bluuebunny/crossref_metadata_2025 huggingface dataset:
La fórmula «Resurreción de la carne» y su significado para la moral cristiana
Authors: O'Callaghan Paul
Published: October 2022

SUMARIO. I. El substrato teológico de la fórmula resurrección de la carne. 1. La resurrección de la carne ¿expresión anti-gnóstica?. 2. La resurrección de la carne, ¿muestra de la helenización del mensaje cristiano?. 3. La resurrección de la carne, ¿expresión quiliasta?. 4. La resurrección de la carne, la universalidad de la resurrección y el juicio final. II. La resurrección de la carne en los Símbolos de la fe. III. La fórmula resurrección de 'esta carne' en los Símbolos de la fe. IV. Síntesis y conclusión: el valor moral de la fórmula «huius carnis resurrectio».
La historiografía de la escuela tomista: el caso Gilson
Authors: Bonino Serge Thomas
Published: October 2022

I. LA CRÍTICA GILSONIANA DE LA ESCUELA TOMISTA. A) DE SANTO TOMÁS A LOS TOMISTAS. 1. Del maestro a los discípulos. 2. El caso del tomismo. B) «CORRUPTORIUM TOMAE» 1. El anticayetanismo de Gilson. 2. Génesis y estructuras del sistema cayetaniano. a) El desconocimiento del principio. b) La demasía aristotélica. c)El filosofismo. C) Los ANTECEDENTES.n. LAS POSICIONES DE LA CRÍTICA. A) PRESENTE y FUTURO DEL TOMISMO. 1. Cayetano y el neotomismo. 2. El porvenir del tomismo. B) EL BUEN EMPLEO DE LA ESCUELA. 1. Primado de Santo Tomás. 2. Compañeros de camino 3. Báñez o el anticayetano.
A Study on China's Sustainable Foreign Trade Development Plan in the Era of the 4th Industrial Revolution
Authors:   Korea Association for International Commerce and Information, Long Yong-Qi , Lee Seo-Young
Published: October 2022

As the 4th industrial revolution brought new international competition patterns, China's foreign trade was faced with changes in the trade environment, trade structure, and trade patterns. The purpose of this study is to present a sustainable foreign trade development plan for China in the era of the 4th industrial revolution. Accordingly, this study analyzed the changes in China's foreign trade following the 4th industrial revolution and suggested the following foreign trade development plans based on this. First, in response to changes in the trade environment, China should strengthen its trade competitiveness by optimizing its innovation system by promoting the protection of intellectual property and fostering scientific and technological talents. Second, in order to respond to changes in the trade structure, China should optimize its digital trade structure and promote the digitization of service trade. Third, in response to changes in trade patterns, China needs to build a safe trade platform, expand investment in human capital, and build a global networking ecosystem.
A Study on the Shipper’s Duty to Inform of Dangerous Cargo under Carriage of Goods by Sea
Authors:   Korea Association for International Commerce and Information, Kim Young-Ju
Published: October 2022

To ship dangerous goods, the shipper is required to prepare a form certifying that the cargo has been packed, labeled and declared according with the IMDGC or SOLAS. But the carriage of dangerous goods raises two main problems: the responsibility of the carrier for loss of the dangerous cargo itself; and the responsibility of the shipper to the ship and the carrier for damage caused by the dangerous goods. In order to determine the foregoing responsibilities one must ascertain in each case whether the carrier and the shipper knew or should have known of the dangerous nature of the cargo.
Under the common law, the shipper’s warranty as to the suitability of the goods for carriage is modified if the carrier knew or should have known of the dangerous cargo. Also, in the civil law such as Germany, France, and Japan, the shipper’s obligation to the carrier to tender goods which are suitable for carriage is contractual obligation and it would seem to be an absolute one. In this respect, the shipper's duty to inform dangerous goods is important in order to set the standard for the cargo claim.
This paper discusses the subject of shipping dangerous cargo at common law and civil law. Especially, the paper reviews and analyzes some issues as to shipper’s duty to inform dangerous goods in the carriage of goods by sea. By examining theories and researching cases, it presents current problems in the Commercial Code of Korea. Finally, it proposes certain further amendments of the Code.
A Study on FDI and Technology Export: From Korea to Vietnam
Authors:   Korea Association for International Commerce and Information, Baek Eunyoung
Published: October 2022

