experience is a key motivation for continued play in online games. We argue that there is an untapped potential for NPCs to act as social mediators in online games by stimulating social interaction between players. For example, a social mediator NPC could ask in chat, “How is everyone’s day?” to encourage conversations between players. We suggest two potential applications for social mediator NPCs in online games by examining social experiences in two contemporary massively-multiplayer online role-playing games (MMOs): Guild Wars 2 and World of Warcraft.
Automated Generation of Conversational Non Player Characters
Authors: Pickett Grant , Khosmood Foaad , Fowler Allan
Published: October 2022

An integral part of social believability in role playing games is believability of non-player characters (NPC). In this paper we argue for the importance of believability in NPCs, even those that are completely outside of any pre-written quest or plot. We present NPCAgency, a system designed to generate many conversational NPCs as packaged narrative assets that can be shared and imported into various projects to increase story-world immersion. We believe such a system can help solve two problems. First, the authorial burden of the game designer is lessened, allowing renderings of large numbers of NPCs, each with their own unique background and conversation topics, all conforming to the norms of a predefined “universe”. Second, the immersive aspect of the game is heightened as the player can engage complex characters with lengthy dialogue affordances. We demonstrate the concept by generating fifty characters with attributes drawn from “Game of Thrones” (GOT) / “A Song of Ice and Fire” universe, and exporting them as Inform 7 code, a popular declarative interactive fiction (IF) programming language and authoring tool. A user study of thirty-seven Inform 7 programmers demonstrates that a 62% majority find the tool useful enough to consider for their own work. Further 70% said they would use the system to create “Game of Thrones” background characters for their own projects.
Fast and Diverse Narrative Planning through Novelty Pruning
Authors: Farrell Rachelyn , Ware Stephen
Published: October 2022

Novelty pruning is a simple enhancement that can be added to most planners. A node is removed unless it is possible to find a set of n literals which are true in the current state and have never all been true in any of that plan's previous states. Expanding on the success of the Iterated Width algorithm in classical planning and general game playing, we apply this technique to narrative planning. Using a suite of 8 benchmark narrative planning problems, we demonstrate that novelty pruning can be used with breadth-first search to solve smaller problems optimally and combined with heuristic search to solve larger problems faster. We also demonstrate that when many solutions to the same problem are generated, novelty pruning can produce a wider variety of solutions in some domains.
An Environment for Transforming Game Character Animations Based on Nationality and Profession Personality Stereotypes
Authors: Durupinar Funda , Wang Kuan , Nenkova Ani , Badler Norman
Published: October 2022

A vast body of literature has dealt with the challenges of creating the impression of human appearance and human-like motion in the animation of game characters. In this paper, we further refine these efforts by creating a flexible environment for animating game characters endowed with personality, which is a core descriptor of stable characteristics of human behavior and which is often expressed in human movement. We base our work on the Big Five personality traits, also known as OCEAN (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism). Our environment incorporates a procedural mapping from OCEAN personality traits to movement modifiers that alter existing motions in ways compatible with a desired personality. Using Amazon Mechanical Turk, we collected stereotypical personality profiles for 135 nationalities and 100 professions. We integrated these stereotypical personality expectations into an interactive interface in Unity3D. Users can linearly blend the nationality and profession OCEAN parameters and individually adjust them for specific characters or groups. The results are validated using Amazon Mechanical Turk pairwise judgments on character types based on movements.
A Programming Model for Boss Encounters in 2D Action Games
Authors: Siu Kristin , Butler Eric , Zook Alexander
Published: October 2022

Boss fights are a memorable and climatic point of many games. In this work we present a programming model for defining boss experiences in 2D action games. The domain we focus on is characterized by real-time movement through a continuous space in which the player and opposing boss damage each other through physical collisions, and player and boss behavior is governed primarily by finite state machines. Our programming model consists of primitive systems such as kinematic physics to store object state and detect collisions paired with finite state machines to define behavior. We describe our model and demonstrate its expressiveness with examples of three classic boss fights from games in The Legend of Zelda, Castlevania, and Sonic the Hedgehog. Our future goals include procedural generation of boss encounters, and we report on the research challenges involved in achieving this goal.
Practical Specification of Belief Manipulation in Games
Authors: Eger Markus , Martens Chris
Published: October 2022

