bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategi Pengurus Masjid dalam Memakmurkan Masjid Al-Furqon di Haurgeulis Indramayu
Authors: Suhariyanti Suhariyanti , Sobirin Sobirin
Published: October 2022

To prosper the mosque requires a good strategy, with a good strategy the program of mosque activities can run effectively and efficiently to achieve the goal of broadcasting Islam and its prosperity. The purpose of the study was to find out how the mosque management strategy in prospering the Al-Furqon mosque in Haurgeulis Indramayu. This research is a field research. This study uses a qualitative research method, with a literature and empirical approach, interview data collection techniques, observation and documentation. The results of the research on the strategies used by mosque administrators in prospering the Al-Furqon Mosque are using socialization strategies for the congregation through a door-to-door approach, and social programs for the congregation so that the congregation can be directed to participate in activities held in the congregation. Al-Furqon Mosque.Keywords: Strategy; Mosque Management; Prospering Mosque. AbstrakUntuk memakmurkan masjid memerlukan strategi yang baik, dengan strategi yang baik program kegiatan masjid dapat berjalan efektif dan efisien untuk mencapai tujuan dalam menyiarkan Islam dan kemakmurannya. Tujuan penelitian untuk mengetahui bagaimana strategi pengurus masjid dalam memakmurkan masjid Al-Furqon di Haurgeulis Indramayu. Penelitian ini menggunakan metode penelitian Kualitatif, dengan pendekatan literatur dan empiris, teknik pengumpulan data wawancara, observasi dan dokumentasi. Hasil penelitian strategi yang digunakan oleh pengurus masjid dalam memakmurkan Masjid Al-Furqon adalah menggunakan strategi sosialisasi pada jama’ah melalui pendekatan dari rumah ke rumah, dan program sosial terhadap jama’ah sehingga jama’ah dapat diarahkan untuk mengikuti kegiatan-kegiatan yang diadakan di masjid Al-Furqon.Kata Kunci: Strategi; Pengurus Masjid; Memakmurkan Masjid
Strategy of The Service of Cooperatives and UKM Province of Sumsel Province in The Development of the Micro, Small and Medium Business Sector (UMKM) During The Covid-19 Pandemic
Authors: Choiriyah Choiriyah , Saprida Saprida , Sari Emilia
Published: October 2022

The Covid-19 pandemic has had an impact on economic instability, including Micro, Small, and Medium Enterprises (UMKM). In general, the majority of UMKMs experienced a decline in income and even went bankrupt due to the Covid-19 pandemic. Facing this, a strategy is needed for UMKMs to survive and be able to develop their business amid the Covid-19 pandemic. One of the main aspects that have an impact on UMKM actors is the decline in the number of sales which makes UMKMs' financial condition in a crisis. Restrictions on social activities make it difficult for UMKMs to move to develop a business scale if they only use these conventional methods. Business actors have taken various ways to respond to these difficult conditions to maintain their business. The lack of mass mobility around the market certainly hampers the promotion rate which affects the level of sales. Overcoming this, online marketing through the media is the right step that must be taken by business actors.Keywords: Strategy; Department of Cooperatives; UKM; Development of The Micro; Small and Medium Enterprise (Umkm) Sector; During The Covid-19 Pandemic AbstrakPandemi Covid-19 memberi dampak pada ketidakstabilan ekonomi, termasuk Usaha Mikro Kecil Menengah (UMKM). Secara umum, mayoritas dari pelaku UMKM mengalami penurunan pendapatan bahkan kebankrutan akibat pandemi Covid-19. Menghadapi hal demikian diperlukan strategi bagi UMKM untuk tetap bertahan dan dapat mengembangkan bisnisnya di tengah pandemi Covid-19. Aspek utama yang sangat berdampak bagi pelaku UMKM salah satunya adalah penurunan jumlah penjualan yang membuat kondisi keuangan UMKM dalam kondisi krisis. Pembatasan kegiatan sosial membuat UMKM sulit untuk bergerak mengembangkan skala usaha jika hanya memanfaatkan metode konvensional tersebut. Pelaku usaha telah melakukan berbagai cara merespon kondisi sulit ini untuk mempertahankan usahanya. Kurangnya mobilitas massa di sekitar pasar tentu menghambat laju promosi yang berpengaruh kepada tingkat penjualan. Mengatasi hal tersebut, pemasaran secara online melalui media merupakan langkah tepat yang harus dilakukan oleh para pelaku usaha.Kata Kunci: Strategi; Dinas Koperasi; UKM; Pengembangan Sector Usaha Mikro Kecil dan Menengah (UMKM); Masa Pandemic Covid-19
Economic efficiency of biochar as an amendment for Acacia mangium Willd. plantations
Authors: Reyes Moreno Giovanni , Barrientos Fuentes Juan Carlos , Darghan Contreras Enrique
Published: October 2022

