The Beginner's Guide to Artificial Intelligence (Unity 2022) | Discount Coupon for Udemy Course

This lecture introduces the course by outlining all the content that will be covered with a section by section breakdown.

In this unit students will gain an understanding of the field of artificial intelligence and how it pertains to game development.

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Important Reading on Common Issues students have and how to ask for help.

In this lecture we will get an overview of vectors and why they are an important concept to have an understanding of before you launch into programming the movement of an NPC.

In this lecture students will learn about the Cartesian coordinate system and how points are defined in the space as well as 2D and 3D nature of a game environment.

In this video students will begin an exploration of vectors and discover their importance in measuring game environment space as well as defining direction of movement of game characters.

In this lecture students will learn how to normalise the movement of a game character by calculating a velocity to gradually move it from one location to another.

In this lecture students will learn how to determine the size of a vector to determine when a character has reached its goal as well as have AI characters follow a player character.

In this lecture students will learn to use Pythagorus' theorem to calculate the distance between points in a game environment and the length of a vector.

In this lecture students will learn how to use Unity methods to calculate distance.

In this lecture students will learn about the derivation of the dot product formulae and how it is used to calculate the angle between vectors for the purposes of rotating a game character.

In this lecture students will be challenged to calculate the angle between vectors to turn a game character to face another.

In this video students will learn about the derivation of the cross product and discover why it is important to include in processes involving the rotation of characters.

In this video students will be challenged to built an autopilot system to drive a character towards a goal and include turning in increments.

In this lecture students will examine the differing game loops that Unity runs and investigate how it affects the timing of character movements.

In this lecture students will learn how to smooth update values to give consistent rates of movement in game characters.

In this lecture students will learn how to work with velocity to control the speed of a rigid body.

In this lecture students will learn how to use velocity vectors to predict where a moving game object will be in the future.

In this lecture students will complete the code that works with a quadratic equation to predict the hit location between a projectile and enemy.

In this unit students will be introduced to acceleration as a means of calculating the shell's velocity and also introduced gravity into the equation.

In this lecture students will add the shell to fire from the red tank and also program it to move under speed and acceleration.

In this lecture students will complete their exploration of projectiles by examining drag and gravity.

In this unit students will learn about the parabola formed by projectile motion and use physics calculations to start programming an automatic attacking tank.

In this lecture students will learn how to calculate the trajectory of an arc made by a moving physics object to determine a hit location.

In this lecture students will learn how to use the trajectory calculations to have a Unity Physics system operated bullet hit its mark as well as create a simple AI character that will shoot at and follow an enemy.

In this lecture students will learn about the concepts behind the A* algorithm and take an in-depth look at its inner workings.

In this lecture students will write some preliminary code to get their project ready for the dedicated AStar method.

In this unit students will begin writing the A* algorithm by creating a start and goal location as well as marking them inside a maze.

In this lecture students will complete most of the code focused on running the A* algorithms logic.

In this lecture students will complete all but the last part of the code focused on running the A* algorithms logic.

In this lecture students will finalise their A* Pathfinding code to search from a starting location to an ending location and then determine the path points between them.

In this unit students will learn about waypoints, how to place them on a map to form a circuit and how to program a vehicle to automatically navigate between them.

In this lecture students will learn how rotational speed and waypoint accuracy affect an AIs ability to traverse waypoints.

In this lecture students will create copies of many tanks with different driving attributes and explore the issues that using waypoints may incur.

In this lecture students will create a tracker object that will make its way around a circuit while the tanks follow.

In this lecture students will learn how to setup waypoints on a map that can later be used with the A* algorithm for finding a path around an environment.

In this lecture we will start writing a new set of classes to store a graph, with it's nodes and edges.

In this lecture students will learn how to introduce the A* algorithm into the graph class.

In this lecture students will complete writing the Graph class with the A* Algorithm code and be ready to continue working on the waypoint system.

In this lecture students will learn how to add the waypoints and edges to the graph and begin programming the AI code for their NPC.

In this lecture students will implement a waypoint path following code for the tank NPC.

In this lecture students will complete programming the waypoint navigation system by adding command buttons to the environment.

In this lecture students will learn how to apply a wheel collider and rigid body to a tyre.

In this lecture students will learn about wheel spring dampening and create a script to apply torque to the wheel.

In this lecture students will learn how to construct a car physics object by combining wheel colliders with meshes.

In this lecture students will learn how to turn the wheel collider so the front wheels can be used to determine a direction of travel.

In this lecture students will learn how to setup a waypoint system to define a circuit for the AI car to drive around.