Actions that affect knowledge asymmetrically between agents occur in numerous domains, from card games such as poker to the secure transmission of information. Applications in such domains often depend on reflection over knowledge, including what an agent knows about what other agents know. We are interested in enabling formal specification of these systems which may be used for executable prototyping as well as verification and other formal reasoning. Dynamic Epistemic Logic provides a formal basis for such reasoning, but is often prohibitively cumbersome to use in practice. We present an implementation and macro system called Ostari, backed by a particular flavor of Dynamic Epistemic Logic, which allows us to scale the ideas to more realistic problems. We demonstrate how actions that manipulate agents' beliefs can be written concisely and how this capability can be applied to modeling a popular card game by utilizing our system's ability to execute action sequences, answer queries about knowledge states, and find action sequences to satisfy a particular goal.
Dante Agent Architecture for Force-On-Force Wargame Simulation and Training
Authors: Hart Brian , Hart Derek , Gayle Russell , Oppel Fred , Xavier Patrick , Whetzel Jonathan
Published: October 2022

Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct “red versus blue” simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.
Towards a Computational Measure of Plot Tellability
Authors: Berov Leonid
Published: October 2022

Measuring the quality of plot is a desirable feature for computational narrative systems.One of the notions of plot quality used in narrative theory is called tellability, which can be derived from certain structural properties, namely the types of events present and the way they are connected.These structures include not only actualized events, but also take into account virtual plans and the affective valencies of events.The present paper introduces Marie-Laure Ryan's tellability principles and suggests to computationally model them using an affective multi-agent simulation system.It discusses how such an approach implies a broader understanding of plot than commonly assumed and analysis several existing narrative systems under these considerations.Furthermore, it introduces a plot-graph formalism that allows the computational representation and analysis of the extended plot understanding.An approach to automatically generating the plot-graph is suggested in the context of the introduced multi-agent simulation system.
Tracing Player Knowledge in a Parallel Programming Educational Game
Authors: Kantharaju Pavan , Alderfer Katelyn , Zhu Jichen , Char Bruce , Smith Brian , Ontanon Santiago
Published: October 2022

This paper focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called Parallel to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.
Design Mining for Minecraft Architecture
Authors: Yoon Euisun , Andersen Erik , Hariharan Bharath , Knepper Ross
Published: October 2022

3D construction sandbox games such as Minecraft have provided new opportunities for people to express their creativity. However, individual players have few tools to help them learn about architectural style or how to improve the structure they are building. Ideally, players could utilize tools that capitalize on the large numbers of 3D models built by others to offer guidance for their particular project. We trained a neural network to classify a large collection of Minecraft models from various websites in terms of style (Ancient, Asian, Medieval, or Modern). We present experimental results demonstrating that our model can classify the user-indicated style of a structure with 55% accuracy. We further demonstrate use of this model to highlight nearest neighbors to a specific query structure. We have integrated these tools into a Minecraft Mod that allows players to classify their structure's style and view nearest neighbors in real-time.
Affect-Based Early Prediction of Player Mental Demand and Engagement for Educational Games
Authors: Wiggins Joseph , Kulkarni Mayank , Min Wookhee , Mott Bradford , Boyer Kristy , Wiebe Eric , Lester James
Published: October 2022

Player affect is a central consideration in the design of game-based learning environments. Affective indicators such as facial expressions exhibited during gameplay may support building more robust player models and adaptation modules. In game-based learning, predicting player mental demand and engagement from player affect is a particularly promising approach to helping create more effective gameplay. This paper reports on a predictive player-modeling approach that observes player affect during early interactions with a game-based learning environment and predicts selfreports of mental demand and engagement at the conclusion of gameplay sessions. The findings show that automatically detected facial expressions such as those associated with joy, disgust, sadness, and surprise are significant predictors of players’ self-reported engagement and mental demand at the end of gameplay interactions. The results suggest that it is possible to create affect-based predictive player models that can enable proactively tailored gameplay by anticipating player mental demand and engagement.
Learning Strategies for Real-Time Strategy Games with Genetic Programming
Authors: Mariño Julian R. H.
Published: October 2022

