AI w grach

AI in games · id gameai · aliases: game AI, AI and games, sztuczna inteligencja w grach, AI w grach wideo, computational intelligence in games, AI gier

Zastosowania AI w grach: agenci grający, proceduralne generowanie treści, zachowanie postaci niezależnych i modelowanie graczy; gry służą też jako poligon badań nad AI.

AI applied to games: game-playing agents, procedural content generation, non-player character behaviour and player modelling; games also serve as AI research testbeds.

In one sentence

Sztuczna inteligencja, która gra w gry, tworzy do nich poziomy i postacie oraz dopasowuje rozgrywkę do konkretnego gracza.

Artificial intelligence that plays games, builds their levels and characters, and tunes the experience to each player.

Problem it solves

Ręczne budowanie światów, przeciwników i testów pochłania lata pracy, a jedna stała trudność nudzi jednych graczy i zniechęca innych.

Hand-building worlds, opponents and tests takes years of work, and one fixed difficulty bores some players while putting others off.

What it does

Jak mistrz gry przy stole: wymyśla świat, odgrywa postacie, pilnuje reguł i dobiera wyzwania do drużyny, która akurat gra.

Like a game master at the table: it invents the world, plays the characters, keeps the rules and pitches challenges to the group playing.

Everyday example

Wrogowie w strzelance flankują cię i chowają się za osłonami, a każda wyprawa w grze przygodowej prowadzi przez inaczej ułożone lochy.

Enemies in a shooter flank you and duck behind cover, and every run of an adventure game leads through differently laid-out dungeons.

This concerns you if you…

• grasz w gry i ciekawi cię, co steruje przeciwnikami
• tworzysz gry albo chcesz zacząć
• chcesz zrozumieć, jak AI uczy się przez zabawę
• play games and wonder what drives the opponents
• make games or want to start
• want to see how AI learns through play

