Yes it is, it’s 100% scripted. And yes, in the environment where you can do like 10 different actions, they start to do their routine adding ones that you used in that cycle before they get reset. In a sense, they act no more natural than monsters from a tabletop game.

But these do make me think that if we talk gamedesign with a LLM as an actor, it should too have a very tight set of options around it to effectively learn. The ideal situation is something simplistic, like Google’s dino jumper where the target is getting as far as it can by recognising a barrier and jumping at the right time.

But when things get not that trivial, like when in CS 1.6 we have a choice to plant a bomb or kill all CTs, it needs a lot of learning to decide what of these two options is statistically right at any moment. And it needs to do this while having a choice of guns, a neverending branching tree of routes to take, tactics to use, and how to coexist with it’s teammates. And with growing complexity it’s hard to make sure that it’s guided right.

Imagine you have thousands of parameters from it playing one year straight to lose and to win. And you need to add weight to parameters that do affect it’s chance to win while it keeps learning. It’s more of a task than writing a believable bot, that is already dificult.

And the way ECHO fakes it… makes it less of a headache. Because if you limit possible options to the point close to Google’s dino, you can establish a firm grasp on teaching the LLM how to behave in a bunch of pre-defined situations.

And if you won’t, it’s probably easier to ‘fake it’ like ECHO or F.E.A.R. does giving a player an impression of AI when it’s just a complicated scri orchestrating the spectacle.