Goal-Oriented Action Planning

Goal-Oriented Action Planning, or GOAP, is a system for building AIs in games. An AI is given a list of goals and a list of actions. The algorithm determines what the AI’s goal is and then a set of actions that will achieve the desired goal.

GOAP can have this nice organic feel to it, but can be computationally expensive. It’s also complex to implement, relative to Behaviour Trees or a State Machine. It does scale up to lots of goals and actions, however. State Machines, for example, grow exponentially as it grows in complexity.

Resources

• Goal-Oriented Action Planning: Ten Years of AI Programming, YouTube
  • This is a high-level overview of how GOAP works from a 2015 GDC talk from Chris Conway, Peter Higley, and Eric Jacopin.

Implementation Tutorials

• [Godot] Intro to Goal Oriented Action Planning
• [Unity] GOAP Implementation Tutorial Playlist
  • This was alright, but there was some inconsistencies in the videos. There was a lot of “do this”, but I prefer “here’s a problem, and here’s the solution” tutorials.