Subtopics

• Agenci grający w gry (Game-playing agents): Agents reaching high skill in board, card and video games through search, reinforcement learning, self-play and visual understanding.
  • Agenci ogólni grający w wiele gier (Generalist game agents): Agents that take screen images and language instructions and act via keyboard and mouse across many 3D games, often built around a vision-language model.
  • Automatyczne testowanie gier przez agentów (Automated playtesting with agents): Using agents trained by reinforcement, imitation or exploration to cover the game space, find bugs and exploits, and assess difficulty.
  • Gry z ukrytą informacją (poker, CFR) (Imperfect-information games and counterfactual regret minimisation): Algorithms for hidden-information games built on iterative counterfactual regret minimisation, game abstraction and real-time search.
  • Uczenie grania z filmów z rozgrywki (VPT) (Video pretraining from gameplay videos): Semi-supervised imitation: an inverse dynamics model trained on a small labelled set annotates a large video corpus, then a policy is behaviour-cloned on it.
  • Uczenie przez grę z samym sobą (self-play) (Self-play and league training): Training agents through games against current or past versions of themselves, often with a population of strategies (a league) to prevent cyclic forgetting.
• Animacja postaci sterowana danymi (Data-driven character animation): Techniques generating body and facial motion of characters from motion-capture data: frame search, neural motion controllers, generative motion models and audio-driven animation.
  • Animacja twarzy z nagrania głosu (Audio-driven facial animation): Models mapping a speech signal to a sequence of facial-animation parameters for a 3D character, followed by retargeting the motion to its rig.
  • Dopasowywanie ruchu z bazy nagrań (motion matching) (Motion matching): A kinematic animation method that every frame or few frames searches the recording database for the nearest pose-and-trajectory feature vector and jumps playback there.
  • Generowanie ruchu postaci z opisu tekstowego (Text-to-motion generation): Generative models, most often diffusion transformers, producing skeleton pose sequences conditioned on text, an action label or other signals.
  • Neuronowe sterowanie ruchem postaci (Neural character controllers): Autoregressive networks predicting the next frame's pose from the previous state, control input and scene geometry, often with weights that depend on the motion phase.
• Modelowanie gracza (Player modelling): Building computational models of players' skill, preferences, emotions and behaviour from gameplay data, physiological signals and context.
  • Bayesowskie oceny umiejętności graczy (Bayesian player skill rating): Bayesian inference of latent skill and its uncertainty from individual or team outcomes; ratings can support balanced matchmaking.
  • Behawioralne wykrywanie oszustw w grach (Behavioural cheat detection in games): Classifying cheating programs from multivariate time series of player input, without access to in-game data.
  • Dynamiczne dopasowanie trudności (Dynamic difficulty adjustment (DDA)): Real-time tuning of game parameters to the player's skill or emotional state, estimated from gameplay performance or physiological signals.
  • Grupowanie graczy według stylu gry (Player segmentation by behavioural clustering): Unsupervised clustering of behavioural telemetry (k-means, archetypal analysis, matrix factorisation) into interpretable player profiles for game design and analytics.
  • Przewidywanie odejść graczy (Player churn prediction): Classification predicting whether a player will stop playing within a defined window, based on session telemetry and aggregated activity features.
• Proceduralne generowanie treści gier (Procedural content generation (PCG)): Algorithmic creation of game content — levels, maps, quests, items, rules — using constructive methods, search, constraint solving or machine learning.
  • Generowanie poziomów uczone na przykładach (Procedural content generation via machine learning (PCGML)): Generating game content with models trained on existing content, including Markov chains, recurrent networks, autoencoders and generative networks.
  • Generowanie terenu z szumu i fraktali (Noise- and fractal-based terrain generation): Classic terrain PCG: heightmaps from gradient or value noise summed over octaves and subdivision algorithms (diamond-square) approximating fractional Brownian motion.
  • Generowanie treści przez przeszukiwanie (ewolucyjne) (Search-based procedural content generation): Generating game content with metaheuristics, mostly evolutionary, which search a space of content representations guided by a quality evaluation function.
  • Generowanie z gramatyk i L-systemów (Grammars and L-systems for content generation): Content generation by repeatedly applying production rules: parallel string rewriting (L-systems) for plants and graph grammars for missions and level layouts.
  • Generowanie z lokalnych wzorów (Wave Function Collapse) (Wave Function Collapse and constraint-based generation): Generating maps and textures by solving neighbourhood constraints learned from an example or given as a tile list, collapsing the lowest-entropy cells first.
  • Grywalne światy z modeli generatywnych (neuronowe silniki gier) (Neural game engines and interactive world generation): Generative models, usually diffusion or autoregressive, that predict the next frames of an interactive world from image history and player actions in real time.
  • Interaktywna fabuła i reżyser historii (Interactive narrative and drama management): Systems in which an experience manager tracks player actions and steers events and characters to reconcile player agency with plot coherence.
• Zachowanie postaci w grach (NPC behaviour and game character AI): Techniques that drive non-player characters: state machines, behaviour trees, goal planning, pathfinding and characters driven by language models.
  • Automaty stanów dla postaci (FSM) (Finite state machines for game characters): A control model where a character is always in exactly one state and events trigger defined transitions; hierarchical and stack variants add sharing and memory.
  • Drzewa zachowań (Behaviour trees): A hierarchical, modular control structure in which sequence, selector and action nodes are ticked periodically and return success, failure or running.
  • Hierarchiczne planowanie zadań postaci (HTN) (Hierarchical task network planning for game characters): A planner that decomposes compound tasks into primitive ones via conditioned methods, simulating action effects on a copy of the world state.
  • Mapy wpływów do decyzji taktycznych (Influence maps for tactical decisions): A grid representation of space in which agents spread proximity and threat values that decay with distance; layers combined arithmetically serve tactical queries.
  • Planowanie działań postaci (GOAP) (Goal-oriented action planning for game characters): Real-time planning for characters in which the system searches for a sequence of actions described by preconditions, effects and costs.
  • Postacie rozmawiające dzięki modelom językowym (LLM-driven NPCs and generative agents): Characters and social simulations driven by language models with memory, reflection and planning, grounded in game rules and state.
  • Siatki nawigacyjne i odnajdywanie drogi (Navigation meshes and pathfinding): Representing walkable space as a mesh of convex polygons generated from level geometry, with pathfinding and collision avoidance on top.
  • Wybór działań przez punktację (utility AI) (Utility AI for game characters): A decision architecture in which every action receives a normalised score from weighted considerations passed through response curves, and the agent picks the highest-utility action.

Related topics

• Robotyka i embodied AI (Robotics and embodied AI)

Sources

• Artificial Intelligence and Games (2nd edition) · Georgios N. Yannakakis, Julian Togelius
• Large Language Models and Games: A Survey and Roadmap · Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis