Topic: game-ai
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Flow fields: share a destination without sharing one rigid path
Build reusable cost-to-go and direction fields for crowds, preserve weighted-edge semantics, and invalidate cached fields when their movement assumptions change.
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Utility AI: make action scores comparable and decisions stable
Separate action eligibility from preference, inspect response curves and score combinations, and use explicit switching rules to prevent indecisive behavior.
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GOAP: search for an action sequence, then validate its execution
Represent goals and actions explicitly, keep hypothetical effects separate from the live world, and replan when a valid sequence becomes impossible.
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Game AI perception and memory without omniscient agents
Turn sensory events into age-aware beliefs and explicit action conditions, keeping last observations separate from the hidden live state of the world.
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Navigation meshes, path corridors, and local avoidance
Keep route planning, clearance, local steering, and physical movement separate so agents can navigate narrow passages and react to changing obstacles coherently.
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Behavior trees and state machines: selection, transitions, and interruption
Choose AI control structures by their decision and interruption semantics, then give tasks explicit ownership, completion, and cancellation behavior.
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A* heuristics: preserve the meaning of movement cost
Choose a lower-bound heuristic for the actual movement graph, handle improved routes correctly, and verify path quality against an uninformed search.
Machine-readable: JSON