The paper puts an in-depth study on Korea's technology trade competitiveness with Vietnam, Korea's No. 1 foreign direct investment and the No.1 technology trade surplus country. In addition, it investigates the impact on technology exports according to the type of investment and the purpose of investment in Vietnam when making foreign direct investments. The trade specialization index was used as an analysis tool to analyze the technology competitiveness of specific industries, and multiple regression analysis was applied to examine the causal relationship between foreign direct investment and technology exports by its type and purpose.
Firstly, a result of the analysis of the trade specialization index reveals that Korea's technology competitiveness with Vietnam was high across all industry sectors. Still, the technological competitiveness of the apparel, accessories, and fur industries is weakening every year.
Also, Korea's technological competitiveness with Vietnam continues to decline in non-metallic minerals and basic etals from the material industry, which is the basis for high-tech sectors such as the semiconductor industry. On the other hand, a result of regression analysis suggests that the technology exports from Korea have increased in the form of joint investment and decreased when Korean companies invested with the aim of cost reduction when entering the Vietnamese market.
This study identifies Korea's strong technology competitiveness with Vietnam in each specific industry, but the competitiveness has been weakening in some industries like apparel and material industries. It is also significant in that the impact on domestic technology exports varies depending on the purpose and type of foreign direct investment in Vietnam. It affects Korea's technology exports accordingly.
Hierarchical Adversarial Search Applied to Real-Time Strategy Games
Authors: Stanescu Marius , Barriga Nicolas , Buro Michael
Published: October 2022

Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutionsare limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets (such as a complete instance of the StarCraft RTS game). This paper presents a hierarchical adversarial search framework which more closely models the human way of thinking --- much like the chain of command employed by the military. Each level implements a different abstraction --- from deciding how to win the game at the top of the hierarchy to individual unit orders at the bottom. We apply a 3-layer version of our model to SparCraft ---a StarCraft combat simulator --- and show that it outperforms state of the art algorithms such as Alpha-Beta, UCT, and Portfolio Search in large combat scenarios featuring multiple bases and up to 72 mobile units per player under real-time constraints of 40 ms per search episode.
Playable Experiences at AIIDE 2014
Authors: Sturtevant Nathan , Orkin Jeff , Zubek Robert , Cook Michael , Ware Stephen , Stith Christian , Young R. , Wright Phillip , Eiserloh Squirrel , Ramirez-Sanabria Alejandro , Bulitko Vadim , Lord Kieran
Published: October 2022

AIIDE 2014 is the second AIIDE event that has featured a playable experience track. This paper describes the seven entries that were accepted in the 2014 track, as well as the motivation behind the track and the criteria used to evaluate and accept entries.
Hide and Sneak: Story Generation with Characters that Perceive and Assume
Authors: Ten Brinke Hans , Linssen Jeroen , Theune Mariët
Published: October 2022

We describe the design of a perception system for the characters in the Virtual Storyteller (VST), a character-centric story generation system. Previously, these characters were omniscient; stories involving sneaking and deception could not be generated. To remedy this, we limited the characters' visual perception using simple rules. We enabled the characters to make assumptions about the story world, so they can plan toward goals in spite of incomplete knowledge. Using the distinction between the character and actor roles of agents in the VST, we can use the assumptions to steer the story plot.
Gameplay as On-Line Mediation Search
Authors: Robertson Justus , Young R.
Published: October 2022

Mediation is a plan-based interactive narrative generation algorithm that creates a cascading policy of plans. This policy represents a branching story and can be used by an execution manager in a game to control the series of events that unfold. With a few modifications we show that mediation's search space can embed all possible traversals through a game world. This shift allows gameplay to be modeled as an on-line mediation search where the game's interface is a representation of the underlying graph traversal. In this paper we outline these modifications and present a text-based implementation.
Guided Music Synthesis with Variable Markov Oracle
Authors: Wang Cheng-i , Dubnov Shlomo
Published: October 2022

In this work the problem of guided improvisation is approached and elaborated; then a new method, Variable Markov Oracle, for guided music synthesis is proposed as the first step to tackle the guided improvisation problem. Variable Markov Oracle is based on previous results from Audio Oracle, which is a fast indexing and recombination method of repeating sub-clips in an audio signal. The newly proposed Variable Markov Oracle is capable of identifying inherent datapoint clusters in an audio signal while tracking the sequential relations among clusters at the same time. With a target audio signal indexed by Variable Markov Oracle, a query-matching algorithm is devised to synthesize new music materials by recombination of the target audio matched to a query audio. This approach makes the query-matching algorithm a solution to the guided music synthesis problem. The query-matching algorithm is efficient and intelligent since it follows the inherent clusters discovered by Variable Markov Oracle, creating a query-by-content result which allows numerous applications in concatenative synthesis, machine improvisation and interactive music system. Examples of using Variable Markov Oracle to synthesize new musical materials based on given music signals in the style of Jazz are shown.
Motivation, Microdrives and Microgoals in Mockingbird
Authors: Lynch Michael
Published: October 2022

