Game Map Research Articles

A constructive approach to strategy game map generation

Designing strategy games poses unique challenges due to the complicated nature of the genre where balancing game elements is a major concern. Maps in strategy games commonly contain elements such as resources to gather and buildings with different capabilities, playing a crucial role in the game life cycle. Resource and base placements must be done carefully. It is difficult to design maps manually and even harder to design multiple variations. This study presents a method for procedurally creating strategy game maps. A strategy game was developed and used as a test-bed for the study. A constructive approach is followed in the main steps of the algorithm design and a novel procedural noise function is used together with a uniform stratified sampling method to create and distribute the resources. Then eligible base locations are detected and scored according to a fitness function that defines how favorable a location is. These scores are used to place bases appropriately. The algorithm is evaluated using simulations and a small-scale user study is conducted at the end. The proposed method is capable of creating playable, diverse, and fair maps and is fast enough to work at runtime.

Application of Artificial Intelligence in the Design of 2D Escape From Pirates Game with A Star Algorithm Search Method

This research is designed to provide a challenging gaming experience by integrating strategy and problem-solving elements. The A* algorithm was chosen due to its efficient ability to find the shortest path in a complex search space. The implementation of this algorithm allows the main character to dynamically avoid obstacles and pirate threats and reach the destination in an optimal way. The test results show that the A* algorithm not only improves game performance but also provides a more realistic and challenging experience for the player. For testing this application, using obstacles and measured based on the value of nodes on the game map. Based on the test results, the A Star algorithm was successfully applied when comparing the computations in the game and manual calculations in the Escape from Pirates game in the test. Thus, this research contributes to the development of artificial intelligence-based games and opens opportunities for further innovation in interactive game design.

Languages of Power, Languages of Protest: Exploring the Rhetorics of Game Maps

In his book The New Nature of Maps: Essays in the History of Cartography, J.B. Harley writes that “Maps are preeminently a language of power, not of protest.” Harley was writing about maps of the physical world, but most game maps also speak in “languages of power,” and for similar reasons; however, this is not always the case. In this paper, I discuss how and why Harley's rubric also applies to game maps. Ultimately, I highlight the unique value of the minority of video games that speak in “languages of protest,” thus allowing us to play – sometimes figuratively, and sometimes literally – with our traditional understanding of maps.

JAHAN: A framework for procedural generation of game maps from design specifications

Procedural content generation is a field that focuses on creating game assets employing algorithmic procedures. A critical asset in many game genres is the map. Due to the sophistication of the game map development, procedural map generation has been the subject of many studies. Considering the fundamental complexity of the game maps, many of these studies try to engage human designers in the generation process to improve the output of algorithmic procedures. However, these studies have not paid adequate attention to important non-functional requirements such as generality, comprehensiveness, controllability, and adaptability to iterative development.This paper presents a procedural map generation framework that places map designers at the center of a pipeline architecture. This framework allows designers to configure different pipeline modules and specify the characteristics of their desired map. Moreover, this framework has assertion checking features that facilitate map evaluation.We demonstrate the capabilities of this framework through one running example, along with three case studies that generate maps for first-person shooter, real-time strategy, and dungeon crawler games. Furthermore, we present a user study to show the suitability of this framework in iterative development processes and demonstrate how it reduces the labor of human designers.

Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games

Elaborate sound design, including background music, ambient sounds (sounds describing the game world), and character sounds (sounds generated by the character’s actions), plays a pivotal role in modern video games. However, the influence of these different types of sound on the player’s experience has not been extensively researched. This study examines the influence of these sound types on immersion, avatar identification, fun, and perceived competence. In two experiments, participants played League of Legends under four different sound conditions. The first experiment (N1 = 32) revealed a non-significant trend in the effect of character sounds on avatar identification. Ambient sounds, however, were limited because the task restricted participants’ movement across the game map. Consequently, we adapted the task to allow for a wider variety of ambient sounds in the second experiment (N2 = 32). Here, a significant impact of character sounds on immersion, avatar identification, and fun was observed, as well as an interaction effect of character sounds and ambient sounds on fun. Furthermore, we observed a trend, though not statistically significant, suggesting that ambient sounds may influence the player’s sense of flow. These findings underline the distinct effects of different sound types, and we discuss implications for the design of sound in video games.