In this lecture students will learn how to get a simple car NPC up and driving around waypoints using a steering mechanic.

In this lecture students will learn how to apply braking to slow a car as it approaches waypoints.

In this lecture students will learn about the forces placed on tires when driving and create code to flip the cars over when they flip onto their roof.

In this lecture students will learn how improve on the acceleration and braking based on the turning direction.

In this lecture students will learn how to add a progress tracker that can be used to guide the car around the track.

In this lecture students will learn how to add an antiroll bar onto the car to stabilising it from tipping over.

In this lecture students will learn how they can reconfigure the AIController code to organise all the variable settings for their cars.

In this lecture students will learn to create code to assist cars in avoiding other cars on the road.

In this lecture students will learn how to improve the avoiding behaviours and add reversing to their cars.

In this lecture students will learn about the principles and theory behind navigation meshes and how to construct one in Unity.

In this lecture students will learn how to convert a waypoint system to use the Navigation Mesh API that comes with Unity to achieve the same thing.

In this lecture students will practice setting up a navigation mesh from scratch and program a simple AI agent.

In this lecture students will learn some code optimisation techniques as well as program in the animations to synchronise with agent movement.

In this lecture students will learn how to restrict agents from going into certain areas on a navmesh.

In this lecture students will learn how to program AI navmesh characters to follow a player character in addition to using off mesh links to traverse gaps in the navmesh.

In this lecture students will setup an environment ready for creating a finite state machine.

In this lecture students will start coding for the environment and design a state class.

In this lecture students will write up the state code for Idle and Patrolling.

In this lecture students will create the code to control the AI and provide an interface to the states.

In this lecture students will begin developing code so the NPC can determine if it senses the player and then act on it.

In this lecture students will complete the AI code that will cause the NPC to chase and shoot at the player as well as lose interest if the player goes out of range.

In this lecture students will be challenged to add a new state into the NPCs behaviour to make it run away when it is threatened.

In this lecture students will follow along to implement simple seek and flee methods for a Nav Mesh agent.

In this lecture students will learn the difference between seeking and pursuit and be able to add predictive code to their agent to make assumptions about where the target will be in the future.

In this unit students will be challenged to create their own evade method which will see the agent move away from the predicted location of the target.

In this unit students will examine one method of producing a wandering behaviour that will have the agent freely walk around the environment with smooth randomness.

In this video students will take a look at the basics behind the hide behaviour.  In it we will create a simple hide method that will have the agent hide behind the closest obstacle in the environment.

In this unit students complete the development of the hiding behaviour and work to calculate a more accurate hiding location and reduce the number of calls to the method in the update.

In this lecture students will learn how to implement code to stop the robber from moving if it can't see the cop.

In this video students will learn how to combine a number of steering behaviours with simple geometric conditions to produce complex behaviours in agents.

In this video students will be challenged to integrate the wander behaviour into the complex behaviour to have the agents go into a wander when the target cop is out of range.

In this lecture students will learn about the dynamics of crowd behaviour and watch some simple simulations.

In this lecture students will set up a navmesh with numerous animated humanoid agents and begin programming a crowded street scene.

In this lecture  students will finalise their work on creating a crowded city scape where humanoid agents walk from one end of a sidewalk to another.

In this lecture students will setup a project to drop an obstacle into the scene that the agents in the crowd will flee from.

In this lecture students will complete the fleeing simulation by adding code to the agents controller to calculate a flee direction.

In this lecture students will learn how the flocking algorithm works and begin setting up a project to create a schooling fish simulation.

In this lecture students will write a basic flocking algorithm to create a school of fish.

In this lecture students will learn how to add a goal position to the flocking simulation.

In this lecture students will learn how to keep the flocking fish within the confines of a bounding box so they can't swim off into infinity.

In this lecture students will learn about the architecture of a GOAP system.

This lecture introduces the starter file that will be used for this section.  Each part of the game project will be examined with respect to the nav mesh setup, the agents and the simulation about to be created.

Before jumping into the coding of a GOAP system, in lecture unit the agents involved in the scenario will be examined and their planning process and actions discussed.

In this lecture students will start building the world states classes that hold, set and manipulate the states of the game environment required for GOAP planning.

In this lecture 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.

In this lecture students will create the basic structure of a class that will drive the GOAP Agent.

In this lecture students will start working on the basic structure of the GOAP Planner class.  It will be the workhorse of the project matching actions in a plan with goals, preconditions and effects.

In this lecture students will complete writing the basic GOAP Planning class by adding in its all important recursive graph building method.