This paper is a work-in-progress report about Mockingbird, an intelligent musical agent (IMA) based on Sun's Clarion cognitive architecture (Sun 2003). In the first part we present the Clarion architecture and the manner in which its Motivation Subsystem models drive states and goals. In the second part we propose a potential structure for modeling fine-grained secondary drives in the context of a free improvisational performance.
Probabilistic Foundations for Procedural Level Generation
Authors: Snodgrass Sam
Published: October 2022

Procedural content generation (PCG) has become a popular research topic in recent years, but not much work has been done in terms of generalized content generators,that is, methods that can generate content for a wide variety of games without requiring hand-tuning. Probabilistic approaches are a promising avenue for creating more general content generators, and specificially map generators. I am interested in exploring probabilistic techniques that could lead to generalized procedural level generators.
Puppet Search: Enhancing Scripted Behavior by Look-Ahead Search with Applications to Real-Time Strategy Games
Authors: Barriga Nicolas , Stanescu Marius , Buro Michael
Published: October 2022

Real-Time Strategy (RTS) games have shown to be very resilient to standard  adversarial tree search techniques. Recently, a few approaches to tackle their complexity have emerged that use game state or move abstractions, or both. Unfortunately, the supporting experiments were either limited to simpler RTS environments (uRTS, SparCraft) or lack testing against state-of-the-art game playing agents. Here, we propose Puppet Search, a new adversarial search framework based on scripts that can expose choice points to a look-ahead search procedure. Selecting a combination of a script and decisions for its choice points represents a move to be applied next. Such moves can be executed in the actual game, thus letting the script play, or in an abstract representation of the game state which can be used by an adversarial tree search algorithm. Puppet Search returns a principal variation of scripts and choices to be executed by the agent for a given time span. We implemented the algorithm in a complete StarCraft bot. Experiments show that it matches or outperforms all of the individual scripts that it uses when playing against state-of-the-art bots from the 2014 AIIDE StarCraft competition.
Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces
Authors: Churchill David , Buro Michael
Published: October 2022

Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art for AI in modern strategy video games which have large state and action spaces. The first is a novel method for performing hierarchical search using a portfolio of algorithms to reduce the search space while maintaining strong action candidates. The second contribution is an overall AI architecture for strategy video games using this portfolio search method. The proposed methods are used as the AI system for Prismata, an online turn-based strategy game by Lunarch Studios. This system is evaluated using three experiments: on-line play vs.~human players, off-line AI tournaments to test the relative strengths of the AI bots, and a survey to determine user satisfaction of the system so far. Our result show that this system achieves a skill level in the top 25% of human players on the ranked ladder, can be modified quickly to create different difficulty settings, is robust to changes in game unit properties, and creates an overall AI experience which is user rated more enjoyable than those currently found in similar video games.
Automatic Learning of Combat Models for RTS Games
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or "simulator") of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several approaches to learn such combat model from replay data to models generated by hand. We use StarCraft, a Real-Time Strategy (RTS) game, as our application domain. Specifically, we use a large collection of already collected replays, and focus on learning a combat model for tactical combats.
Exploring the Use of Role Model Avatars in Educational Games
Authors: Kao Dominic , Harrell D. Fox
Published: October 2022

Research has indicated that role models have the potential to boost academic performance. In this paper, we describe an experiment exploring role models as game avatars in an educational game. Of particular interest are the effects of these avatars on players' performance and engagement. Participants were randomly assigned to a condition: a) user selected role model avatar, or b) user selected shape avatar. Results suggest that role models are heavily preferred. African American participants had higher game affect in the role model condition. South Asian participants had higher self-reported engagement in the role model condition. Participants that completed <= 1 levels had higher performance in the role model condition. General trends suggest that the role model's gender and racial closeness with the player, could play a role in player performance and self-reported engagement as consistent with the social science literature.
Sarah and Sally: Creating a Likeable and Competent AI Sidekick for a Videogame
Authors: Cerny Martin
Published: October 2022