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings

The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images or tile maps from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images or maps are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models' performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model to demonstrate quality generation.

Level Building Sidekick: An AI-Assisted Level Editor Package for Unity

Developing an original video game requires high investment levels, market research, cost-effective solutions, and a quick development process. Game developers usually reach for commercial off-the-shelf components often available in the engine's marketplace to reduce costs. Mixed-initiative authoring tools allow us to combine the thoughtful work of human designers with the productivity gains of automated techniques. However, most commercial AI-assisted Procedural Content Generation tools focus on generating small independent components, and standalone research tools available for generating full game levels with state-of-the-art algorithms usually lack integration with commercial game engines. This article aims to fill this gap between industry and academia. The Level Building Sidekick (LBS) is a mixed-initiative procedural content generation tool built by our research lab in association with four small independent game studios. It has a modular software architecture that enables developers to extend it for their particular projects. The current version has two working modules for building game maps, an early version of a module for populating the level with NPCs or items, and the first stages of a quest editor module. An automated testing module is planned. LBS is distributed as an AI-Assisted videogame-level editor Unity package. Usability testing performed using the ``Think-Aloud'' methodology indicates LBS has the potential to improve game development processes convincingly. However, at this stage, the user interface and the AI recommendations could improve their intuitiveness. As a general comment, the tool is perceived as a substantial contribution to facilitating and shortening development times, compared to only using the base game engine. There is an untapped market for mixed-initiative tools that assist the game designer in creating complete game levels. We expect to fill that market for our partner development studios and provide the community with an open research and development platform in a standard game engine.

Deep Game Location Acquisition for Pokemon GO on Mobile Devices

Advanced AR (Augmented Reality) games such as Pokemon Go attract million and million players because they integrate various sensors to impress excellent user experiences on mobile devices. In this paper, for energy saving on the mobile devices, we can use an accelerometer and a magnetometer to estimate the current user's location approaching interest of points on a game map without active GPS sensors and wireless network. In addition, we create an effective data structure with R-tree and adopt deep learning to efficiently answer the location query of Pokemons on the game location database. Our algorithm outperforms previous approaches in not only smaller running time but also the better power consumption for the AR games on the mobile devices

Harnessing generative grammars and genetic algorithms for immersive 2D maps

Modern games typically demand varied, detailed, and wide maps. In particular, 2D game maps are typically designed as a combination of smaller pieces called tiles, so positioning each tile on a map layer is analogous to colorizing a pixel using a palette. However, building a map is an exhaustive process that consumes a lot of planning and maintenance time from a multidisciplinary team, including artists and designers. Consequently, PCG techniques emerge as an attractive solution to this challenge. We propose a novel PCG technique to assist designers in writing rules applied to a balanced combination of genetic algorithms and generative grammar. Our implementation supports generating two-dimensional pathways and places detailed objects (e.g., plants, flowers, forest, etc.) along the path while following a desired distribution given beforehand by the designer. Notably, our PCG method allows the generation of variable transitions of biomes around a road and these results are compatible with real-world technologies for game development and prototyping. Our method can aid designers in various specific modeling tasks regarding environments with two or more dimensions since the results are sufficiently interpretable to undergo subsequent human intervention.