Planning in real-time strategy (RTS) games is challenging due to their very large state and action spaces. Action abstractions have shown to be a promising approach for dealing with this challenge. Previous approaches induce action abstractions from a small set of hand-crafted strategies, which are used by algorithms to search only on the actions returned by the strategies. Previous works use a set of expert-designed strategies for inducing action abstractions. The main drawback of this approach is that it limits the agent behaviour to the knowledge encoded in the strategies. In this research, we focus on learning novel and effective strategies for RTS games, to induce action abstractions. In addition to being effective, we are interested in learning strategies that can be easily interpreted by humans, allowing a better understanding of the workings of the resulting agent.
Bringing Stories Alive: Generating Interactive Fiction Worlds
Authors: Ammanabrolu Prithviraj , Cheung Wesley , Tu Dan , Broniec William , Riedl Mark
Published: October 2022

Interactive fictions—also called text-based games—are games in which a player interacts with a virtual world purely through textual natural language. In this work, we focus on procedurally generating interactive fiction worlds. Generating these worlds requires (a) referencing everyday and thematic commonsense priors in addition to (b) being semantically consistent, (c) interesting, (d) coherent throughout, all while (e) producing fluent natural language descriptions of places, people, and things. Using existing story plots from books as inspiration, we present a method that first extracts a partial knowledge graph encoding basic information regarding world structure such as locations and objects. This knowledge graph is then automatically completed utilizing thematic knowledge and used to guide a neural language generation model that fleshes out the rest of the world. We evaluate generated worlds with human-participant studies, comparing our technique against rule-based and human-made baselines
TOAD-GAN: Coherent Style Level Generation from a Single Example
Authors: Awiszus Maren , Schubert Frederik , Rosenhahn Bodo
Published: October 2022

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.
Computer-Generated Music for Tabletop Role-Playing Games
Authors: Ferreira Lucas , Lelis Levi , Whitehead Jim
Published: October 2022

In this paper we present Bardo Composer, a system to generate background music for tabletop role-playing games. Bardo Composer uses a speech recognition system to translate player speech into text, which is classified according to a model of emotion. Bardo Composer then uses Stochastic Bi-Objective Beam Search, a variant of Stochastic Beam Search that we introduce in this paper, with a neural model to generate musical pieces conveying the desired emotion. We performed a user study with 116 participants to evaluate whether people are able to correctly identify the emotion conveyed in the pieces generated by the system. In our study we used pieces generated for Call of the Wild, a Dungeons and Dragons campaign available on YouTube. Our results show that human subjects could correctly identify the emotion of the generated music pieces as accurately as they were able to identify the emotion of pieces written by humans.
Are Strong Policies Also Good Playout Policies? Playout Policy Optimization for RTS Games
Authors: Yang Zuozhi , Ontañón Santiago
Published: October 2022

Monte Carlo Tree Search has been successfully applied to complex domains such as computer Go. However, despite its success in building game-playing agents, there is little understanding of general principles to design or learn its playout policy. Many systems, such as AlphaGo, use a policy optimized to mimic human expert as the playout policy. But are strong policies good playout policies? In this paper, we take a case study in real-time strategy games. We use bandit algorithms to optimize stochastic policies as both gameplay policies and playout policies for MCTS in the context of RTS games. Our results show that strong policies do not make the best playout policies, and that policies that maximize MCTS performance as playout policies are actually weak in terms of gameplay strength
Tiered State Expansion in Optimization Crosswords
Authors: Botea Adi , Bulitko Vadim
Published: October 2022