Creating reasonable AI for sidekicks in games has proven to be a difficult challenge synthetizing player modelling and cooperative planning, both being problems hard by themselves. In this paper, we experiment with designing around these problems: we propose a cooperative puzzle-platformer game that was designed to look similarly to the mainstream of the genre, but to allow for an easy implementation of a quality sidekick AI, letting us test player reactions to the AI. The game was designed so that it is easy for the AI to find optimal solutions while the problem is relatively hard for a human player. We gathered survey responses from players who played the game online (N=28). While the AI sidekick was reported as likeable and helpful, players still reported greater enjoyment of the game when they were allowed to control the sidekick themselves. These findings indicate that the AI itself is not the only obstacle to truly enjoyable gameplay with an AI sidekick.
A Tripartite Plan-Based Model of Narrative for Narrative Discourse Generation
Authors: Barot Camille , Potts Colin , Young R. Michael
Published: October 2022

This paper presents a tripartite model of narrative in which three different kinds of actions are modeled: story actions, discourse actions, and narration actions. We argue that this model can support particularities of narrative discourse where typical discourse models do not (e.g., ambiguity/change of beliefs), and accounts for the difference in expressivity of the media used to convey the narrative, as opposed to bipartite models.
Object-Model Transfer in the General Video Game Domain
Authors: Braylan Alexander , Miikkulainen Risto
Published: October 2022

A transfer learning approach is presented to address the challenge of training video game agents with limited data. The approach decomposes games into objects, learns object models, and transfers models from known games to unfamiliar games to guide learning. Experiments show that the approach improves prediction accuracy over a comparable control, leading to more efficient exploration. Training of game agents is thus accelerated by transferring object models from previously learned games.
Improving Terrain Analysis and Applications to RTS Game AI
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

This paper presents a new terrain analysis algorithm for RTS games. The proposed algorithms significantly improves the analysis time of the state of the art via contour tracing, and also offers better chokepoint detection. We demonstrate that our approach (BWTA2) is at least 10 times faster than the commonly used BWTA in a collection of StarCraft maps. Additionally, we show the usefulness of terrain analysis in tasks such as pathfinding and discuss potential applications to strategic decision making tasks.
Deep Static and Dynamic Level Analysis: A Study on Infinite Mario
Authors: Guzdial Matthew , Sturtevant Nathan , Li Boyang
Published: October 2022

Automatic analysis of game levels can provide as- sistance to game designers and procedural content generation. We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation. Due to its ability to automatically learn intermediate representations for the task, a convolutional neural network (CNN) provides a general tool for both types of analysis. In this paper, we explore the use of CNN to analyze 1,437 Infinite Mario levels. We further propose a deep reinforcement learning technique for dynamic analysis, which allows the simulated player to pay a penalty to reduce error in its control. We empirically demonstrate the effectiveness of our techniques and complementarity of dynamic and static analysis.
Marahel: A Language for Constructive Level Generation
Authors: Khalifa Ahmed , Togelius Julian
Published: October 2022

Marahel is a language and framework for constructive gen- eration of 2D tile-based game levels. It is developed with the dual aim of making it easier to build level generators for game developers, and to help solving the general level generation problem by creating a generator space that can be searched using evolution. We describe the different sections of the level generators, and show examples of generated maps from 5 dif- ferent generators. We analyze their expressive range on three dimensions: percentage of empty space, number of isolated elements, and cell-wise entropy of empty space. The results show that generators that have starkly different output from each other can easily be defined in Marahel.
Towards End-to-End Natural Language Story Generation Systems
Authors: Valls-Vargas Josep , Zhu Jichen , Ontañón Santiago
Published: October 2022

Storytelling and story generation systems usually require knowledge about the story world to be encoded in some form of knowledge representation formalism, a notoriously time-consuming task requiring expertise in storytelling and knowledge engineering. In order to alleviate this authorial bottleneck, in this paper we propose an end-to-end computational narrative system that automatically extracts the necessary domain knowledge from corpus of stories written in natural language and then uses such domain knowledge to generate new stories. Specifically, we employ narrative information extraction techniques that can automatically extract structured representations from stories and feed those representations to an analogy-based story generation system. We present the structures we used to connect two existing computational narrative systems and report our experiments using a dataset of Russian fairy tales. Specifically we look at the perceived quality of the final natural language being generated and how errors in the pipeline affect the output.
Directing Intentional Superposition Manipulation
Authors: Robertson Justus , Amos-Binks Adam , Young R.
Published: October 2022

Strong story interactive narratives (IN) are stories that branch based on participant actions where all branches conform to a set of predefined constraints. However, participants in these systems may create branches where the constraints no longer hold. Strong story experience management, the process of generating IN trees, can be viewed as a game where the experience management agent wins if the story constraints hold during gameplay and loses if they are broken. In domains where the player has incomplete information of the story world, the experience manager can take action by shifting the player between alternate states that are consistent with the player's observations in order to maximize the probability that constraints will hold. This process is called superposition manipulation. In this paper we present a method of estimating the number of goal states reachable from different states in order to make informed decisions during superposition manipulation.
Automated Screenplay Annotation for Extracting Storytelling Knowledge
Authors: Winer David , Young R.
Published: October 2022

