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Home > Data > Artificial Intelligence > Learning game AI programming with Lua
Learning game AI programming with Lua
Learning game AI programming with Lua

Learning game AI programming with Lua: Leverage the power of Lua programming to create game AI that focuses on motion, animation, and tactics

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Arrow left icon
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Paperback Nov 2014 352 pages 1st Edition
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Learning game AI programming with Lua

Chapter 2. Creating and Moving Agents

In this chapter, we will cover the following topics:

  • Setting up your first sandbox executable
  • Creating your first sandbox Lua scripts
  • Understanding agent properties and their effects
  • Basic Newtonian motion
  • Agent-steering forces
  • Creating seeking, pursuing, path following, and grouped agents

Now that we understand how AI sandbox is set up and some of the underlying system structure, we're going to dive head-first into creating a brand new sandbox demo. Throughout the rest of the book, the new Lua sandbox API will be introduced piece by piece, extending this demo with additional AI functionality, animation, graphics, and game play.

While the sandbox is doing a lot of heavy lifting in terms of graphics and physics, the core AI logic will be completely implemented in Lua, backed by data structures that are managed by the C++ code.

Creating a new sandbox project

First, to create a new sandbox executable, we need to declare a new Visual Studio demo project within the Premake build scripts. You can add a new sandbox project by opening the SandboxDemos.lua script and appending a new entry to the SandboxDemos table. In this case, you can name your my_sandbox demo or any other name you'd like. The project name will determine the name of the executable that is built:

SandboxDemos.lua:

SandboxDemos = {
    "chapter_1_introduction",
    ...
    "my_sandbox"
};

Note

All the heavy lifting of configuring a sandbox demo actually takes place in the premake.lua file by the CreateDemoProject function. The premake.lua script simply loops over all entries within the SandboxDemos table and creates the corresponding projects, setting up the source files, project dependencies, library includes, and so on.

Setting up the file structure

The next step is to set up the actual file structure for the C++ source files, C++ header files, and Lua script files for the demo. Create the corresponding directory structure based on the entry you added to the SandboxDemos table. Premake will automatically search for any .h, .cpp, and .lua files that reside within these folders and any subfolders, automatically adding them to the Visual Studio project during the solution regeneration:

src/my_sandbox/include
src/my_sandbox/src
src/my_sandbox/script

Extending the SandboxApplication class

Once your project has been set up, you need to create three blank files for Premake to discover.

Create the source and header files as follows:

src/my_sandbox/include/MySandbox.h
src/my_sandbox/src/MySandbox.cpp
src/my_sandbox/src/main.cpp

Now, it's time to regenerate the Visual Studio solution by executing vs2008.bat, vs2010.bat, vs2012.bat, or vs2013.bat. When you open the Visual Studio solution, you'll see your brand new My_Sandbox project!

Each of the sandbox demos is set up to extend the base SandboxApplication class and declare where to find the corresponding Lua script files for the executable.

Declaring your MySandbox class follows the same pattern and looks as follows:

MySandbox.h:

#include "demo_framework/include/SandboxApplication.h"

class MySandbox : public SandboxApplication {
public:
    MySandbox(void);

    virtual ~MySandbox(void);

    virtual void Initialize();
};

Inheriting from SandboxApplication gives you a base...

Running your sandbox for the first time

Now, compile your solution and run your sandbox. As no meshes, lights, and so on have been added to the sandbox yet, all you should see is a black screen. While this might seem small, a lot has happened already, and your SandboxApplication class is properly set up to let Lua take over.

Creating a new Decoda project

Once Visual Studio is out of the way, it's time to create a Decoda project. Open Decoda and create a blank new project. Save the project to the decoda folder, which will create the .deproj and .deuser files. Whenever we need to create a new Lua script file, we will create the file within Decoda and save the .lua file to the src/my_sandbox/script folder:

decoda/my_sandbox.deproj
decoda/my_sandbox.deuser

Creating a new sandbox project

First, to create a new sandbox executable, we need to declare a new Visual Studio demo project within the Premake build scripts. You can add a new sandbox project by opening the SandboxDemos.lua script and appending a new entry to the SandboxDemos table. In this case, you can name your my_sandbox demo or any other name you'd like. The project name will determine the name of the executable that is built:

SandboxDemos.lua:

SandboxDemos = {
    "chapter_1_introduction",
    ...
    "my_sandbox"
};

Note

All the heavy lifting of configuring a sandbox demo actually takes place in the premake.lua file by the CreateDemoProject function. The premake.lua script simply loops over all entries within the SandboxDemos table and creates the corresponding projects, setting up the source files, project dependencies, library includes, and so on.

Setting up the file structure

The next step is to set up the actual file structure for the C++ source files, C++ header files, and Lua script files for the demo. Create the corresponding directory structure based on the entry you added to the SandboxDemos table. Premake will automatically search for any .h, .cpp, and .lua files that reside within these folders and any subfolders, automatically adding them to the Visual Studio project during the solution regeneration:

src/my_sandbox/include
src/my_sandbox/src
src/my_sandbox/script

Extending the SandboxApplication class

Once your project has been set up, you need to create three blank files for Premake to discover.