Crosswords puzzles continue to be a popular form of entertainment. In Artificial Intelligence (AI), crosswords can be represented as a constraint problem, and attacked with a combinatorial search algorithm. In combinatorial search, the branching factor can play a crucial role on the search space size and thus on the search effort. We introduce tiered state expansion, a completeness-preserving technique to reduce the branching factor. In problems where the successors of a state correspond to the values in the domain of a state variable selected for instantiation, the domain is partitioned into two subsets called tiers. The instantiation of the two tiers is performed at different times, with tier 1 first and tier 2 in a subsequent state. Before a tier-2 instantiation occurs, its set of applicable values can shrink substantially due to constraint propagation, with a corresponding reduction of the branching factor. We apply tiered state expansion to a constraint optimization problem modeled on the Romanian Crosswords Competition, empirically demonstrating a substantial improvement. Tiered state expansion allows finding a full solution, with an average CPU time of up to 1.2 minutes, to many puzzles that would otherwise time out after 4 hours.
Towards AI as a Creative Colleague in Game Level Design
Authors: Larsson Tinea , Font Jose , Alvarez Alberto
Published: October 2022

In Mixed-Initiative Co-Creative tools, the human is mostly in control of what will and can be created, delegating the AI to a more suggestive role instead of a colleague in the co-creative process. Allowing more control and agency for the AI might be an interesting path in co-creative scenarios where AI could direct and take more initiative within the co-creative task. However, the relationship between AI and human designers in creative processes is delicate, as adjusting the initiative or agency of the AI can negatively affect the user experience. In this paper, different degrees of agency for the AI are explored within the Evolutionary Dungeon Designer (EDD) to further understand MI-CC tools. A user study was performed using EDD with three varying degrees of AI agency. The study highlighted elements of frustration that the human designer experiences when using the tool and the behavior in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions and suggested further research.
A Review of Uncertainty for Deep Reinforcement Learning
Authors: Lockwood Owen , Si Mei
Published: October 2022

Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area.
Speaking with Your Sidekick: Understanding Situated Speech in Computer Role Playing Games
Authors: Gorniak Peter , Roy Deb
Published: October 2022

Speech and natural language are natural and convenient ways to interact with artificial characters. Current use of language in games, however, is limited to menu systems and interplayer communication. To achieve smooth linguistic communication with synthetic agents, research should focus on how language connects to the situation in which it occurs. Taking account of the physical scene (where is the speaker located, what is around her, when does she speak?) as well as the functional aspects of the situation (why did he choose to speak? What are his likely plans?) can disambiguate the linguistic signal in form and content. We present a game environment to collect time synchronized speech and action streams, to visualize these data and to annotate them at different stages of processing. We further sketch a framework for situated speech understanding on such data, taking into account aspects of the physical situation as well as the plans players follow. Our results show that this combination of influences achieves remarkable improvements over the individual situation models despite the very noisy and spontaneous nature of the speech involved. This work provides a basis for developing characters that use situated natural spoken language to communicate meaningfully with human players.
From Linear Story Generation to Branching Story Graphs
Authors: Riedl Mark , Young R.
Published: October 2022

Narrative is an important part of the way we interact with and make sense of the world. Interactive narrative systems tell stories in a virtual world in which the user is an interactive participant. Since the behaviors the user performs in the virtual world can affect the way in which a storyline unfolds, interactive narrative systems often use a branching story structure where non-interactive story presentations are interleaved with user decision points. An alternative approach — narrative mediation — represents story as a linear progression of events with anticipated user actions and system-controlled agent actions together in a partially-ordered plan. For every possible way the user can violate the story plan, an alternative story plan is generated. If narrative mediation is powerful enough to express the same interactive stories as systems that use branching story structures, then linear narrative generation techniques can be applied to interactive narrative generation with the use of narrative mediation. This paper sketches out a proof that narrative mediation is at least as powerful as acyclic branching story structures.
Increasing Replayability with Deliberative and Reactive Planning
Authors: Van Lent Michael , Riedl Mark , Carpenter Paul , McAlinden Ryan , Brobst Paul
Published: October 2022

Opponent behavior in today's computer games is often the result of a static set of Artificial Intelligence (AI) behaviors or a fixed AI script. While this ensures that the behavior is reasonably intelligent, it also results in very predictable behavior. This can have an impact on the replayability of entertainment-based games and the educational value of training-based games. This paper proposes a move away from static, scripted AI by using a combination of deliberative and reactive planning. The deliberative planning (or Strategic AI) system creates a novel strategy for the AI opponent before each gaming session. The reactive planning (or Tactical AI) system executes this strategy in real-time and adapts to the player and the environment. These two systems, in conjunction with a future automated director module, form the Adaptive Opponent Architecture. This paper describes the architecture and the details of the deliberative and reactive planning components.
Dynamic Game Balancing: an Evaluation of User Satisfaction
Authors: Andrade Gustavo , Ramalho Geber , Gomes Alex , Corruble Vincent
Published: October 2022