Biochar is a product of pyrolysis obtained from any type of biomass and can be used as a soil amendment or conditioner, improving the physical, chemical, and biological properties of the soil. Additionally, it can serve as an alternative to the application of synthetic fertilization in forest species such as Acacia mangium Willd. This research was oriented towards the determination of the economic efficiency of the use of biochar in A. mangium compared to the use of synthetic fertilizers. Production costs of wood and by-products, income and profits from forestry, economic efficiency of capital (cost-benefit ratio), labor (wood production per worker), and land (wood production ha-1) were considered. We found that the production of wood using biochar increased by 47% per unit area (ha), by 23% per unit of work (worker), and increased earnings by approximately one million Colombian pesos ha-1 compared to the use of only synthetic fertilizers.
Use of reduced Bokashi doses is similar to NPK fertilization in iceberg lettuce production
Authors: Lima Danilo Pezzoto de , Fregonezi Gustavo Adolfo de Freitas , Hata Fernando Teruhiko , Ventura Maurício Ursi , Resende Juliano Tadeu Vilela de , Wanderley Christina da Silva , Figueiredo Alex
Published: October 2022

The aim of the study was to compare horticultural variables of iceberg lettuce using two Bokashi compost doses, alone and in combination, with the bioactivator Penergetic (Penergetic International AG®) against standard mineral fertilization (NPK) in three crop cycles. Experiments were conducted in a plastic greenhouse at the Universidade Estadual de Londrina, Brazil (548 m a.s.l.). The treatments were: negative control (water, only); positive control (NPK, 4-14-8); Penergetic alone; Bokashi doses 5 g and 10 g/plant with or without a combination with Penergetic. Lettuce plants were grown in pots filled with soil. Commercial biomass (CM), head diameter (HD), plant height (PH) and chlorophyll index (CI) were evaluated. In the three cycles tested, the Bokashi 10 g/plant, Bokashi 10 g/plant + Penergetic, and NPK treatments surpassed the control. The studied variables (CM, HD, PH and CI) in the lower Bokashi dose treatment were also higher than controls but lower than higher Bokashi doses and NPK. Penergetic increases the CM in the lower Bokashi dose treatment just in the first production cycle, probably due to the poor organic matter content in the soil. The treatment Bokashi 10 g/plant improved significantly the lettuce horticultural variables vs. control treatments and was similar to chemical fertilization in two or three crop cycles.
Eclesiología y misionología en el siglo XVIII
Authors: Illanes José Luis
Published: October 2022

SUMARIO: 1. Hacia la constitución de un nuevo tratado: a) Inicios del tratado «devera religione»; b) La aparición del «de locis»; c) La constitución de la llamada Teología Fundamental clásica. 2. El lugar del tratado «de Ecclesis» en los manuales del siglo XVIII 3. Contenido del atractus de Eclesian: a) El cristianismo en el contexto de las religiones; b) La Iglesia instrumento de salvación. 4. Eclesiología y misionología.
Teología y santidad. Reflexiones sobre la razón de ser del teólogo a la luz del magisterio de S.S el Papa Juan Pablo II
Authors: Rico José
Published: October 2022

La vocación eclesial del teólogo al servicio de la evangelización y la teología vivida de los santos son temáticas presentes de forma ininterrumpida en el magisterio de Juan Pablo II desde el inicio de su Pontificado. El presente artículo pretende reflexionar sobre la identidad propia del teólogo en la Iglesia, tomando como guía las intervenciones más destacadas del Santo Padre a este propósito.
Comunicación de malas noticias en medicina: un estudio exploratorio
Authors: Bascuñán M. Luz , Roizblatt A. , Roizblatt D.
Published: October 2022

La comunicación de malas noticias es una práctica inherente a la medicina. Las guías elaboradas para realizar esta tarea poseen un gran valor aún cuando constituyen orientaciones globales que deben ser adaptadas a cada situación particular considerando la idiosincrasia cultural propia de los actores sociales. En este contexto el presente estudio se propone explorar las prácticas de comunicación de malas noticias en distintos servicios hospitalarios de Santago-Chile con el objeto de contribuir a la formación médica y a la elaboración de políticas de salud locales. Materiales y métodos: Se utilizó una metodología cualitativa consistente en tres grupos focales con una muestra total de 33 médicos de dos hospitales de Santiago-Chile.