In this lecture students will write the code that controls the execution of a plan and that's the contents of the agent's LateUpdate method.

In this lecture students will begin creating multiple actions that need to be strung together to create a longer plan that will have the agent moving around the environment.

In this lecture students will create a spawner for patients and place it outside the hospital.

In this lecture students will work to create a plan for the nurse that is triggered by a change in world states.

In this lecture students will learn how to assign another game object to the target destination for the nurse so she can move to her patient.

In this lecture students will work on creating an action that is dependent on items in the agent's inventory.  With this we'll program the patient to go to the cubicle.

In this lecture students will work on creating an action that is dependent on items in the agent's inventory.  With this we'll program the patient to go to the cubicle.

This lecture presents just one way to solve the challenge posed at the end of the previous one and that is how to get the nurse to follow the patient to the cubicle.

In this lecture students will add an extra goal to the patient to make them go home after they are treated and fix a pesky little bug in the navigation mesh system.

In this lecture students will learn about the structure of behaviour trees and the fundamental elements that are used to construct them.

In this lecture students will write the first class for the behaviour tree system that will underwrite the majority of the functionality for the technique.

In this lecture students will learn how to write code to traverse a behaviour tree and print out the structure.

In this lecture students will work begin writing the code for leaf nodes and get their NavMeshAgent character moving.

In this lecture students will create a helpful method to assist in moving an agent around on the navmesh and reduce repetitive code.

In this lecture students will learn how to construct the logic to implement a sequence node.

In this lecture students will learn how to create a selector node and insert it into the behaviour tree.

In this lecture students will learn how to integrate game engine specifics such as navmesh commands with the behaviour tree.

In this lecture students will learn how to create a condition node to add extra logic and functionality to a sequence.

In this video Penny provides some final words about the course.

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In this lecture we will take a quick look at the nature of vectors and then discuss through a practical application how they can be used to move a character forward at a constant speed.

Follow along in the lecture as we setup a goal location for the character to move towards. You will also be shown how to change the facing direction of a character such that it looks in the direction it is travelling.

In this lecture we will examine a different approach to moving the character. This time instead of setting a vector to travel along we will turn the character towards a goal and then push the character in a forward direction.

Instead of snapping a character around to face a goal in a single update you may want more smooth turning behaviour to occur that makes the movement appear more natural. In this lecture we will examine the slerp method for achieving this.

In this lecture we will take a quick look at the difference between animation translation and translating with code. Eitherway is acceptable and it will depend on what you are trying to achieve with the look of your character as to which one is more appropriate.

In this lecture we will look at the simplest form of AI navigation: waypoints. Using the code we've developed thus far by giving the NPC an array of goals we can change its behaviour from chasing to patrolling.

If you import the Utilities from Unity's included assets you'll find code for setting up waypoints and navigating a circuit. In this lecture we will look at how to integrate these Unity asset's with our own code.

In this lecture we will examine how to create a simple car NPC that can accelerate and brake while following a circuit tracker.

Continuing where we left the previous lecture we will add in another car and set about making the properties of the movement behaviour customisable.

Inside Unity's Standard Assets you will find the setup for a Waypoint following AI car with many customisable properties. In this lecture we will explore this feature as well as discuss how to add your own custom model into the system.

Graph Theory underpins so many AI techniques and it certainly deserves some focussed attention. In this lecture we will examine the structure of graphs and several path searching algorithms will be discussed.

In this lecture we will apply the A* algorithm to finding a path through a system of waypoints converted into a graph.

In part 2 of this hands-on workshop to put together a waypoint graph traversal system using the A * algorithm we will add UI buttons to send location commands to the NPC.

This short article will explain how the code for dealing with 3D waypoints can be modified for 2D environments.

In this lecture we will modify the pathfinding exercise created thus far and introduce the concept of a NavMesh. This will reduce the amount of code we need to write as well as the time taken to setup a waypoint system manually.

In this lecture we will turn the previous waypoint system with the tank into a navmesh based project.

In this lecture we will start taking a closer look at the navmesh agent component and explore the various settings for the baked nav mesh agent that controls how the navmesh is generated.

Agents can be assigned different areas of the NavMesh through which to travel. In this lecture we will look at how to set up different areas with different costs and associate the agents with them.

The test of a NavMesh is to try it out with a real character in a real situation. It's then you'll find glitches in the default settings and automatically generated paths. Without having to remodel the environment, off-mesh links can be generated to provide smooth paths were their aren't any. In this lecture we will make a character follow a first person player character around a map while tweaking the NavMesh to handle different situations when gaps and drops appear in the NavMesh.