Narrative screenplays follow a standardized format fortheir parts (e.g., stage direction, dialogue, etc.) including short descriptions for what, where, when, and howto film the events in the story (shot headings). We created a grammar based on the syntax of shot headings toextract this and other discourse elements for automatic screenplay annotation. We test our annotator on over a thousand raw screenplays from the IMSDb screenplay corpus and make the output available for narrative intelligence research.
Generating Paths with WFC
Authors: Scurti Hugo , Verbrugge Clark
Published: October 2022

We describe a tool based on the Wave Function Collapse algorithm that performs example-based path generation on fixed maps. Our design aims at a practical system usable by non-programmers, and includes both easy input control and multiple post-processing steps. The design is implemented in Unity and enables users to easily visualize the results of experimenting with different path descriptions and game levels.
Action Abstractions for Combinatorial Multi-Armed Bandit Tree Search
Authors: Moraes Rubens , Mariño Julian , Lelis Levi , Nascimento Mario
Published: October 2022

Search algorithms based on combinatorial multi-armed bandits (CMABs) are promising for dealing with state-space sequential decision problems. However, current CMAB-based algorithms do not scale to problem domains with very large actions spaces, such as real-time strategy games played in large maps. In this paper we introduce CMAB-based search algorithms that use action abstraction schemes to reduce the action space considered during search. One of the approaches we introduce use regular action abstractions (A1N), while the other two use asymmetric action abstractions (A2N and A3N). Empirical results on MicroRTS show that A1N, A2N, and A3N are able to outperform an existing CMAB-based algorithm in matches played in large maps, and A3N is able to outperform all state-of-the-art search algorithms tested.
Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation
Authors: Sifa Rafet , Yawar Raheel , Ramamurthy Rajkumar , Bauckhage Christian
Published: October 2022

Players of digital games face numerous choices as to what kind of games to play and what kind of game content or in-game activities to opt for. Among these, game content plays an important role in keeping players engaged so as to increase revenues for the gaming industry. However, while nowadays a lot of game content is generated using procedural content generation, automatically determining the kind of content that suits players' skills still poses challenges to game developers. Addressing this challenge, we present matrix- and tensor factorization based game content recommender systems for recommending quests in a single player role-playing game. We discuss the theory behind latent factor models for recommender systems and derive an algorithm for tensor factorizations to decompose collections of bipartite matrices. Extensive online bucket type tests reveal that our novel recommender system retained more players and recommended more engaging quests than handcrafted content-based and previous collaborative filtering approaches.
A User Study on Learning from Human Demonstration
Authors: Packard Brandon , Ontanon Santiago
Published: October 2022

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical for learning from human demonstrators. In this paper, we focus on LfD with limited training data, and specifically on the problem of active LfD where the demonstrators are human. We present the results of a user study in comparing SALT, a new active LfD approach, versus a previous state-of-the-art Active LfD algorithm, showing that SALT significantly outperforms it when learning from a limited amount of data in the context of learning to play a puzzle video game.
Mimisbrunnur: AI-Assisted Authoring for Interactive Storytelling
Authors: Stefnisson Ingibergur , Thue David
Published: October 2022

Authoring in the context of Interactive Storytelling (IS) is inherently difficult, and there is a need for authoring tools that both enable and assist authors in the creation of new content. In this paper, we discuss our approach for creating an AI-assisted authoring tool via the concept of mixed-initiative systems. We introduce our tool, Mimisbrunnur, which uses this concept to assist authors in the creation of story content. We explain how the tool functions and introduce its fundamental components, including Natural Language Processing, a Suggestion Generator, and three authoring modules.
Guiding Monte Carlo Tree Search by Scripts in Real-Time Strategy Games
Authors: Yang Zuozhi , Ontañón Santiago
Published: October 2022

In Real-Time Strategy (RTS) games, the action space grows combinatorially with respect to the number of units. With limited computing budget between actions, methods like Monte Carlo Tree Search (MCTS) tend to get lost in the massive search space. An interesting line of existing work is to incorporate human knowledge in the form of scripts. In this paper, we investigate different possibilities for incorporating scripts into the tree policy while still maintaining the convergence guarantees of MCTS. We also report experiments on incorporating the scripts into the playout policy, which showed that unbiased bots perform better than biased bots.
Tree Search versus Optimization Approaches for Map Generation
Authors: Bhaumik Debosmita , Khalifa Ahmed , Green Michael , Togelius Julian
Published: October 2022

