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How AI and machine learning are applied when creating games
AI in 2025 is not a magic button, but a working infrastructure that speeds up production, supports creativity and helps make data-driven decisions. Below is a map of AI/ML applications throughout the cycle: pre-production → production → testing → launch → live ops.
1) Pre-production: research, idea, prototype
1. 1. Market and audience analytics
Clustering of players by interests and payment behavior (unsupervised learning).
Prediction of virality and genre trends (time-series + gradient boosting).
Semantic analysis of reviews/forums (LLM/embeddings) to identify segment "pains."
1. 2. Ideation and fast proto
Generation of draft concepts of levels/quests (procedural content generation, PCG) with control of game design restrictions.
LLM as "co-designer": writing versions of lore, descriptions of objects, NPC replicas - with the final editing passage of a person.
Fast game loops (core loop) with economy simulators: agent models check the stability of the "soft currency," the pace of progress and the "bottlenecks" of the gameplay.
Tools: Python, PyTorch/TF, JAX for prototypes; Unity ML-Agents, Unreal AI/Behavior Trees; simulation environments (Gym-compatible), embedding vectors (FAISS).
2) Production: Content, Mechanics, NPC Intelligence
2. 1. Generation and Asset Pipeline
PCG levels: graph/evolutionary algorithms and diffusion models for variable maps, puzzles, dungeons; metric checks (patency, readability, time-to-complement).
Audio/voice acting: TTS/Voice Cloning for draft lines and emotion variation; final localization - under the control of the sound director.
Art assets: generative models for references and variations - with a strict legal policy of datasets and the obligatory work of the finalist artist.
2. 2. Game math and behavior
Adaptive difficulty (DDA): player models (skill models) and feedback loops that dynamically adjust the frequency of events, the health of enemies, prompts.
NPC and tactics: RL/IL (renewal/imitation learning) for behaviors that learn from "recordings" of tester sessions; decision trees/GOAP for predictability.
Dynamic directing: "conductor" of events, adjusting the intensity of the battle/puzzle without interfering with the honesty of the RNG.
2. 3. Performance and optimization
Auto-LOD and ML-based asset compression; texture upscale (SR).
It is an inference device (mobile/consoles) with quantization (int8), prying and distillation for 60-120 FPS.
3) Testing: quality, balance, anti-cheat
3. 1. Automated playtesting
Agent bots passing levels on different styles of play; regression tests of "impossible" states.
Models that catch "dead" loops, soft locks, exploits of the economy.
3. 2. Anti-cheat and anti-fraud
Anomaly detection: atypical input/speed patterns, client spoofing, macros.
Graph models for coordinated cheating and butnet.
On servers - real-time rules + ML scoring with human verification for controversial cases.
3. 3. Balance and economics
Bayesian adjustment of loot/complexity parameters; multi-body optimization (fun, progress, retention).
Simulations of seasons/events before deploy.
4) Launch and live ops: personalization, retention, monetization
4. 1. Player models and recommendations
Personal collections of modes/missions/skins (recsys): ranking by the probability of involvement, and not just by coin.
Contextual tutorials and "smart clues" - reduce the cognitive load of beginners.
Important: personalization does not change the honesty of the drops and the basic chances of the mechanic - it controls the delivery of content and training.
4. 2. Live balance and A/B experiments
Fast A/B/n-cycles with metrics: D1/D7/D30, game time, frustration level (proxy metrics), NPS, ARPDAU.
Causal inference (uplift models) - to distinguish correlation from the effect of change.
4. 3. Responsible play and safety
Real-time detection of risky patterns (tilt, "dogon," bursts of spending) → soft prompts/timeouts/limits.
Transparent logs and privacy control (data minimization, anonymization, metadata storage separately).
5) Data architecture and MLOps
5. 1. Collection and preparation
Client and server telemetry (events, economic transactions, device profiles).
Cleaning/normalization, deduplication, reconciliation of build versions and event schema.
5. 2. Training and Deploy
Feature stores for repeatability; pipelines in the orchestrator (Airflow/Dagster).
CI/CD for models: comparison with baselines, automatic "canary" calculations.
Drift monitoring: if the feature distributions are gone, the model goes into degrade mode or fallback rules.
5. 3. Inference
On-device: low latency, privacy; memory/energy constraints.
Server: heavy models, but need protection against overloads and queues.
6) Ethical and legal aspects
Datasets: licenses and origin, prohibition of toxic content in NPC dialogue training.
Transparency: Players understand where AI "directs experience" and where strict probabilities/rules apply
Privacy: minimization of personal data, storage of aggregates, the ability to delete data on request.
Accessibility: AI clues and voice acting improve accessibility for players with special needs.
7) Practical scenarios by genre
Action/adventure: DDA, tactical NPC, generation of side quests, dynamic combat directing.
Strategies/sims: agent economies, demand/price forecast, training AI rivals on behavioral trajectories.
Puzzles/casual: auto-generation of levels with a target transit time, personal tips.
Online projects/seasons: recommendation events, segmentation of "returnees," toxicity-moderation of chats.
8) Tools and Stack (2025)
ML/DL: PyTorch, TensorFlow, ONNX Runtime (quantization/acceleration).
Game AI: Unity ML-Agents, Unreal EQS/Behavior Trees/State Trees.
Data & MLOps: Spark, DuckDB/BigQuery, Airflow/Dagster, Feast (feature store), MLflow/W&B.
Generation: diffusion models for art/audio, LLM scriptwriters with rule controllers.
Real time: gRPC/WebSocket, streaming telemetry, AB platforms.
9) Success metrics
Gaming: tutorial-completion, "time to first fan," win/lose streak fairness perception,% of "dead" levels.
Grocery: D1/D7/D30, sessions/day, retention cohorts, churn scoring.
Those: FPS p95, delay in inference, drift of features, share of folbacks.
Quality/security: bug rate, cheat incidents/million sessions, false positive with anti-cheat.
10) Typical mistakes and how to avoid them
1. Retraining on "old" patterns. - Introduce regular re-training and drift monitoring.
2. LLM without rules. - Wrap "agents" in an orchestrator with restrictions and test scripts.
3. Mixing personalization and honesty. - Firmly separate RNG/odds from UX recommendations.
4. Lack of offline ethics of datasets. - Document sources, undergo legal review.
5. No folbacks. - Any AI module must have a "manual mode" or a simple heuristic layer.
Mini checklist for the team
- Telemetry map and single event map.
- Feature store and basic baselines for each task.
- CI/CD for models + canary releases.
- Privacy policy and explainability of decisions.
- Split: RNG/odds - unchanged; AI manages submission and training.
- A/B-plan: hypothesis → metrics → duration → stopping criterion.
- A set of "red flags" for anti-cheat and risk patterns.
AI and ML are no longer an experiment: this is the infrastructure of gamedev. They speed up art and code, help balance economies, make NPCs smarter and onboarding softer. The key to success is data with discipline, correct MLOps processes, transparency for the player and a clear line between fair chance and adaptive experience directing.