Resultados: la comunicación de malas noticias es una práctica frecuente para la cual no se ha recibido formación y que genera preocupación e interés en los distintos servicios hospitalarios. No se dispone de un procedimiento acordado de comunicación de malas noticias. Cada profesional ha desarrollado sus estrategias a partir de la experiencia. El informar al paciente es visto como un deber ético pero en muchos casos éste es interferido por la presión familiar. Se distinguen tipos de malas noticias y estrategias empleadas, así como aspectos facilitadores y obstaculizadores de este proceso comunicacional.

Conclusión: La vivencia del profesional es un factor central en la comunicación de malas noticias por lo que el desarrollo de mecanismos de auto-cuidado constituye un importante desafío.
Oral health of homebound older adults followed by primary care: a cross sectional study
Authors: Oliveira Thayná Ferreira Simões de , Embaló Bubacar , Pereira Mateus Cardoso , Borges Samara Cristina , Mello Ana Lúcia Schaefer Ferreira de
Published: October 2022

Abstract Objective to characterize the life, health, and oral health conditions of homebound older people registered in the primary care teams and oral homecare provided. Method cross-sectional, household based study with a convenience sample, in Florianópolis, southern Brazil. Data collection through a questionnaire and clinical oral examination which included sociodemographic variables, condition of teeth and mucosa, oral hygiene, access to dental care and dentist providing homecare. Absolute and relative frequency analysis and bivariate analysis (chi-square, CI=95%) were performed. Results 123 older people participated with mean age of 81.3 years, 62.6% were women. Living with a caregiver were 87%, 60% were domiciled for up to 5 years, and 89.4% were frail. Regarding the presence of teeth, 56.1% were edentulous and 40.5% had from 1 to 8 teeth. Root remains were observed in 12.8%, untreated caries lesions in 25.2%, visible biofilm in 69.9%, tooth mobility in 57.7% and mucosal lesions in 8.9% of the elders; 45.5% needed help with oral hygiene and 24.4% did not perform daily mouth cleaning. The difficulty in accessing dental care due to homeboundness was reported by 32.5% and home visits provided by the dentist occurred in only 16.3%. Conclusion the oral health of the older adults studied is poor due to the presence of oral problems that require intervention. There is dependence on third parties for oral care, which is not consistently guaranteed at home. The study points to the need for dental homecare provided by public health services.
Nutritional interventions for older adults in palliative care: a scoping review
Authors: Moura Raquel Bezerra Barbosa de , Barbosa Janine Maciel , Gonçalves Maria da Conceição Rodrigues , Lima Andréa Márcia da Cunha , Mélo Cláudia Batista , Piagge Carmem Sílvia Laureano Dalle
Published: October 2022

Abstract Objective To analyze the nutritional interventions adopted in older people in palliative care found in the literature. Method A scoping review was conducted involving a search of the following databases: PubMed, LILACS, CINAHL, Scopus, Web of Science, EMBASE and of the gray literature through Google Scholar, OpenGrey and ProQuests & Theses Global, without restrictions on publication date or language. The searches were performed using the descriptors and keywords, combined using Boolean operators AND and OR: “Nutritional Intervention”, “Intervenção Nutricional”, “Palliative Care”, “Cuidados Paliativos”, “Aged” and “Idosos”. Results Of the 5,942 studies found, 13 studies were selected. The backward citation search strategy identified 13 additional studies, giving a final total of 26 studies. Nutritional interventions adopted in older people in palliative care predominantly comprised nutritional counseling, oral nutritional supplementation and artificial nutrition through enteral and parenteral nutrition. These interventions focused on quality of life, symptom management and nutritional status. Conclusion Although there are gaps in the literature regarding nutritional interventions for older adults in palliative care, the importance of the role of nutritionists in promoting quality of life and relieving suffering of this population is clear.
Glaive: A State-Space Narrative Planner Supporting Intentionality and Conflict
Authors: Ware Stephen , Young R. Michael
Published: October 2022

Glaive is a state-space planner based on Hoffmann and Nebel's Fast-Forward which solves the narrative planning problem defined by Riedl and Young — to construct a plan which achieves the author's goals out of steps which are clearly motivated and goal-oriented toward individual character goals. Glaive reasons about how characters cooperate and conflict based on causal structures and possible worlds. By leveraging the unique constraints of narrative planning, Glaive reduces its branching factor and calculates a more accurate heuristic. We evaluate it on 8 narrative planning problems and demonstrate that it can solve certain non-trivial problems in under 1 second.
Games as Conversation
Authors: Cardona-Rivera Rogelio , Young Robert
Published: October 2022