Efficiently computing alternative paths in game maps

Alternative pathfinding requires finding a set of k alternative paths (including the shortest path) between a given source s and a target t. Intuitively, these paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While finding alternative paths in road networks has been extensively studied, to the best of our knowledge, we are the first to formally study alternative pathfinding in game maps which are typically represented as Euclidean planes containing polygonal obstacles. First, we adapt the existing techniques designed for road networks to find alternative paths in the game maps. Then, based on our web-based system that visualises alternative paths generated by different approaches, we conduct a user study that shows that the existing road network approaches generate high-quality alternative paths when adapted for the game maps. However, these existing approaches are computationally inefficient especially when compared to the state-of-the-art shortest path algorithms. Motivated by this, we propose novel data structures and exploit these to develop an efficient algorithm to compute high-quality alternative paths. that shows that the existing road network approaches generate high-quality alternative paths in game maps. Our extensive experimental study demonstrates that our proposed algorithm is more than an order of magnitude faster than the existing approaches and returns alternative paths of comparable quality. Furthermore, our algorithm is comparable to a state-of-the-art shortest path algorithm in terms of running time.

Reducing Redundant Work in Jump Point Search

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios.

Como as marcas utilizam a publicidade in game no jogo Grand Theft Auto

This article aims to present a study on the insertion of advertising practices in digital games, this being within the servers of the game Grand Theft Auto Roleplay (called GTA RP), an illegitimate modification of the game Grand Theft Auto V by the developer Rockstar Games. It is worth mentioning that GTA RP is nothing more than a base platform for GTA V, which supports a large number of players simultaneously and allows partial customizations in the game map, in which the brands that we will discuss used this resource to carry out activations at the along the Cidade Alta server, this being a kind of functional city in satire of the real world. In addition, more than identifying the brands in the game, the study seeks to verify the ad formats and their possible impacts on the player's perception of such brands presented within this universe. In short, it is understood that digital games have been an important media channel in our society, capable of reaching new consumer niches, in which brands were able to visualize in their structure countless possibilities for the insertion of advertising messages.

Research of the Path Finding Algorithm A* in Video Games

Pathfinding is an essential component of many video games. This review provides a simple overview of the classical pathfinding algorithm A*, including a brief summary of the A* algorithm, as well as a few optimizations and applications of A* in video games. In the gaming industry, A* is a widely used pathfinding algorithm. Based on breadth first search and heuristic search, A* uses a heuristic function to quickly find the shortest path in a game map. A common optimization of A* is to use a binary heap to save the open queue, a variety of heuristic functions are also commonly used to increase the accuracy and speed of A*. This paper summarizes work done on the A* algorithm. The goal of this paper is to give researchers and developers a simple overview of the A* algorithm and its applications in video games.

Aesthetic Bot: Interactively Evolving Game Maps on Twitter

This paper describes the implementation of the Aesthetic Bot, an automated Twitter account that posts images of small game maps that are either user-made or generated from an evolutionary system. The bot then prompts users to vote via a poll posted in the image's thread for the most aesthetically pleasing map. This creates a rating system that allows for direct interaction with the bot in a way that is integrated seamlessly into a user's regularly updated Twitter content feed. Upon conclusion of the each voting round, the bot learns from the distribution of votes for each map to emulate user preferences for design and visual aesthetic in order to generate maps that would win future vote pairings. We discuss the ongoing results and emerging behaviors that have occurred since the release of this system from both the bot's generation of game maps and the participating Twitter users.

Unity Snappable Meshes

The Snappable Meshes algorithm procedurally generates 3D maps for computer games by iteratively selecting and linking pre-built map pieces via designer-specified connectors. In this paper we present an implementation of this algorithm in the Unity game engine, describing its architecture and discussing core implementational solutions. A number of examples illustrate the potential of the algorithm and the capabilities of the software. We assess the application’s impact on past and ongoing research, and how it can be improved to support future research questions.

