Instead, the focus is on simply defining the specific reasons why the single behavior in question would be beneficial (i.e. its "utility").
Only in the early 21st century, however, has that method started to take on more of a formalized approach now referred to commonly as "utility AI".
While the player didn't see the calculations themselves, they were made aware of the relative needs of the Sim and the varying degrees of satisfaction that objects in the game would provide.
[5] These lectures served to inject utility AI as a commonly-referred-to architecture alongside finite state machines (FSMs), behavior trees, and planners.
[7] The IAUS was designed to be a data-driven, self-contained architecture that, once hooked up to the inputs and outputs of the game system, did not require much programming support.
Bill Merrill wrote a segment in the book, Game AI Pro,[8] entitled "Building Utility Decisions into Your Existing Behavior Tree"[9] with examples of how to re-purpose selectors in BTs to use utility-based math.
This made for a powerful hybrid that kept much of the popular formal structure of behavior trees but allowed for some of the non-brittle advantages that utility offered.