We present a metaphor through which to study games: games as conversation, which casts gameplay as a communicative exchange between player and game. We propose to view aspects of gameplay as speech acts, as defined by Austin and Searle, and we present several examples that illustrate the diverse locutionary, illocutionary, and perlocutionary acts present in the design of digital games. Through our perspective, we are able to cast problems relevant to the interactive entertainment community as discourse problems, where an interactive system must determine what to “say,” in order to elicit in the minds of players a specific mental model that will allow them to perform successfully in the game. We conclude with a research agenda that proposes to leverage the artificial intelligence paradigm of discourse planning to tackle the discourse problems of interactive entertainment.
MKULTRA (Demo)
Authors: Horswill Ian
Published: October 2022

MKULTRA is an experimental game that explores novel AI-based game mechanics. Similar in some ways to text-based interactive fiction, the player controls a character who interacts with other characters through dialog.  Unlike traditional IF, MKULTRA characters have simple natural language understanding and generation capabilities, sufficient to answer questions and carry out simple tasks.  The game explores a novel game mechanic, belief injection, in which players can manipulate the behavior of NPCs by injecting false beliefs into their knowledge bases.  This allows for an unusual form of puzzle-based gameplay, in which the player must understand the beliefs and motivational structure of the characters well enough to understand what beliefs to inject.
Automated Gameplay Generation from Declarative World Representations
Authors: Robertson Justus , Young R. Michael
Published: October 2022

An open area of research for AI in games is how to provide unique gameplay experiences that present specialized game content to users based on their preferences, in-game actions, or the system's goals. The area of procedural content generation (PCG) focuses on creating or modifying game worlds, assets, and mechanics to generate tailored or personalized game experiences. Similarly, the area of interactive narrative (IN) focuses on creating or modifying story worlds, events, and domains to generate tailored or personalized story experiences. In this paper we describe a game engine that utilizes a PCG pipeline to generate and control a range of gameplay experiences from an underlying IN experience management construct.
Symbolic Plan Recognition in Interactive Narrative Environments
Authors: Cardona-Rivera Rogelio , Young Robert
Published: October 2022

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the intended narrative experience and potentially mediate or eliminate them. The process of observing player actions and attempting to come up with an explanation for those actions (i.e. the plan that the player is trying to carry out) is the problem of plan recognition. We adopt the framing of narratives as plans and leverage recent advances that cast plan recognition as planning to develop a symbolic plan recognition system as a proof-of-concept model of a player's reasoning in an interactive narrative environment. In this paper we outline the system architecture, report on performance metrics that demonstrate adequate performance for non-trivial domains, and discuss the implications of treating players as plan recognizers.
Culturally Appropriate Behavior in Virtual Agents: A Review
Authors: AlSaleh Mashael , Romano Daniela
Published: October 2022

Social behavior cannot be considered without the culture in which it is expressed. The following is a concise state of the art review of intelligent virtual agents displaying culturally appropriate behavior in games and serious games. In particular, it focuses on agents displaying personality and emotion, and their ability to engage in social interactions with others. The relationship between the characters’ external representation and the cultural believability is highlighted; and the internal and visual aspects of the current state of the art agents are discussed. A schematic view of the literature and the elements required for embodied culturally appropriate agents is presented, offering opportunities for future research.
Rock, Paper, StarCraft: Strategy Selection in Real-Time Strategy Games
Authors: Tavares Anderson , Azpúrua Hector , Santos Amanda , Chaimowicz Luiz
Published: October 2022

The correct choice of strategy is crucial for a successful real-time strategy (RTS) game player. Generally speaking, a strategy determines the sequence of actions the player will take in order to defeat his/her opponents. In this paper we present a systematic study of strategy selection in the popular RTS game StarCraft. We treat the choice of strategy as a game itself and test several strategy selection techniques, including Nash Equilibrium and safe opponent exploitation. We adopt a subset of AIIDE 2015 StarCraft AI tournament bots as the available strategies and our results suggest that it is useful to deviate from Nash Equilibrium to exploit sub-optimal opponents on strategy selection, confirming insights from computer rock-paper-scissors tournaments.
Mixed Reality Meets Procedural Content Generation in Video Games
Authors: Azad Sasha , Saldanha Carl , Gan Cheng-Hann , Riedl Mark
Published: October 2022

The use of artificial intelligence and procedural content generation algorithms in mixed reality games is an unexplored space. We posit that these algorithms can enhance the gameplay experience in mixed reality games. We present two prototype games that use procedural content generation to design levels that make use of the affordances in the player’s physical environment. The levels produced can be tailored to a user, customizing gameplay difficulty and affecting how the player moves around the real-world environment.
Balancing Zero-Sum Games with One Variable per Strategy
Authors: Liu Albert , Marschner Steve
Published: October 2022