Do Personality Traits Matter? Exploring Anti-drug Behavioral Patterns in a Computer-Assisted Situated Learning Environment

Drug abuse has been and continues to be, a common social issue worldwide, yet the efficiency of widely adopted sweeping speech for anti-drug campaigns has proven inefficient. To provide students with a safe and efficient learning situation related to drug refusal skills, we used a novel approach rooted in a serious learning game and concept map during a brief extracurricular period to help students understand drugs and their negative effects. The proposed game-based situational learning system allowed all students to participate simultaneously and individually in multiple scenarios of drug temptation posed by peers and classmates to practice responding and refusing drugs in school and community settings. Moreover, to explore whether different personality traits (such as the Big Five personality traits) result in different anti-drug responses, we used a serious game to conduct an anti-drug experiment on 53 junior middle school students aged 13–15. Each participant’s decision-making process was recorded in the serious game as behavioral patterns for lag sequential analysis (LSA). The outcomes revealed seven behavioral patterns including differentiation (D), acceptance (A), effective (ER) and ineffective responses (IR), effective (ES) and ineffective solution-seeking (IS), and failure to refuse (F). The GSEQ (Generalized Sequential Querier) which is a computer program for analyzing sequential observational data was used. The results indicated the following: (1) Neuroticism was performed at a relatively low level under the guidance of a concept map. (2) “Neuroticism” was associated with the lowest risk of accepting drugs. (3) Students with “openness to experiences” were at high risk of accepting drugs. (4) Almost all personality behavioral transition diagrams showed that failure to refuse (F) drugs was followed by inefficient seeking of help (SI) and inefficient refusal (RI). These findings provide reference points for designing adaptive anti-drug education programs.

Systematic comparison of path planning algorithms using PathBench

Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly exploring random tree, are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. A unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported, e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, and gated path planning networks; integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real-world robot to demonstrate PathBench's support for Robot Operating System. PathBench is open source1.

Angle-Based Multi-Objective Evolutionary Algorithm Based On Pruning-Power Indicator for Game Map Generation

This paper presents a multi-objective search-based approach to generate balanced maps for real-time strategy games. First, an angle-based pruning-power indicator guided evolutionary algorithm is proposed to solve the many-objective optimization problems. This algorithm divides the hyper-spherical coordinate space into a set of local partitions, where the pruning power indicator is used to access the dominance ability of the solution over its located partition, and the radius-penalized angle calculation indicator aims to guide the evolution of population along specific partition direction. In this way, both the convergence and diversity of solutions are considered, and the selection pressure toward Pareto fronts is strengthened. We show the effectiveness of the proposed strategies on a set of many-objective benchmark functions. Then, the proposed algorithm is used to generate game maps –a real-world problem derived from the procedural content generation, in which there are four objective functions to guide the map generation of MegaGlest based on the principle of fairness, playability, strategy, and interestingness of games. Experimental results on four map generation instances verify the competitiveness and effectiveness of the proposed algorithm.

Role-Playing Game as Learning Media To Support Online Learning

Lack of learning media will affect the learning process in the classroom. Various learning media can motivate students in online learning activities. It causes learning media to be essential in the learning process. In the current era of online learning, students are prone to boredom and stress. Therefore this study aims to develop learning media with RPG (Role Playing Games). This research is exploratory because the field of play is vast, with validation using an E-GameFlow survey to assess the level of comfort of players when gaining knowledge. The research will be conducted on 15 junior high school students by distributing online questionnaires. The methods used to collect data are observation and questionnaires. The instrument used to collect data is a questionnaire. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results of each question on each aspect of E-GameFlow will be analyzed to find conclusions about the games that have been made. The results of the data obtained show that the lowest value is 5.49 in the challenge aspect, which is caused by a static game map and lack of guidance regarding the game. The overall results of E-GameFlow show that the game can provide comfort to players while providing learning materials. This is evident from no aspect with a score below the average.

Computer Application in Game Map Path-Finding Based on Fuzzy Logic Dynamic Hierarchical Ant Colony Algorithm

Computer Application in Game Map Path-Finding Based on Fuzzy Logic Dynamic Hierarchical Ant Colony Algorithm