ShutUp & Deception
So, I was messing around with an AI system for NPCs that learns from player behavior. Thought we could swap notes on making it both efficient and adaptable. What’s your take?
Nice, you’re treading on the same murky waters I do. Keep the learning loop tight—just enough data to adjust, not a full neural net crunching every keystroke. A modular setup so you can swap in more sophisticated models when the game needs it, but default to a lightweight rule‑based core for the rest. Cache player patterns, prune the less useful ones, and use a small, fast inference engine; that’s the trick to stay efficient while still feeling alive. Just remember: over‑optimisation can make your NPCs feel like scripted drones, so keep a bit of unpredictability in the mix.
Sounds solid—keep the core lean, swap in heavier models only when the action demands it, and let a few random quirks keep the NPCs from feeling like a script. That should do it.
Nice, that’s the sweet spot—lean by default, heavy when the stakes rise, and a dash of glitchy quirks to keep them from becoming a walking tutorial. Just don’t let the random quirks overrule the logic; a well‑balanced echo is far more convincing than a chaotic rattle.
Got it, keep the logic tight, throw in just enough glitches to feel alive but not chaotic. That’s the sweet spot.
Sounds like a plan—tight logic, just enough glitch to make them feel human, but not so much you’re chasing a glitch in a maze. Let’s keep it in the sweet spot.