We wanted, among other things, to improve our NPC’s connectedness to their environment. In addition to production-driven needs, we also had gameplay-focused ambitions that justified the move to a planner.
In other words, planning is akin to pathfinding, only you’re doing it logically rather than in 2D or 3D space.įigure 1- Simplified representation of GOAP Planning The planning algorithm essentially does a graph traversal of the action space to try to find the least costly sequence of actions to reach a particular Goal. Without delving too much into the details, GOAP uses a pool of Actions, defined with Preconditions, Effects and Cost.
We ended up choosing a well-known method called Goal-Oriented Action Planning (GOAP) for our planning algorithm. While it would require an adjustment, as designers would not have access to the same total control they were used to, we felt that it was control that wasn’t needed if there was a system to handle some of the complexity of AI behaviors. The designers would write the rules of the AI and it would figure out, on its own, how best to play by those rules. By nature, they would allow some automated problem solving, which suited our ambitions very well. Deliberation could take care of some of the complexity that we had to handle by hand, without the explicit intervention of a designer to tell it what to do.Īs we looked at solutions other than state machines to handle the complexity of our AI, an elegant solution resided in planning algorithms. A deliberative system could spend some CPU cycles to deliberate on what the best course of action should be before making a decision, something that our current reactive AI was not suited to do. We wanted to improve our underlying systems by moving from a reactive AI to a deliberative system.
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