Search-based procedural content generation uses stochastic global optimization algorithms to search for game content. However, standard tree search algorithms can be competitive with evolution on some optimization problems. We investigate the applicability of several tree search methods to level generation and compare them systematically with several optimization algorithms, including evolutionary algorithms. We compare them on three different game level generation problems: Binary, Zelda, and Sokoban. We introduce two new representations that can help tree search algorithms deal with the large branching factor of the generation problem. We find that in general, optimization algorithms clearly outperform tree search algorithms, but given the right problem representation certain tree search algorithms performs similarly to optimization algorithms, and in one particular problem, we see surprisingly strong results from MCTS.
Watershed Graphs for Faster Path Planning in Binary Occupancy Grids
Authors: Hew Patrick
Published: October 2022

We speed up path planning in binary occupancy grids by using the watershed transform to construct a ‘watershed graph’ of the terrain’s topology, thereby cueing the path planner via a heuristic for the search, setting subgoals, or declaring cells to be blocked. Experiments with A*, Theta*, and Phi* showed that the time invested in constructing the watershed graph can be recovered when repeatedly planning paths through the same terrain, or planning paths over longer distances. Speedup factors exceeding 5× were obtained with only modest inflations in path length. This article will help developers and users of path planners who are looking to reduce runtime without disrupting their algorithm architecture.
“It’s Unwieldy and It Takes a Lot of Time” — Challenges and Opportunities for Creating Agents in Commercial Games
Authors: Jacob Mikhail , Devlin Sam , Hofmann Katja
Published: October 2022

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.
A Good Story Is One in a Million: Solution Density in Narrative Generation Problems
Authors: Siler Cory , Ware Stephen
Published: October 2022

Narrative generation systems can be classified on a spectrum from strong autonomy to strong story. Systems on the strong autonomy side treat characters as fully independent agents but may struggle to meet the author’s requirements, while those on the strong story side direct character behaviors centrally but may struggle to create the illusion of character believability. In this paper, we use benchmark story generation problems as a framework to compare the spaces of stories that could be generated by prototypical strong story and strong autonomy systems. Comparing the relative solution densities of these spaces helps us quantify how common certain desirable narrative properties are. This can be informative for system designers when deciding, for instance, whether to strictly enforce all desired properties or to generate and filter from a broader class of solutions.
Evaluating and Comparing Skill Chains and Rating Systems for Dynamic Difficulty Adjustment
Authors: Sarkar Anurag , Cooper Seth
Published: October 2022

Skill chains define how in-game skills build on each other and the order in which players ideally acquire them during gameplay. This can enable dynamic difficulty adjustment (DDA) by serving levels based on the skills that players currently have and those required to solve a given level. Similarly, DDA can also be achieved by using rating systems to match players with suitable levels by assigning ratings to players and levels based on ability and difficulty respectively. However, the relative effects of these two methods remain unclear, particularly in the context of human computation games (HCGs). In this paper, we present a general model for using skill chains and rating systems in a combined DDA system along with an evaluation comparing the two for difficulty balancing within HCGs, focusing on the relative merits of both methods when used separately as well as together. We evaluate our methods using the HCGs Iowa James and Paradox. Our findings suggest that incorporating skill chains can improve upon previously shown benefits of using only rating systems for DDA in HCGs.
A Demonstration of Anhinga: A Mixed-Initiative EPCG Tool for Snakebird
Authors: Sturtevant Nathan , Decroocq Nicolas , Tripodi Aaron , Yang Carolyn , Guzdial Matthew
Published: October 2022

Mixed-initiative procedural content generation (PCG) refers to systems where a human and AI cooperate in some way to produce content. While there has been increasing interest in research on these systems, there are still many domains and PCG approaches that have not yet been explored. In this demonstration we introduce a novel mixed-initiative tool that employs Exhaustive PCG for puzzle level design.
Artificial Intelligence as an Art Director
Authors: Gonzalez Adrian
Published: October 2022

My research focuses on the design of an artificial intelligence-based system that can perform the tasks of a videogame art director. I began with the analysis of game developers’ design processes, workflows, and methodologies. I observed they involve fast-paced proposal-evaluation-correction cycles. This, along with my interest in generative methods, led me to consider using Variational Autoencoders (VAEs), which also include similar cycles, and do not require a high number of samples to produce satisfactory results. Currently, I am using VAEs on experiments over Pokémon images, with positive results.
Playable Experiences at the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Authors: Dickinson Melanie , Kreminski Max , Mateas Michael , Wardrip-Fruin Noah , Wieluch Sabine
Published: October 2022

