The A* Pathfinding Algorithm Part 1

The A* Pathfinding Algorithm Part 2

The A* Pathfinding Algorithm Part 3

The A* Pathfinding Algorithm Part 4

The A* Pathfinding Algorithm Part 5

The A* Pathfinding Algorithm Part 6

In this tutorial you will learn about the fundamental concepts used in the creation of Behaviour Trees.  You will also take this knowledge and apply it in Unity by building a simple simulation scenario where a behaviour tree is used to define action sequences of a thief stealing a diamond from a gallery.

The A* Algorithm

Welcome to the Course

Introduction to Artificial Intelligence

Not So Scary Vector Mathematics

Cartesian Coordinates

Vectors

Calculating Distance

Dot Product

Cross Product

AutoPilot

Time

Velocity

Acceleration

Calculating Trajectories

Waypoints

Following a Circuit&Tracker

Using A* with Waypoints Part 1

A Simple Graph API

Using A* with Waypoints Part 2

Commanding a Path

Navigation Mesh Introduction

From Waypoints to NavMesh

NavMesh Agents

Following a Player on a NavMesh and Setting up Off-Mesh Links

Finite State Machines

Creating and Using a State Class

Patrolling the Perimeter

Chasing the Player

Finite State Machine Challenge

Seek and Flee

Pursuit

Evade

Wander

Hide

Complex Behaviours

Behaviour Challenge

Moving As One

Creating a City Crowd

Fleeing

Flocking

An Introduction to GOAP

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

Introducing Behaviour Trees

Nodes

Tree Printing

Leaf and Action Nodes

NavMesh Movement

Sequences

Selectors

Extended Action Methods

Conditions

In the tutorial you will be introduced to the course content as well as given an overview of the field of AI and how AI in games fits into the domain.

Introduction

In this project, you will learn about the fundamental mathematics used in moving characters around in a game environment. You will follow along as geometry and trigonometry is used to develop methods that move and turn game characters.  With these techniques you'll put together an auto-navigating character that represents the basis of the simple artificial intelligence used in developing non-player characters.  In doing this you will discover the mathematics behind some of Unity's most used Vector and Angle manipulating methods. 

The Mathematics of AI

In this project, you will be introduced to some of the fundamental principles in the field of physics and how they are used to move objects around in the game environment. The relationships between time, velocity and acceleration will be investigated and then applied in the creation of an automated shooting tank that calculates the angle of it's turret based on parabolic mathematics and the position of an enemy.

The Physics of AI

In this project you will learn about the major role that the mathematical construct of graphs plays in developing many artificial intelligence techniques in games.  You will discover how graphs are visualised in a set of waypoints used for non-player character navigation.  In addition we will cover the algorithms used for searching through graphs and develop classes for the A* algorithm, one of the most prolific search methods used with graphs in games.

Waypoints & Graphs

In this project you will be introduced to navigation meshes and learn to use them in a game environment to move characters between goals.  You will develop AI algorithms that command characters to find the path between their current location and destination using the navigation mesh as the waypoint system.  The waypoint mecanics in the previous section will be reworked to use a navigation mesh rather than predefined straight lines between points.  This will make for more dynamic and intelligent appearing non-player characters.

Navigation Meshes

In this project you will learn about the design patter of "states" and use them to construct an AI behavioural algorithm called a Finite State Machine.  This tutorial will combine you knowledge of NavMeshes and Graphs.

In this project you will be working with NavMesh agents to recreate some very popular steering behaviours to create non-player characters that move around independently in a game environment and make choices about their movements based on the location and orientation of the player character.

Autonomously Moving Agents

In this project you will be working with a high number of agents to simulate crowd behaviour.  You will discover how patterns of movement can be formed from the simpliest of rules that mimic crowd behaviour in the real world.  By the end you will have created a crowded city street scene with humanoid characters walking along a pavement and avoiding each other as well as a simulation of a school of fish.

Crowd Simulation

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

In this project you will learn about the fundamental concepts used in the creation of Behaviour Trees. You will also take this knowledge and apply it in Unity by building a simple simulation scenario where a behaviour tree is used to define action sequences of a thief stealing a diamond from a gallery.

Behaviour Trees

In this course, Dr Penny de Byl reveals the most popular AI techniques used for creating believable game characters using her internationally acclaimed teaching style and knowledge from over 25 years researching and working with games, computer graphics and artificial intelligence. Throughout, you will follow along with hands-on workshops designed to teach you about the fundamental AI techniques used in today's games.  You'll join in as NPCs are programmed to chase, hide, flee, patrol, shoot, race, crowd, plan and much more. 

2021.3

Artificial Intelligence for Beginners

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AStarPart5Solution.zip

S0405.mp4

In this tutorial students will finalise their A* code project and find the final path between the start and the end markers.

AI, ML Curricular Framework Resources 

In this unit students will continue working on the A* code to add the necessary functionality to find the most efficient path through the maze.

The A* Pathfinding Algorithm Part 5

1. The A* Pathfinding Algorithm Part 5

Complete this tutorial