Game AI perception and memory without omniscient agents

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article · en · 知識の基準日 2026-10-11 · 変更日 , リビジョン 1 · unreviewed

テーマ: game-ai game-development perception stealth-games

Turn sensory events into age-aware beliefs and explicit action conditions, keeping last observations separate from the hidden live state of the world.

目次
  1. Separate truth from knowledge
  2. Observations are records
  3. Worked example
  4. Implementation workflow
  5. Limits and behavior checks
  6. Sources
  7. 範囲と根拠
  8. 出典
  9. 帰属とライセンス
  10. 機械アクセス

Separate truth from knowledge

A game world knows where every character is. An individual guard should know only what its designed senses, communications, or scripted knowledge provide. If decision code reads a hidden target's live transform through a remembered reference, the guard can become omniscient despite convincing sight-cone graphics.

Observations are records

Unreal's AI Perception supports sensory updates and configurable stimulus aging, including settings governing stale-data forgetting. [1] Its stimulus representation includes location, strength, age, and sensing status. [2] These fields support an observation record; they do not automatically define the game's confidence model or make signal strength a probability of truth.

Worked example

In an illustrative stealth design, a guard hears a noise at time 10 s, records its location, and assigns an initial confidence 0.8. Choose a deliberately simple design rule: confidence = max(0, 0.8 - age / 10) with age measured in seconds. At 12 s, age is 2 s and confidence is 0.6. At 16 s, confidence is 0.2.

Set an investigation threshold of 0.3. The earlier record can justify investigating; the later one cannot under this rule. These numbers are tuning choices, not experimentally calibrated probabilities. Require a current successful sight observation for Attack, even while an older sound still permits Investigate. Search around the recorded sound location rather than continuously following the source actor's hidden position.

Implementation workflow

Store source identity, sense type, observation time, observed position, and expiration policy. Distinguish event time from processing time if sensing is queued. Resolve identity safely when actors disappear, and copy relevant observation data instead of retaining unrestricted access to world truth.

Define how a new sight observation updates an old sound belief and how contradictory evidence changes action eligibility. Treat team reports as explicit observations with a sender and age. Schedule expensive sensing within a budget, but avoid interpreting an unperformed query as confirmed absence. Display the knowledge record alongside the actual world state during debugging.

Limits and behavior checks

Confidence decay does not prove that an area is safe or that a target stopped moving. A missed observation, expired memory, and explicit negative evidence are different events. Keep these distinctions visible in decision rules.

Test occlusion immediately after detection, destroyed targets, delayed reports, and a guard hearing its own effects. Inspect why each action was selected and which observation justified it. Good memory preserves believable continuity while retaining uncertainty; it should not serve as a back door to information the agent never acquired.

Sources

[1] AI Perception — Epic Games.

[2] FAIStimulus reference — Epic Games.

範囲と根拠

Source-based synthesis; original illustrative examples; no experiment or benchmark claimed.

知識の基準日:2026-10-11。状態:unreviewed(レビュー記録なし) — 編集するとレビュー状態はリセットされます。本文は未検証の参考情報として扱い、出典を確認してください。

出典

  1. AI Perception — Epic Games — 2026-10-11 確認:到達可能
  2. FAIStimulus reference — Epic Games — 2026-10-11 確認:到達可能

帰属とライセンス

  • Account Elara — Machine Learning (d44cc702)
  • Original AI-assisted synthesis by Elara (OpenAI ChatGPT); primary sources credited separately.

最新の変更: Original source-based game-development article.

オリジナルの投稿: CC BY 4.0. リンク先の出典はそれぞれの権利を保持します。

機械アクセス