This paper describes the accepted entries to the eighth Playable Experiences track to be held at the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’20). The Playable Experiences track showcases innovative complete works that are informed, inspired, or otherwise enabled by artificial intelligence.
Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search
Authors: Pereira Lucas V. S. , Chaimowicz Luiz , Lelis Levi H. S.
Published: October 2022

In this paper we present Birds in Boots (BiB), a system that uses a sampling-based search algorithm to learn a neural policy for solving Angry Birds levels. Our learning procedure is based on the Bootstrap algorithm, which was previously used to learn heuristic functions for solving classic heuristic search problems. BiB starts its learning procedure with a policy given by a randomly initialized neural network. This initial policy is used to guide the search algorithm on a set of procedurally generated Angry Birds levels. The levels the search algorithm is able to solve are used to improve the neural policy. We repeat this procedure a number of times, until solving all levels or reaching a time limit. We perform several experiments with different instances of our method and show that it can solve more levels than other approaches, including learning-based and rule-based methods.
TAG: Terraforming Mars
Authors: Gaina Raluca D. , Goodman James , Perez-Liebana Diego
Published: October 2022

Games and Artificial Intelligence (AI) have had a tight relationship for many years. A multitude of games have been used as environments in which AI players can learn to act and interact with others or the game mechanics directly; used as optimisation problems; used as generators of large amounts of data which can be analysed to learn about the game, or about the players; or used as containers of content which can be automatically generated by AI methods. Yet many of these environments have been very simple and limited in scope. We propose here a much more complex environment based on the boardgame Terraforming Mars, implemented as part of the Tabletop Games Framework: a very large and dynamic action space, hidden information, large amounts of content, resource management and high variability make this problem domain stand out in the current landscape and a very interesting problem for AI methods of multiple domains. We include results of baseline AI game-players in this game and in-depth analysis of the game itself, together with an exploration of problem complexity, challenges and opportunities.
Can We Infer Player Behavior Tendencies from a Player’s Decision-Making Data? Integrating Theory of Mind to Player Modeling
Authors: Shergadwala Murtuza N. , Teng Zhaoqing , Seif El-Nasr Magy
Published: October 2022

Game AI systems need the theory of mind, which is the humanistic ability to infer others' mental models, preferences, and intent. Such systems would enable inferring players' behavior tendencies that contribute to the variations in their decision-making behaviors. To that end, in this paper, we propose the use of inverse Bayesian inference to infer behavior tendencies given a descriptive cognitive model of a player's decision making. The model embeds behavior tendencies as weight parameters in a player's decision-making. Inferences on such parameters provide intuitive interpretations about a player's cognition while making in-game decisions. We illustrate the use of inverse Bayesian inference with synthetically generated data in a game called \textit{BoomTown} developed by Gallup. We use the proposed model to infer a player's behavior tendencies for moving decisions on a game map. Our results indicate that our model is able to infer these parameters towards uncovering not only a player's decision making but also their behavior tendencies for making such decisions.
Advanced Real-Time Hierarchical Task Network: Long-Term Behavior in Real-Time Games
Authors: Namiki Kousuke , Mori Tomohiro , Miyake Youichiro , Sakata Shinpei , Martins Gustavo
Published: October 2022

HTN (Hierarchical Task Network) is a widespread technique in the game industry to achieve long-term behavior and smart intelligence in NPCs. However, in the more real-time games of recent years, a situation has emerged where the system is not functioning effectively. We understand the re-planning process is the fundamental problem of HTN in real-time games and propose a new method to solve this problem.
Social Modeling via Logic Programming in City of Gangsters
Authors: Zubek Robert , Horswill Ian , Robison Ethan , Viglione Matthew
Published: October 2022

City of Gangsters is a commercial strategy game with significant social modeling mechanics: it is a tycoon management game, where the player needs to work their social connections with a network of roughly 1200 NPCs to get things done, and NPC opinions about the player modulate the player's ability to succeed.

We found logic programming to be well suited to our knowledge representation problem, including the need to perform inferences over a relationship network with more than a thousand active characters, and to provide the player with meaningful feedback about the consequences of their actions in the social space.

