# AI w grach

EN: **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

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › AI w grach

## Description

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

- PL: Sztuczna inteligencja, która gra w gry, tworzy do nich poziomy i postacie oraz dopasowuje rozgrywkę do konkretnego gracza.
- EN: Artificial intelligence that plays games, builds their levels and characters, and tunes the experience to each player.

## Problem it solves

- PL: Ręczne budowanie światów, przeciwników i testów pochłania lata pracy, a jedna stała trudność nudzi jednych graczy i zniechęca innych.
- EN: Hand-building worlds, opponents and tests takes years of work, and one fixed difficulty bores some players while putting others off.

## What it does

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

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

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

## Keywords

procedural content generation · game-playing agents · NPC behaviour · behavior trees · player modeling · self-play · Monte Carlo tree search · counterfactual regret minimization · dynamic difficulty adjustment · automated playtesting · neural game engines · generative agents · General Video Game AI · IEEE Conference on Games

## How it works

- PL: Gry są dla AI produktem i poligonem zarazem. Agentów grających trenuje się przeszukiwaniem i uczeniem ze wzmocnieniem, często przez grę z samym sobą. Treść generuje się przeszukiwaniem, regułami ograniczeń i modelami generatywnymi, a dane z rozgrywki zasilają modele umiejętności, emocji i zachowań graczy.
- EN: Games are both a product and a testbed for AI. Game-playing agents are trained with search and reinforcement learning, often through self-play. Content is generated with search, constraint rules and generative models, while gameplay data feed models of players' skill, emotions and behaviour.

## Limits

- PL: Mistrzowski agent nie musi być zabawnym przeciwnikiem: gry potrzebują wiarygodności i kontroli projektanta, nie tylko wygranej. Metody badawcze są drogie obliczeniowo, a generowana treść bywa niegrywalna lub powtarzalna. Studia ostrożnie podchodzą też do praw autorskich i reakcji graczy.
- EN: A champion agent is not necessarily a fun opponent: games need believability and designer control, not just winning. Research methods are compute-hungry, and generated content can be unplayable or repetitive. Studios are also wary of copyright and player backlash.

## Key terms

- PL: **NPC**: Postać niezależna, sterowana przez grę, a nie przez gracza.
- PL: **PCG**: Proceduralne generowanie treści: poziomy, mapy czy zadania tworzone przez algorytm.
- PL: **self-play**: Trening, w którym program gra przeciw kopiom samego siebie.
- PL: **modelowanie gracza**: Przewidywanie umiejętności, emocji i zachowań gracza z danych.
- EN: **NPC**: Non-player character, controlled by the game rather than a player.
- EN: **PCG**: Procedural content generation: levels, maps or quests built by an algorithm.
- EN: **self-play**: Training in which a program plays against copies of itself.
- EN: **player modelling**: Predicting a player's skill, emotions and behaviour from data.

## Know first

- [Podstawy RL (Reinforcement learning fundamentals)](https://aiatlasgraph.com/t/rlbasics.md)
- [Gry i przeszukiwanie z przeciwnikiem (minimax) (Adversarial search and games (minimax, alpha-beta))](https://aiatlasgraph.com/t/adversarialsearch.md)

## Subtopics (5 direct, 34 in total)

- [Agenci grający w gry (Game-playing agents)](https://aiatlasgraph.com/t/gameplaying.md): 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)](https://aiatlasgraph.com/t/generalistgameagents.md): 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)](https://aiatlasgraph.com/t/gametesting.md): 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)](https://aiatlasgraph.com/t/imperfectinfo.md): 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)](https://aiatlasgraph.com/t/vptgames.md): 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)](https://aiatlasgraph.com/t/selfplay.md): 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)](https://aiatlasgraph.com/t/charanimation.md): 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)](https://aiatlasgraph.com/t/audiofacial.md): 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)](https://aiatlasgraph.com/t/motionmatching.md): 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)](https://aiatlasgraph.com/t/textmotion.md): 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)](https://aiatlasgraph.com/t/neuralcharcontrol.md): 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)](https://aiatlasgraph.com/t/playermodeling.md): 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)](https://aiatlasgraph.com/t/skillrating.md): 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)](https://aiatlasgraph.com/t/cheatdetection.md): Classifying cheating programs from multivariate time series of player input, without access to in-game data.
  - [Dynamiczne dopasowanie trudności (Dynamic difficulty adjustment (DDA))](https://aiatlasgraph.com/t/dda.md): 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)](https://aiatlasgraph.com/t/playersegmentation.md): 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)](https://aiatlasgraph.com/t/churnprediction.md): 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))](https://aiatlasgraph.com/t/pcg.md): 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))](https://aiatlasgraph.com/t/pcgml.md): 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)](https://aiatlasgraph.com/t/noiseterrain.md): 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)](https://aiatlasgraph.com/t/searchpcg.md): 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)](https://aiatlasgraph.com/t/grammarpcg.md): 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)](https://aiatlasgraph.com/t/wfc.md): 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)](https://aiatlasgraph.com/t/neuralgameengines.md): 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)](https://aiatlasgraph.com/t/narrativegen.md): 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)](https://aiatlasgraph.com/t/gamecharacters.md): 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)](https://aiatlasgraph.com/t/gamefsm.md): 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)](https://aiatlasgraph.com/t/behaviortrees.md): 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)](https://aiatlasgraph.com/t/htngames.md): 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)](https://aiatlasgraph.com/t/influencemaps.md): 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)](https://aiatlasgraph.com/t/goap.md): 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)](https://aiatlasgraph.com/t/llmnpc.md): 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)](https://aiatlasgraph.com/t/navmesh.md): 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)](https://aiatlasgraph.com/t/utilityai.md): 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

Untyped, symmetric "see also" links.

- [Robotyka i embodied AI (Robotics and embodied AI)](https://aiatlasgraph.com/t/robotics.md)

## Sources

- [Artificial Intelligence and Games (2nd edition)](<https://gameaibook.org/>) `yannakakis-togelius-aigames-2025` · by Georgios N. Yannakakis, Julian Togelius
- [Large Language Models and Games: A Survey and Roadmap](<https://arxiv.org/abs/2402.18659>) `gallotta-llm-games-2024` · by Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis

---

AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/gameai · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30