A key challenge in game design is achieving balance between the strategies available to the players. Traditionally this has been done through playtesting, with its difficult requirements of time, labor, and interpretation of results. To make it quicker and easier to balance games, we propose a game-theoretic approach that automatically balances strategies based on a mathematical model of the game. Specifically, we model the balance problem as modifying a zero-sum game, using one variable per strategy, so that every strategy has an incentive to be employed. We begin with a special case where these variables affect player payoffs multiplicatively, and show that the simple Sinkhorn-Knopp algorithm can be used to balance the game. We then proceed to analyze the more general case where the variables have a monotonic effect on payoffs, and show that it is amenable to standard optimization methods. We give examples inspired by well-known game series including Pokemon and Warhammer 40,000.
Bardic: Generating Multimedia Narratives for Game Logs
Authors: Barot Camille , Branon Michael , Cardona-Rivera Rogelio , Eger Markus , Glatz Michelle , Green Nancy , Mattice James , Potts Colin , Robertson Justus , Shukonobe Makiko , Tateosian Laura , Thorne Brandon , Young R.
Published: October 2022

In this paper we present the system called Bardic, which was developed over three years as the core technology in the Narrative for Sensemaking project, an effort to automatically generate narrative from low-level event data as an aid for sense making. At its core, Bardic is a narrative report generator that uses a logic-based language torepresent a story based on event/activity logs. Bardic generates different types of narrative discourse designed to convey different aspects of the underlying data. The system consists of aUnity application that allows users to explore generated stories and select parts of the stories to analyze in detail. In addition to its narrative generation capabilities, the application provides visualization and querycapabilities. In this paper we provide screen shots of the application in use, show examples of narrative output produced by Bardic, detail how Bardic generates its output, and discuss the current system's capabilities and limitations.
The Shape of Story: A Semiotic Artistic Visualization of a Communal Storytelling Experience
Authors: Long Duri , Gupta Sanjana , Anderson Jessica , Magerko Brian
Published: October 2022

Telling stories is a central part of human culture. The development of computational systems that can understand and respond to human language is an integral part of AI research in general and narrative technologies in particular. In this paper, we describe a system that is able to understand human spoken English sentences and portray that understanding via a semiotic visual language in real-time. We then employ this system in a communal storytelling environment as part of an interactive art installation and create a medium for collaborative creative expression.
Postmortem: MKULTRA, An Experimental AI-Based Game
Authors: Horswill Ian
Published: October 2022

Games are inherently situated within the cultures of their players. Players bring a wide range of knowledge and expectations to a game, and the more the game suggests connections to that culture, the stronger those expectations are and/or the more problematic they can be. MKULTRA is an experimental, AI-heavy game that ran afoul of those issues. It’s interesting to hear a talk about or to see demonstrated by the author, but frustrating for players who do not already understand its internals in some detail. In this paper, I will give a postmortem of the game, in the rough style of industry postmortems from venues such as Gamasutra or GDC. I will discuss the goals and design of the game, what went right, what went wrong, and what I should have done instead. In my discussions of the game’s problems, I’ll focus on the ways in which it frustrated the players’ cultural expectations, and what we can learn from them for the design of future games.
Modeling Player Engagement with Bayesian Hierarchical Models
Authors: Sawyer Robert , Rowe Jonathan , Azevedo Roger , Lester James
Published: October 2022

Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
Evaluating AI-Based Games through Retellings
Authors: Kreminski Max , Samuel Ben , Melcer Edward , Wardrip-Fruin Noah
Published: October 2022

We propose a new approach to the human-centered evaluation of AI-based games, grounded in the analysis of player retellings of their play experiences. Retellings offer unique insight into dimensions of player experience that can be hard to get at through existing evaluation methods, such as the typical narrative structures that tend to emerge in the player’s mind when they play a particular game; the variety of subjectively experienced narratives that are possible and probable within a particular game; and the ways in which a game supports, or fails to support, the player’s process of narrativization. We used a grounded theory methodology to analyze retellings of play experiences in Civilization VI, Stellaris, and two distinct versions of the research game Prom Week. We also interviewed the creators of several retellings to gain insight into the subjective experience of story construction in collaboration with these games.
GenerationMania: Learning to Semantically Choreograph
Authors: Lin Zhiyu , Xiao Kyle , Riedl Mark
Published: October 2022

Beatmania is a rhythm action game where players must reproduce some of the sounds of a song by pressing specific controller buttons at the correct time. In this paper we investigate the use of deep neural networks to automatically create game stages—called charts—for arbitrary pieces of music. Our technique uses a multi-layer feed-forward network trained on sound sequence summary statistics to predict which sounds in the music are to be played by the player and which will play automatically. We use another neural network along with rules to determine which controls should be mapped to which sounds. We evaluated our system on the ability to reconstruct charts in a held-out test set, achieving an F1-score that significantly beats LSTM baselines.
Improving Deep Reinforcement Learning in Minecraft with Action Advice
Authors: Frazier Spencer , Riedl Mark
Published: October 2022

Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypothesize that interactive machine learning (IML), wherein human teachers play a direct role in training through demonstrations, critique, or action advice, may alleviate agent susceptibility to aliasing. However, interactive machine learning is only practical when the number of human interactions is limited, requiring a balance between human teacher effort and agent performance. We conduct experiments with two reinforcement learning algorithms which enable human teachers to give action advice—Feedback Arbitration, and Newtonian Action Advice—under visual aliasing conditions. To assess potential cognitive load per advice type, we vary the accuracy and frequency of various human action advice techniques. The training efficiency, robustness against infrequent and inaccurate advisor input, and sensitivity to aliasing are examined.
A Structured Analysis of Experience Management Techniques
Authors: Mori Giulio , Thue David , Schiffel Stephan
Published: October 2022

As a field of study, Experience Management spans a set of technology that is becoming increasingly relevant in applications that aim to improve the experiences of their users. Given the youth of the field, however, few attempts have been made to identify and discuss the common elements of systems that manage user experiences as they occur. In this paper, we consider a subset of existing AI experience managers in the context of a shared conceptual framework. We offer directly comparable summaries of the managers that we discuss, and we highlight how some of them use different technologies to perform similar tasks. While many experience managers remain undiscussed, we nonetheless demonstrate that distinct, well-defined components exist across a diverse set of managers, and that a modular method for building new managers might well be within reach.
Creativity Support for Story Construction Play Experiences
Authors: Kreminski Max
Published: October 2022

My research involves the design and development of mixed-initiative AI tools that provide players with creativity support in the context of story construction play experiences, especially those driven by malleable simulations that the player has a chance to help design or configure. To that end, I also study existing storytelling practices within game communities as a way of understanding what a desirable computational creative partner might look like; what features of computational systems tend to facilitate and frustrate creativity in their human partners; and what new creative practices might emerge as we create computational creative partners for new domains.
Your Buddy, the Grandmaster: Repurposing the Game-Playing AI Surplus for Inclusivity
Authors: Aytemiz Batu , Shu Xueer , Hu Eric , Smith Adam
Published: October 2022

Advances in artificial intelligence (AI) techniques have resulted in immense breakthroughs in how well we can algorithmically play videogames. Yet, this increased investment in game-playing AI (GPAI) techniques has not translated into a tangible improvement in the game-playing experience for our players. This paper is inspired by the positive impact of accessibility modes in recent games as well as previous calls in games research literature to focus on player experience. Responding to these calls, we propose utilizing GPAI techniques not to beat the player, as is traditionally done, but to support them in fully experiencing the game. We claim that utilizing GPAI agents to help players overcome barriers is a productive way of repurposing the capabilities of these agents. We further contribute a design exercise to help developers explore the space of possible GPAI-driven assistance methods. This exercise helps developers discover types of challenges and ideate methods that vary in magnitude and assistance type. We first apply this design exercise to explore the design space of possible assistance methods for the action platformer game Celeste. We then implement two of the discovered methods that target different challenge types in a Unity clone of Celeste. Through this implementation, we discover several additional research questions we must answer before GPAI-driven assistance methods can be truly effective. We believe this research direction furthers the discussion on how to utilize GPAI in service of the player experience and also contributes to the creation of more inclusive games.
Using Domain Compilation to Add Belief to Narrative Planners
Authors: Christensen Matthew , Nelson Jennifer , Cardona-Rivera Rogelio
Published: October 2022

Using domain compilation, we present a narrative planning system that is capable of creating narrative plans that use both character intention and character beliefs. We introduce a model capable of representing character beliefs in PDDL domains. This model allows characters to fail at actions when their beliefs about the world differ from the actual world state. Domains of this type can be compiled into purely intentional domains, and fed as input to intentional planners. The resulting stories feature characters that pursue their own intentions based on their own knowledge of the world, learn from mistakes to update their beliefs, and communicate information to each other. These types of stories are not possible with purely intentional domains.
Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games
Authors: Ling Carlos , Tollmar Konrad , Gisslén Linus
Published: October 2022

In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB) as input to be classified into one of five defined classes, normal image, or one of four different kinds of glitches (stretched, low resolution, missing and placeholder textures). Using a supervised approach, we train a ShuffleNetV2 using generated data. This work focuses on detecting texture graphical anomalies achieving arguably good performance with an accuracy of 86.8%, detecting 88% of the glitches with a false positive rate of 8.7%, and with the models being able to generalize and detect glitches even in unseen objects. We apply a confidence measure as well to tackle the issue with false positives as well as an effective way of aggregating images to achieve better detection in production. The main use of this work is the partial automatization of graphical testing in the final stages of video game development.
Dynamic Guard Patrol in Stealth Games
Authors: Al Enezi Wael , Verbrugge Clark
Published: October 2022

