This paper examines a Retrieval-Augmented Generation (RAG) approach for generating facial expressions in 3D game characters using artificial intelligence. By integrating large language models within a RAG-based architecture, we developed a proof-of-concept system that animates expressions based on Facial Action Coding System (FACs) action units. Testing demonstrates the potential of RAG-driven animations to create immersive, adaptive experiences with contextually appropriate expressions that enhance perceived emotional responsiveness in Non-Playable Characters, highlighting RAG’s promise for dynamic character interactions and AI-driven personalization in game development.
Client-side exploits, such as wall-hacks and aimbots, affect the competitive integrity of games. Competitive games prevent such exploits through kernel-level monitoring that not only invades the player’s privacy but also potentially opens a channel for kernel-level attacks on the player’s computer.Capability architectures, which attach additional metadata to memory, offer a solution to preventing client-side exploits that can be palatable to both players and game designers. By compartmentalizing memory with restricted permissions, games can prevent players from viewing or modifying memory associated with the game directly. However, games need not have access to the entire system at the kernel-level to perform this task. We propose integrating a first-person shooter running on Linux within a modern capability architecture.
Ontologies are adopted in, and for information systems, Artificial Intelligence, software engineering, and a range of knowledge engineering and data integration practices. However, the concept of ontology can be challenging to grasp, foundational and domain ontologies can be difficult to learn, develop, and (re)use, while dedicated methods for engaging domain experts and non-scientific stakeholders with ontologies are limited. This paper addresses these issues with the introduction of an easy-to-learn ontology game called Type Token, a multiplayer analog card game based on the Unified Foundational Ontology (UFO), which offers a fun and appealing way to learn basic ontology theories. Through a competitive, collaborative modeling activity and the scaffolding of increasingly complex ontological notions in gameplay, the game represents a tool for groups to engage in active learning with ontologies. Feedback from initial playtesting at various informal and formal events suggests that game-based group learning is a promising addition to ontology education materials and that discussion and engagement fostered through game and play can activate and reinforce ontological thinking. Ongoing development initiated by the design and implementation of Type Token has also uncovered ways to enact collaborative ontology development through dialogue and rule-based interactions, a future direction in the evolution of ontology education.
As the gaming industry grows, there is a greater need for high-quality, complex games, which requires efficient and scalable testing methods. This paper maps recording-based game test automation tools that record and replay user actions to make testing easier and reduce manual work. We analyzed 20 tools mentioned in industry blogs and grouped them into game-specific and general-purpose categories. We also evaluated their compatibility, input simulation features, scripting options, and integration with game engines.Our findings indicate that general-purpose tools, the most frequently mentioned in blog sources, offer broad adaptability across platforms but are typically limited to user interface and visual testing. In contrast, game-specific tools provide deeper integration with game engines, supporting access to in-game mechanics and making them better suited for testing gameplay aspects. Our observations intend to conduct industry and academic efforts toward more reliable, scalable, and cost-effective approaches to automated game testing.
Game testing ensures quality and is typically performed by human testers. Although game testing has advanced, the literature lacks research on generating human-crafted game tests. Traditional software testing techniques, such as exploratory testing, can be adapted to video games. We introduce XPloiT, a framework for exploratory testing of video games. We developed it using insights from traditional exploratory software testing, game testing, and prior research. The framework includes testing strategies tailored for video games, a recommended strategy order, and guidelines for applying each strategy. As an initial evaluation, we applied XPloiT to a platform game under development by an indie studio. XPloiT uncovered several bugs, more than half of which were previously unknown and confirmed by the developer. These findings support further development and assessment of the framework.
