enemy-ai-framework

Designing enemy AI behavior trees that feel fair requires modeling perception, threat, and combat pacing consistently. This Skill helps structure FSMs, GOAP, or hybrid architectures to ensure attacks, telegraphs, and targeting rules remain readable and balanced.

Enemy AI flip-flops targets or swarms the player due to missing aggro rules, hysteresis, or combat tokens. This Skill diagnoses these encounter design failures and provides architectures for target selection, attack tokens, and group roles to restore combat pacing.

Structuring enemy perception and alert states requires building sight, hearing, and threat modeling systems. This Skill outlines architectures for consistent aggro rules, alert states, and hysteresis to ensure stealth encounters feel intelligent without becoming exploitable.

Utility scoring and HTN planners are highly applicable to large-scale crowd AI in simulation games. This Skill guides architecture choices for AI LOD, group roles, and lifecycles to ensure scalable performance across action combat, RTS, racing, and crowds.

Preventing enemy AI from feeling too smart involves implementing combat pacing, leashes, and AI LOD. This Skill provides design frameworks for encounter composition and telegraphs to balance challenge and fairness so enemies remain engaging without being robotic.

Choosing between behavior trees and GOAP depends on your game's role, encounter design, and performance needs. This Skill compares FSMs, utility scoring, HTN, and hybrid patterns to help select the architecture that best fits your combat or simulation requirements.