In this paper we present the technical details of this social modeling problem, the details of our logic programming implementation, and how this interacts with the game's design and its social and material economies.
Curve-Based Level Generation for a Full-Body VR Rhythm Game
Authors: Truesdell Erin J.K.
Published: October 2022

Due to the large amount of content authoring required to produce levels for rhythm games, procedural content generation provides an attractive alternative for generating levels in large numbers. Frequently, however, level generators for rhythm games rely only on a “difficulty” measure to describe desired level properties. This work presents level generation tool for BEAMS, a full-body virtual reality rhythm game. The BEAMS level generator allows designers to shape curves describing four characteristics of the obstacles needed for a game in which the primary interaction involves the human body in three-dimensional space.
Game System Models: Toward Semantic Foundations for Technical Game Analysis, Generation, and Design
Authors: Cardona-Rivera Rogelio E. , Zagal José P. , Debus Michael S.
Published: October 2022

Game system models introduce abstractions over games in order to support their analysis, generation, and design. While excellent, models to date leave tacit what they abstract over, why they are ontologically adequate, and how they would be realized in the engine underlying the game. In this paper we model these abstraction gaps via the first-order modal mu-calculus. We use it to reify the link between engines to our game interaction model, a player-computer interaction framework grounded in the Game Ontology Project. Through formal derivation and justification, we contend our work is a useful code studies perspective that affords better understanding the semantics underlying game system models in general.
On the Challenges of Generating Pixel Art Character Sprites Using GANs
Authors: Coutinho Flávio , Chaimowicz Luiz
Published: October 2022

We pose the problem of generating pixel art character sprites facing one side (e.g., right), given their images facing another one (e.g., front), as an image-to-image translation task and investigate the use of the Pix2Pix architecture to solve it. Aiming to improve the results over unseen data, we propose and investigate two architecture modifications: (a) representing images using color palettes and (b) adding a histogram loss term to the generator. We compared the results qualitatively and quantitatively using FID and L1 distances between the generated and target images. Results indicate that representing images with color palettes encourages overfitting, and the histogram loss leads to slightly improved results.
Loose Ends: A Mixed-Initiative Creative Interface for Playful Storytelling
Authors: Kreminski Max , Dickinson Melanie , Wardrip-Fruin Noah , Mateas Michael
Published: October 2022

We present Loose Ends, a mixed-initiative co-creative storytelling play experience in which a human player and an AI system work together to compose a story. Loose Ends specifically aims to provide computational support for managing multiple parallel plot threads and bringing these threads to satisfying conclusions—something that has proven difficult in past attempts to facilitate playful mixed-initiative storytelling. We describe the overall human-AI interaction loop in Loose Ends, including the implementation of the rules-based AI system that enables this interaction loop; discuss four examples of desirable mixed-initiative interactions that are possible in Loose Ends, but not in similar systems; and present results from a preliminary expert evaluation of Loose Ends. Altogether, we find that Loose Ends shows promise for creating a sense of coauthorship in the player while also mitigating the directionlessness reported by players of earlier systems.
RPGPref: A Planning Heuristic That Uses Playstyle Preferences to Model Player Action and Choice
Authors: Lang Eric W. , Young R. Michael
Published: October 2022

Recent work extending planning algorithms that reason about action and change has been successful at supporting game design, player modeling, and story generation. Incorporating agent preferences over actions and propositions into a planning process allows for a more accurate prediction of what a human might do when solving a problem like playing through a game level. This paper presents the preference-based planning heuristic RPGPref which uses relaxed plan graphs (RPGs) and preference sets to guide a planner toward a preference-conforming path to its goal. A human subjects evaluation confirms that RPGPref successfully guides the planning process toward solution plans that recognizably match and differentiate player playstyles.
Puck: A Slow and Personal Automated Game Designer
Authors: Cook Michael
Published: October 2022

In this paper we introduce Puck, a new automated game design system which combines continuous creativity with an exhaustive approach to content generation. We explain the motivation behind Puck, and in particular its focus on users and small communities. Puck is, to our knowledge, the first automated game designer that can be downloaded and individualise itself through testing and design. We then describe the engineering and structure of the system, detail some initial outputs and evaluation of the system, and future work.
Playing with the Strings: Designing Puppitor as an Acting Interface for Digital Games
Authors: Junius Nic , Mateas Michael , Wardrip-Fruin Noah , Carstensdottir Elin
Published: October 2022

Interactive drama has focused on how to allow the player agency in influencing their experience through plot action. Interactive drama's preoccupation with changing plot structure bears little resemblance to theater's emphasis on character expression and dramatic play. Dramatic play allows the player to embody the character through actions, focusing on how characters express themselves and react rather than on how or even if that impacts the overarching sequence of events. In this paper, we present Puppitor, a system for character expression of emotion for interactive storytelling built using acting practice and fighting games as the foundation for its core design and describe its usage in conjunction with Ren'Py to facilitate a novel interactive narrative experience as a case study.