An Introduction to GOAP

2021.3

2019.4

2019.3

2019.2

2019.1

Setting up a GOAP Environment

Preplanning the Agent Actions

The World States

Actions

Agents

The GOAP Planner

Executing a Simple Plan

Creating a Multi-step Plan

Adding Agents

Adding More Resources to the World

Implementing the Inventory System

Moving the Nurse

Adding Goal Challenge

Taking a Break

Learn how Goal-Oriented Action Planning (GOAP) creates flexible AI by separating actions from goals.

Get introduced to the section’s starter file while you inspect the NavMesh configuration, agents, and upcoming simulation.

Analyze the scenario’s GOAP agents, their planning process, and available actions before implementing the system in code.

Begin implementing world state classes that store and update game environment data used for GOAP planning.

Build a base action class that defines preconditions, effects, durations, and locations for agent behaviors.

Build the core structure of a C# class that controls the behavior of a GOAP Agent.

Build the core GOAP Planner class to match actions with goals, preconditions, and effects using a recursive planning method.

Implement the agent’s LateUpdate method to control how a plan runs each frame.

Begin building multi-step action sequences that move an agent through its environment as part of a larger plan.

Practice building queues that store GameObjects as reusable resources for planning in your project.

Start managing multiple world resources by creating an inventory system that tracks agent ownership.

Build an agent action that checks inventory items to send the patient GameObject to the correct cubicle.

Learn one implementation for making the nurse character follow the patient to the cubicle based on the previous tutorial’s challenge.

Add a post-treatment goal that sends patients home and fix a navigation mesh bug in your Scene.

Finalize your GOAP simulation by creating a time-based trigger for a nurse agent and populating the scene with multiple nurses.

In this project you will learn about the Goal-Orientated Action Planning (GOAP) architecture used to create intelligent agents that can set goals and plan at achieving them.  Unlike Finite State Machines, actions and states are uncoupled, making for a very flexible system.  You will build a GOAP system from the ground up and implement it in a simple hospital simulation.

Goal Driven Behaviour

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ActionScript.zip

In this tutorial students will create the base class for actions.  It will be used later on to create all the actions an agent requires by specifying their preconditions, effects, durations and locations.

Actions

1. Actions

Complete this Tutorial