User satisfaction in computer games seems to be influenced by game balance, the level of challenge faced by the user. This work presents an evaluation, performed by human players, of dynamic game balancing approaches. The results indicate that adaptive approaches are more effective. This paper also enumerates some issues encountered in evaluating users' satisfaction, in the context of games, and depicts some learned lessons.
Predicting User Physiological Response for Interactive Environments: An Inductive Approach
Authors: McQuiggan Scott , Lee Sunyoung , Lester James
Published: October 2022

Affective reasoning holds significant potential for interactive digital entertainment, education, and training. Incorporating affective reasoning into the decision-making capabilities of interactive environments could enable them to create customized experiences that are dynamically tailored to individual users' ever changing levels of engagement, interest, and emotional state. Because physiological responses are directly triggered by changes in affect, biofeedback data such as heart rate and galvanic skin response can be used to infer affective changes. However, biofeedback hardware is intrusive and cumbersome in deployed applications. This paper proposes an inductive framework for automatically learning models of users’ physiological response from observations of user behaviors in interactive environments. These models can be used at runtime without biofeedback hardware to continuously predict users' physiological state directly from situational context in the interactive environment. Empirical studies with induced decision tree, naïve Bayes, and Bayesian Network physiological response models suggest that they may be sufficiently accurate for practical use.
A Guided Performance Interface for Augmenting Social Experiences with an Interactive Animatronic Character
Authors: Patel Seema , Bosley William , Culyba David , Haskell Sabrina , Hosmer Andrew , Jackson T. J. , Liesegang Shane= , Stepniewicz Peter , Valenti James , Zayat Salim , Harger Brenda
Published: October 2022

Entertainment animatronics has traditionally been a discipline devoid of interactivity. Previously, we brought interactivity to this field by creating a suite of content authoring tools that allowed entertainment artists to easily develop fully autonomous believable experiences with an animatronic character. The recent development of a Guided Performance Interface (GPI) has allowed us to explore the advantages of nonautonomous control. Our new hybrid approach utilizes an autonomous AI system to control low-level behaviors and idle movements, which are augmented by high-level processes (such as complex conversation) issued by a human operator through the GPI. After observing thousands of interactions between human guests and our animatronic character at SIGGRAPH 2005's Emerging Technologies Exhibition, we strongly feel that both autonomy and guided performance have important roles in interactive, entertainment robotics. Together, the autonomous system and the new Guided Performance Interface allow guests to experience extremely rich, believable, social experiences with robots using technology available today.
Improving Collaborative Pathfinding Using Map Abstraction
Authors: Sturtevant Nathan , Buro Michael
Published: October 2022

In this paper we combine recent pathfinding research on spatial abstractions, partial refinement, and space-time reserva- tions to construct new collaborative pathfinding algorithms. We first present an enhanced version of WHCA* and then show how the ideas from WHCA* can be combined with PRA* to form CPRA*. These algorithms are shown to effectively plan trajectories for many objects simultaneously while avoiding collisions, as the original WHCA* does. These new algorithms are not only faster than WHCA* but also use less memory.
Personality-Based Adaptation for Teamwork in Game Agents
Authors: Tan Chek , Cheng Ho-lun
Published: October 2022

This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted towards each other.
Automatically Generating Summary Visualizations from Game Logs
Authors: Cheong Yun-Gyung , Jhala Arnav , Bae Byung-Chull , Young R.
Published: October 2022

In this paper we describe a system called ViGLS (Visualization of Game Log Summaries) that generates summaries of gameplay sessions from game logs. ViGLS automatically produces visualization of the summarized actions that are extracted based on cognitive models of summarization. ViGLS is implemented using a service-oriented architecture, de-coupling the summarization methods from any particular game engine being used. The camera code libraries used in visualization are based on constraint based camera control approaches and, in our implementation, make use of the scripting layer of the Unreal game engine.
IMPLANT: An Integrated MDP and POMDP Learning AgeNT for Adaptive Games
Authors: Tan Chek Tien , Cheng Ho-lun
Published: October 2022