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In this lecture we will examine how the NavMesh agent code and be coordinated with character animation to provide smooth and believable movement of a character when pathfinding over a NavMesh.

In this short lecture I will demonstrate how to sync the animation speed with the nav mesh agent speed.

In order to include multiple navmeshes to cater for multiple different navmeshagent sizes we need to turn to some beta code that isn't currently included in Unity but available for download and inclusion. In this lecture we will walk through the setups of creating multiple navmeshes after a quick refresher on setting up humanoid animations with strafe sets.

In this video students will follow along to implement simple seek and flee methods for a Nav Mesh agent.

In this video students will learn about the differences in dynamics between the seek and pursuit behaviours and add an new method for moving an agent into the code.

In this video students will be challenged to create their own evade method which will see the agent move away from the predicted location of the target.

In this video students will examine one method of producing a wandering behaviour that will have the agent freely walk around the environment with smooth randomness.

In this video students will take a look at the basics behind the hide behaviour.  In it we will create a simple hide method that will have the agent hide behind the closest obstacle in the environment.

In this video students complete the development of the hiding behaviour and work to calculate a more accurate hiding location and reduce the number of calls to the method in the update.

In this video students will learn how to combine a number of steering behaviours with simple geometric conditions to produce complex behaviours in agents.

In this video students will be challenged to integrate the wander behaviour into the complex behaviour to have the agents go into a wander when the target cop is out of range.

There are many factors to included in a crowd simulation... or are there? Seemingly very complex movement behaviour can be generated for groups of characters with some very simple rules. This lecture will show you how.

In this series of lectures we will set up a city scene with a crowd walking along the sidewalk.

This lecture continues on from the last adjusting animation behaviours and speeds to vary from agent to agent as well as introduces the concept of a dynamic obstacle that can move around a navmesh and be avoided by the agents.

A behaviour seen in NPC movement is fleeing. This is a technique for steering a character away from danger. In this lecture we will look at the mathematics involved in the movement and apply it to the crowd simulation.

Flocking is capable of producing incredible behaviour from three simple vector calculations. In this lecture you will learn how to apply this to the development of schooling fish.

In this second part of the flocking workshop we will add extra behaviours to the fish by controlling the boundary of their swimming range.

The third part of the flocking workshop will explore ways in which the fish can avoid obstacles in their environment through raycasting and vector calculations.

In this lecture we will develop an NPC that can detect the presence of a player and act accordingly. We will implement step-by-step this algorithm which is one of the simplest AI techniques used.

Finite State Machines are a staple AI method used in games as they elegantly segregate the states, behaviours and logic used by NPCs. In part one we will begin looking at how the Unity animation system can be used to create such a machine.

In this second part of the FSM workshop we will add a base NPC class to enable sharing of properties between behaviour scripts and then add in the functionality to move from patrol to chase and back again.

In the final FSM lecture, steping you through the setup for a FSM, we will complete the example by adding in bullet shooting functionality and states onto the NPC tank.

In this short lecture I'll show you how to convert the waypoint system used to move the tank around to have the same FSM work on a navmesh.

In this lecture Behaviour Trees will be introduced. Well take a look at their structure and the theory of their execution.

This lecture begins by taking you through the setup for our behaviour tree project before starting with a very simple sequencial behaviour tree as a demo.

We continue our examination of sequences in this lecture by creating a longer set of actions and modifying different behaviour tree specific returns discover what makes sequences run and fail. Towards the end we will start creating a new behaviour tree for attacking and take a look at the wait leaf node.

In this lecture we will take a look at the while decorator node that allows you to invert the value of an action and also perform logic tests in the tree at a higher level than leaf nodes.

This lecture will introduce the selector node. This type of node can be used when any (but only one) of the children nodes need to run.

In this lecture we will complete the full set of single behaviour trees that will define all behaviours of the agent by adding the abilities to flee from danger and die.

To finish our investigation into Behaviour Trees all the smaller trees made throughout this section will be combined to inform the total behaviour of the agent droid. A final version of the project is attached as a download.

Goal-Orientated Action Planning is an AI technique that turns Finite State Machines on their head. It decouples goals from actions and provides for a very dynamic NPC behavioural system.

In this lecture we will get started with GOAP by setting up a simple scene and adding an agent.

In this lecture the GOAP agent will be given its first set of actions.

To create more complex behaviours in an agent you can add multiple plans that it can execute when the world states are right. In this lecture we will do this by extending the baker behaviour.

Even more complexity can be added when multiple inventories and agents are introduced to the system. This lecture will take you through the addition of a world inventory as well as a new agent type.

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