Guard patrols are common in stealth games, both for a sense of realism and to form a basic challenge to acting out covert behaviour. Automated patrol route construction, however, is difficult, as simple approaches based on randomized movement or goal selection tend to result in unrealistic and overly repetitive guarding motion, and poor area coverage. In this work we introduce an approach based on modelling location “staleness” in a manner similar to occupancy maps, but applied to both a grid representation and a dynamically constructed coverage-visibility mesh. The latter provides a continuous abstraction of the game environment which can be efficiently used and maintained for real-time decision making. We use Unity3D and multiple game levels to compare our grid and mesh-based results, showing the advantages of the visibility mesh over the grid-based approach.
Generating Game Levels for Multiple Distinct Games with a Common Latent Space
Authors: Kumaran Vikram , Mott Bradford , Lester James
Published: October 2022

Generative adversarial networks (GANs) are showing significant promise for procedural content generation (PCG) of game levels. GAN models generate game levels by mapping a low dimensional latent space to game levels in the game space. An intriguing challenge in GAN-based PCG is enabling GANs to produce game levels for multiple distinct games with similar gameplay characteristics using a common underlying low-dimensional representation. In this paper, we present a method for training a novel GAN-based PCG architecture that generates levels in multiple distinct games, starting from a common gameplay action sequence. We evaluate the solvability of the generated games using an automated playing agent and show how the generated game levels are separate representations of the same gameplay by quantifying the similarity between the solution action sequences for the game levels. By probing the common latent space, we show how our approach provides control over the levels generated in distinct games for the presence of desired gameplay patterns in the generated game levels. Results also demonstrate that the GAN-based PCG approach creates novel game levels in multiple distinct games, as indicated by the distance between the action sequences required to solve the game levels.
AI Design Lessons for Social Modeling at Scale
Authors: Robison Ethan , Viglione Matthew , Zubek Robert , Horswill Ian
Published: October 2022

City of Gangsters is a commercial strategy game in which the player is
principally challenged by navigating a complex and large-scale social network,
in which every action resonates through the network and holds associated risks
and rewards.

Our procedurally-generated city results in a large social network with
randomized and non-prescriptive configurations. Gameplay is oriented around
social reciprocity and engaging in the same via an understanding of a concise
set of social norms. Addressing these problems in a video game required a close
unity between AI design and game design.

We present four key AI design and implementation lessons learned in developing
and shipping this game: the paramount need to make social actions and their
consequences legible, the need for reversible actions, the need for modeled
social norms to comprise a succinct set, and the need for individuals to be
fungible with one another vis-a-vis social actions. We conclude with a
description of the design affordances of this approach.
Representational Requirements for a Plan Based Approach to Automated Camera Control
Authors: Jhala Arnav , Young R.
Published: October 2022

Automated camera control has been an active area of research for a number of years. The problem has been addressed in the Graphics, AI and Game communities from different perspectives. The main focus of the research in the Graphics community has been frame composition and coherence. The AI community has focused on intelligent shot selection, and the Games community strives for real- time cinematic camera control. While the proposed solutions in each of these fields are promising, there has not been much effort spent on listing out the requirements of an intelligent camera control system and how these can be satisfied through a combination of approaches taken from these different fields. This paper attempts to list out the representational requirements with a view of finding a unifying representation for combining these disparate approaches. We show how a plan based approach can capture some of these requirements and it can be connected to a geometric constraint solver for camera placement.
SquadSmart — Hierarchical Planning and Coordinated Plan Execution for Squads of Characters
Authors: Gorniak Peter , Davis Ian
Published: October 2022

This paper presents an application of Hierarchical Task Network (HTN) planning to a squad-based military simulation. The hierarchical planner produces collaborative plans for the whole squad in real time, generating the type of highly coordinated behaviours typical for armed combat situations involving trained professionals. Here, we detail the extensions to HTN planning necessary to provide real-time planning and subsequent collaborative plan execution. To make full hierarchical planning feasible in a game context we employ a planner compilation technique that saves memory allocations and speeds up symbol access. Additionally, our planner can be paused and resumed, making it possible to impose a hard limit on its computation time during any single frame. For collaborative plan execution we describe several synchronization extensions to the HTN framework, allowing agents to participate in several plans at once and to act in parallel or in sequence during single plans. Overall, we demonstrate that HTN planning can be used as an expressive and powerful real-time planning framework for tightly coupled groups of in-game characters.
Self-Validated Behaviour Trees through Reflective Components
Authors: Llansó David , Gómez-Martín Marco , González-Calero Pedro
Published: October 2022

