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This article provides a step‑by‑step guide, complete with Python code examples, for creating a Protoss AI bot using DeepMind's pysc2 library to mine resources, construct buildings, train units, implement scouting, and execute attack strategies against increasingly difficult computer opponents in StarCraft II.
This article explains the design, implementation, and underlying Agent‑Oriented‑Programming framework of NetEase Fuxi’s multimodal real‑time voice AI teammate for the mobile game ‘Naraka: Bladepoint’, highlighting its capabilities such as autonomous navigation, combat assistance, natural dialogue, teaching, and broader applications of voice technology in games.
The article outlines how to obtain the DataFun Summit e‑book by following the public account instructions and provides concise English summaries of twelve technical sessions covering data lineage, integration, AI language models, multimodal content, game AI agents, lake‑warehouse governance, big‑data architecture, and cluster management.
This article details Tencent Game's end‑to‑end approach to building intelligent NPCs, covering the opportunities brought by AI, the practical implementation of multimodal LLM‑driven dialogue, knowledge‑augmented retrieval, long‑context handling, safety measures, multimodal expression (voice and facial animation), and system‑level performance optimizations for real‑time deployment.
This article reviews the evolution of game AI agents, examines how large language models (LLMs) can drive new AI behaviors in games, and discusses practical case studies across genres such as Werewolf‑style, war‑SLG, and MOBA games, concluding with challenges and future research directions.
The article details NetEase Fuxi's research on AI‑Generated Action (AIGA) for game AI bots, covering the shift from AIGC to AIGA, advances in making bots more human‑like and stylized, and the application of RLHF fine‑tuning to bridge objective metrics with subjective player experience.
Tencent Robotics X unveiled how its robot dog Max combines pre‑trained AI models with reinforcement learning across three learning stages, enabling it to acquire, store, and apply skills for autonomous decision‑making in complex tasks such as the World Chase Tag competition.
This article explains what artificial intelligence is, distinguishes weak and strong AI, explores its applications in games, product testing, and NetEase's Fuxi platform, and introduces fundamental machine‑learning concepts such as supervised, unsupervised, and reinforcement learning, as well as neural networks and loss functions.
This article explains how to build a Tetris game with a basic AI using Python 3.6.4 and PyQt5, covering environment setup, the underlying algorithm that evaluates future board states, and the complete source code for the AI decision‑making and scoring functions.
Zheng Lin‑kai’s record‑breaking Tetris AI for the Tencent Geek Challenge combines a two‑layer breadth‑first search—stage‑level pruning in Python and fast round‑level BFS in C++—with a heuristic that rewards high score, low occupied cells, and smooth board transitions, enabling a 1,413,876‑point performance.
This article explains how to create an AI‑driven Dou Dizhu card‑playing assistant by integrating the open‑source DouZero model with a PyQt5 GUI, covering UI layout, hand‑card and role recognition via screen capture, AI decision making, and step‑by‑step deployment instructions.
Tencent’s open‑source Game AI SDK provides a versatile automation testing platform—supporting a wide range of game genres and mobile/PC apps—by integrating environment simulation, configurable tools, image‑recognition modules, and deep‑learning algorithms (DQN and IM) into a unified, user‑friendly workflow for training and executing AI agents.
From Steve Jobs’s early work at Atari to modern DeepMind breakthroughs, the article traces how video games have grown into a multibillion‑dollar industry that serves as a testbed for AI research, while highlighting current AI techniques for smarter agents, procedural content generation, and the collaborative challenges shaping the future of game development.
The talk surveyed game‑AI evolution—from rule‑based systems to AlphaGo‑style reinforcement learning—highlighted industry and academic methods, detailed challenges of applying deep‑learning techniques to MOBA titles like Honor Kings, and proposed a hierarchical, multimodal framework with analysis and execution modules supported by robust simulation environments.
Game AI relies on a variety of search techniques—ranging from uninformed breadth‑first and depth‑first methods to heuristic‑driven A*, minimax with alpha‑beta pruning, and Monte Carlo Tree Search—as well as optimization approaches such as hill climbing, simulated annealing, genetic and evolution strategies, multi‑objective evolutionary algorithms, and neuroevolutionary methods like NEAT to generate intelligent, balanced, and adaptable game behavior.
This article explains Monte Carlo Tree Search (MCTS), covering its origin in AlphaGo, fundamental algorithm steps, node‑selection strategies such as UCB, strengths and weaknesses, enhancements, historical background, and recent research developments in artificial intelligence.