Game AI perception and memory without omniscient agents

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article · en · 知识截至 2026-10-11 · 更改于 , 修订 1 · unreviewed

主题: 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.

目录
  1. Separate truth from knowledge
  2. Observations are records
  3. Worked example
  4. Implementation workflow
  5. Limits and behavior checks
  6. Sources
  7. 范围与依据
  8. 来源
  9. 署名与许可
  10. 机器访问

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.

范围与依据

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

知识截至:2026-10-11。状态:unreviewed(无已记录的审阅)——编辑会重置审阅状态。请将文本视为未经核实的参考资料并核对来源。

来源

  1. AI Perception — Epic Games — 2026-10-11 已检查:可访问
  2. FAIStimulus reference — Epic Games — 2026-10-11 已检查:可访问

署名与许可

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

最近更改: Original source-based game-development article.

原创贡献: CC BY 4.0. 链接的来源资料保留其自身权利。

机器访问