Developing the AI for non-player characters in a video game is a collaborative task between programmers and designers. Most of the times, there is a tension between the freedom that designers require to include their narrative in the game and the effort required from programmers to debug faulty AI specified by good story tellers who are not programmers.In this paper is presented an architecture for building the AI of an NPC that extends the component-based approach, which represents the functionality of an entity as a collection of functionality-specific components. By associating an action in a behaviour tree with a collection of components, and equipping those components with some reflection capabilities, we are able to identify faulty behaviour trees at design time.
A Multi-Agent Potential Field-based Bot for a Full RTS Game Scenario
Authors: Hagelback Johan , Johansson Stefan
Published: October 2022

Computer games in general, and real time strategy games in particular is a challenging task for both AI research and game AI programmers. The player, or AI bot, must use its work- ers to gather resources. They must be spent wisely on struc- tures such as barracks or factories, mobile units such as sol- diers, workers and tanks. The constructed units can be used to explore the game world, hunt down the enemy forces and destroy the opponent buildings. We propose a multi-agent architecture based on artiﬁcial potential ﬁelds for a full real time strategy scenario. We validate the solution by participat- ing in a yearly open real time strategy game tournament and show that the bot, even though not using any form of path planning for navigation, is able to perform well and win the tournament.
AIIDE 2010 StarCraft Competition
Authors: Weber Ben
Published: October 2022

In 2010, AIIDE will host the StarCraft AI competition. This event enables researchers to evaluate their systems in a commercial real-time strategy game. Participants are given the task of building the best performing bot for StarCraft: Brood War, which was released by Blizzard Entertainment in 1998. The goals of the competition are to provide a testbed for AI and to promote game AI research by exhibiting techniques in an immensely popular game. It includes participants from several universities, industry professionals, and StarCraft enthusiasts.
AI for Herding Sheep
Authors: Cowling Peter , Gmeinwieser Christian
Published: October 2022

Shepherding with a dog presents an interesting challenge for artificial intelligence, with multiple intelligent systems assessing and interacting with each other in order to achieve a variety of goals. We present a solution to this problem, which consists of a dog AI making use of influence mapping, state machines and A* pathfinding to respond intelligently to real-life shepherding commands issued by a high-level shepherd AI steering the flock of sheep through waypoints on a variety of maps by using pathfinding and influence maps. The role of the AI shepherd can also be taken by a human player (using either a point and click or voice recognition interface) for matches against the artificial shepherd which proved to be a worthy opponent for human testers. The system was evaluated through user testing and provided a high degree of realism and engaging gameplay relying heavily on the workings of the presented AI components.
Applying Goal-Driven Autonomy to StarCraft
Authors: Weber Ben , Mateas Michael , Jhala Arnav
Published: October 2022

One of the main challenges in game AI is building agents that can intelligently react to unforeseen game situations. In real-time strategy games, players create new strategies and tactics that were not anticipated during development. In order to build agents capable of adapting to these types of events, we advocate the development of agents that reason about their goals in response to unanticipated game events. This results in a decoupling between the goal selection and goal execution logic in an agent. We present a reactive planning implementation of the Goal-Driven Autonomy conceptual model and demonstrate its application in StarCraft. Our system achieves a win rate of 73% against the built-in AI and outranks 48% of human players on a competitive ladder server.
Polymorph: A Model for Dynamic Level Generation
Authors: Jennings-Teats Martin , Smith Gillian , Wardrip-Fruin Noah
Published: October 2022

Players begin games at different skill levels and develop their skill at different rates—so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we have created Polymorph, which employs techniques from level generation and machine learning to understand level difficulty and player skill, dynamically constructing levels for a 2D platformer game with continually-appropriate challenge. We present the results of the user study on which Polymorph's model of level difficulty is based, as well as a discussion of the unique features of the model. We believe Polymorph creates a play experience that is unique because the changes are both personalized and structural, while also providing an example of a new application of machine learning to aid game design.
Goal Recognition with Markov Logic Networks for Player-Adaptive Games
Authors: Ha Eun , Rowe Jonathan , Mott Bradford , Lester James
Published: October 2022

Goal recognition is the task of inferring users’ goals from sequences of observed actions. By enabling player-adaptive digital games to dynamically adjust their behavior in concert with players’ changing goals, goal recognition can inform adaptive decision making for a broad range of entertainment, training, and education applications. This paper presents a goal recognition framework based on Markov logic networks (MLN). The model’s parameters are directly learned from a corpus of actions that was collected through player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with multiple solution paths.