This paper proposes an Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture for adaptation in modern games. The modern game world basically involves a human player acting in a virtual environment, which implies that the problem can be decomposed into two parts, namely a partially observable player model, and a completely observable game environment. With this concept, the IMPLANT architecture extracts both a POMDP and MDP abstract model from the underlying game world. The abstract action policies are then pre-computed from each model and merged into a single optimal policy. Coupled with a small amount of online learning, the architecture is able to adapt both the player and the game environment in plausible pre-computation and query times. Empirical proof of concept is shown based on an implementation in a tennis video game, where the IMPLANT agent is shown to exhibit a superior balance in adaptation performance and speed, when compared against other agent implementations.
Bowyer: A Planning Tool for Bridging the Gap between Declarative and Procedural Domains
Authors: Cash Steven , Young R.
Published: October 2022

Traditionally, there have been two large obstacles faced in attempting to apply AI techniques to games and other virtual environments.  The first obstacle is the gap between the largely declarative representations used by many AI techniques and the largely procedural approaches used in virtual environments. The second obstacle is the gap between the skill sets and knowledge bases of the two domain experts with AI researchers often lacking experience using virtual environment APIs and development environments and virtual environments developers often lacking significant AI knowledge. In this paper we present Bowyer, a tool designed to address these two obstacles to the integration of AI planning algorithms into virtual environments. Bowyer bridges the gap between the declarative representations in a planning domain and the procedural framework of a virtual environment via the use of code generation techniques.  Bowyer’s functionality also allows planning researchers to integrate their planning research into virtual environments without the need to have extensive knowledge of virtual environment development.
Socially Consistent Characters in Player-Specific Stories
Authors: Thue David , Bulitko Vadim , Spetch Marcia , Webb Michael
Published: October 2022

In the context of interactive, virtual experiences, the use of personality models to maintain consistent character behaviour is becoming more widespread in both industry and academia. Most current techniques, however, are limited in one of three ways: either they overly restrict user actions, have a high cost for creating varied content, or rely on a representation that prohibits conveying complex content to the user.  Toward addressing these issues, we introduce Socially Consistent Role Passing, a mechanism for ensuring consistent character behaviour that leverages the design of PaSSAGE, an existing system for generating adaptive, interactive stories.  While results from previous human user studies have shown that PaSSAGE improves the enjoyment of players with little gaming experience, we present results from a new study showing that PaSSAGE's adaptive stories, augmented with Socially Consistent Role Passing, improve the enjoyment of all players versus a set of fixed-structure alternatives.
Behavior Learning-Based Testing of Starcraft Competition Entries
Authors: Blackadar Michael , Denzinger Jörg
Published: October 2022

In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro action sequences for individual game units, by adjusting the concept to the real-time requirements of StarCraft, and by using macros involving specific abilities of game units, our testing system was able to find either weaknesses or system crashes for all of the competition entries of the chosen scenarios. Additionally, by requiring a minimal margin with respect to surviving units, we were able to clearly identify the weaknesses of the tested AIs.
A Real-Time Concurrent Planning and Execution Framework for Automated Story Planning for Games
Authors: Vidal Eric Cesar Jr. , Nareyek Alexander
Published: October 2022

This paper presents a framework that facilitates communication between a planning system (“planner”) and a plan execution system (“executor”) to enable them to run concurrently, with the main emphasis on meeting the real-time requirements of the application domain. While the framework is applicable to general-purpose planning, its features are optimized for the requirements of automated story planning for games—with emphasis on monitoring player-triggered events and handling on-time (re-)generation of story assets such as characters, maps and scenarios. This framework subsumes the traditional interleaved planning-and-execution paradigm used in embedded continual planning systems and generalizes it to a non-embedded context, making the framework ideal for use with contemporary game architectures (e.g., multithreaded game engines, or games with subsystems communicating over a network).
Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative
Authors: Lee Greg , Bulitko Vadim , Ludvig Elliot
Published: October 2022

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.