Almost two years ago I got ahold of some of the AI papers coming out of the University of Alberta. I was totally off-grid then and couldn't *do* anything but dream. But, man! They sure inspired me.
Recently, while looking for the link to share with someone, I revisited the Scriptease site. They haven't stood still.
They have some good stuff there! I especially like the possibilities inherent in a synergy between their ALeRT-AM learning AI
U of A Abstract
Our goal is to provide learning mechanisms to game agents so they are capable of adapting to new behaviors based on the actions of other agents. We introduce a new on-line reinforcement learning (RL) algorithm, ALeRT-AM, that includes an agent-modeling mechanism. We implemented this algorithm in BioWare Corp.’s role-playing game, Neverwinter Nights to evaluate its effectiveness in a real game. Our experiments compare agents who use ALeRTAM with agents that use the non-agent modeling ALeRT RL algorithm and two other non-RL algorithms. We show that an ALeRT-AM agent is able to rapidly learn a winning strategy against other agents in a combat scenario and to adapt to changes in the environment.
and the Behavior-MultiQueue system.
U of A
A movie clip (www.cs.ualberta.ca/~script/movies/tavern.mov) shows a tavern scene with one owner, two servers, and eighteen patrons. This scene runs in game at more than 30 frames per second, despite the high activity in the scene. We have run this scene for days without any noticeable stalling of behaviors or NPCs who stop performing their designated behaviors. This illustrates that the multi-queue approach is both efficient and robust enough for commercial computer games.
(Note: Their ALeRT-AM AI can achieve 80-90% wins vs. NwN scripted AI. Interesting, no?)
Is anyone here mucking about with Scriptease? Can I get some other viewpoints not tinted with these rosy glasses? :-)
<...to keep it from exploding>
Modifié par Rolo Kipp, 09 octobre 2011 - 09:34 .





Retour en haut







