This paper presents a comprehensive comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats. Traditional machine learning models with embeddings, large language models (LLMs) with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation (RAG) approaches are evaluated. The evaluation framework assesses three critical dimensions: classification accuracy, processing speed, and computational costs. A hybrid moderation system architecture is proposed that optimizes human moderator workload through automated detection and incorporates continuous learning mechanisms. The experimental results demonstrate significant performance variations across methods, with fine-tuned DistilBERT achieving optimal accuracy-cost trade-offs. The findings provide empirical evidence for deploying cost-effective, efficient content moderation systems in dynamic online gaming environments.

With the rise of multiplayer online games, real-time voice communication is essential for team coordination. However, general ASR systems struggle with gaming-specific challenges like short phrases, rapid speech, jargon, and noise, leading to frequent errors. To address this, we propose the GO-AEC framework, which integrates large language models, Retrieval-Augmented Generation (RAG), and a data augmentation strategy using LLMs and TTS. GO-AEC includes data augmentation, N-best hypothesis-based correction, and a dynamic game knowledge base. Experiments show GO-AEC reduces character error rate by 6.22% and sentence error rate by 29.71%, significantly improving ASR accuracy in gaming scenarios.

Enhancing the player experience is the ultimate goal of artificial intelligence in gaming. Given that game developers create games for a variety of platforms, it is imperative. The option between a console and a desktop PC gaming has become obsolete. Thanks to AI, developers can now create console-like experiences for several device kinds. AI games come in several formats every year. Some experts argue that the less obvious uses of AI in games are the most potent. AI is becoming more prevalent in games, which has significant economic advantages for companies.

Emergent artificial intelligence and machine learning technologies are affecting all areas of technology, including online gambling and gaming. AI denotes the capability of machines and electronic systems to mimic functions that resemble the cognitive activity of the human mind. These include instances of automated learning or problem-solving. Such technologies are increasingly changing all walks of life. From Google to YouTube to Netflix, every widespread service and large company employ similar services.

Electronic Arts (EA) yesterday revealed its latest title: a custom game environment for deep learning networks to learn how to play video games. In the future the "computer" player in games won't rely on basic scripts; it'll react to you, and play against you, using the same information and controls a human player does. If you're a gamer, you've probably played one of EA's games. The list of hits in the company's catalog contains some of the greatest selling franchises of all time. Many of us have grown up with games like Battlefield, Madden, and FIFA, each titles with robust computer (CPU) opponents built-in.

We report results from a human computation study that tests the extent to which output agreement games are better than traditional methods in terms of increasing quality of labels and motivation of voluntary workers on a task with a gold standard. We built an output agreement game that let workers recruited from Amazon's Mechanical Turks label the semantic textual similarity of 20 sentence pairs. To compare and test the effects of the major components of the game, we created interfaces that had different combinations of a gaming environment (G), social interaction (S), and feedback (F). Our results show that the main reason that an output agreement game can collect more high-quality labels is the gaming environment (scoring system, leaderboard, etc). On the other hand, a worker is much more motivated to voluntarily do the task if he or she can do it with another worker (i.e., with social interaction). Our analysis provides human computation researchers important insight on understanding how and why the method of Game with a Purpose (GWAP) can generate high-quality outcomes and motivate more voluntary workers.