Create the source and header files as follows:

src/my_sandbox/include/MySandbox.h
src/my_sandbox/src/MySandbox.cpp
src/my_sandbox/src/main.cpp

Now, it's time to regenerate the Visual Studio solution by executing vs2008.bat, vs2010.bat, vs2012.bat, or vs2013.bat. When you open the Visual Studio solution, you'll see your brand new My_Sandbox project!

Each of the sandbox demos is set up to extend the base SandboxApplication class and declare where to find the corresponding Lua script files for the executable.

Declaring your MySandbox class follows the same pattern and looks as follows:

MySandbox.h:

#include "demo_framework/include/SandboxApplication.h"

class MySandbox : public SandboxApplication {
public:
    MySandbox(void);

    virtual ~MySandbox(void);

    virtual void Initialize();
};

Inheriting from SandboxApplication gives you a base to start with. For now...

Running your sandbox for the first time

Now, compile your solution and run your sandbox. As no meshes, lights, and so on have been added to the sandbox yet, all you should see is a black screen. While this might seem small, a lot has happened already, and your SandboxApplication class is properly set up to let Lua take over.

Creating a new Decoda project

Once Visual Studio is out of the way, it's time to create a Decoda project. Open Decoda and create a blank new project. Save the project to the decoda folder, which will create the .deproj and .deuser files. Whenever we need to create a new Lua script file, we will create the file within Decoda and save the .lua file to the src/my_sandbox/script folder:

decoda/my_sandbox.deproj
decoda/my_sandbox.deuser

Configuring Decoda's run executable

In order for Decoda to execute your sandbox, we need to configure the Decoda project with the following settings. You can access the project settings by navigating to Project | Settings. Typically, we'll have Decoda run the Release version of the sandbox executable for performance. Unless you need to debug the C++ sandbox code and Lua scripts at the same time, it's advisable to run the Release version of your executable.

The configuration of new Decoda project debug settings

Note

Note the relative path for the release executable. The path that Decoda will execute is based on the location of the .deproj file.

Remember that you have to build the Visual Studio solution before trying to debug with Decoda.

Creating a sandbox Lua script

With a basic sandbox application out of the way, we're going to create the basic Lua script that sets up the sandbox. First, create a new Sandbox.lua script within the script folder:

Create the Lua file as follows:

src/my_sandbox/script/Sandbox.lua

A sandbox Lua script must implement four global functions that the C++ code will call, and they are Sandbox_Cleanup, Sandbox_HandleEvent, Sandbox_Initialize, and Sandbox_Update:

Sandbox.lua:

function Sandbox_Cleanup(sandbox)
end

function Sandbox_HandleEvent(sandbox, event)
end

function Sandbox_Initialize(sandbox)
end

function Sandbox_Update(sandbox, deltaTimeInMillis)
end

With the basic hooks in place, modify your SandboxApplication class to create a sandbox based on your Lua script:

MySandbox.cpp:

void MySandbox::Initialize() {
    SandboxApplication::Initialize();

    ...
    CreateSandbox("Sandbox.lua");
}

Tip

Don't forget to recompile your sandbox application whenever a change is made to any of the C++ files.

Creating...

Shooting blocks

Now that we have some basic lighting, a physics plane, and the ability to create and simulate physics objects, it's time to start shooting things. Before we jump head-first into creating agents, we'll take a quick detour into accessing some of the physics aspects of sandbox objects, as well as interacting with input controls.

The Sandbox_HandleEvent function allows the sandbox to respond to mouse and keyboard inputs. The event parameter is a Lua table that stores the source of the event, whether the event was generated by a down or up key press, and what key caused the event. Mouse-movement events are similar, but contain the width and height location of the mouse cursor.

As we already know how to create a sandbox object, all we need to do to shoot objects is position the object at the camera's position and orientation and apply a physics impulse on the object.

In this case, we're going to create and shoot a block on a space_key press event. The camera's position and forward...

Creating an agent Lua script

To start creating an agent, we need to create another Lua script that implements the Agent_Cleanup, Agent_HandleEvent, Agent_Initialize, and Agent_Update functions:

Create the Lua file as follows:

src/my_sandbox/script/Agent.lua

Agent.lua:

function Agent_Cleanup(agent)
end

function Agent_HandleEvent(agent, event)
end

function Agent_Initialize(agent)
end

function Agent_Update(agent, deltaTimeInMillis)
end

Now that we have a basic agent script, we can create an instance of the agent within the sandbox. Modify the initialization of the sandbox in order to create your AI agent with the Sandbox.CreateAgent function.

Tip

Remember that each AI agent runs within its own Lua virtual machine (VM). Even though a separate VM is running the agent logic, you can still access and modify properties of an agent from the sandbox Lua script, as the C++ code manages agent data.

Modify the initialization of the sandbox in order to create your AI agent with the Sandbox.CreateAgent function...

Agent properties

Now that we can create agents, we're going to take a step back and look at what properties are available to an agent and what they mean.

Orientation

Whenever you need to return the orientation of an agent, it's easiest to use the forward vector that usually represents the direction of movement of an agent. Both the left and up vectors of orientation are available as well. Whenever you need to change an agent's direction, simply set its forward vector.

The forward axis

To access and set the forward vector of our agents, we can use the built-in GetForward and SetForward helper functions.

local forwardVector = agent:GetForward();
Agent.SetForward(agent, forwardVector);

The left axis

We can also access the left orientation vector using the GetLeft helper function.

local leftVector = agent:GetLeft();

The up axis

Accessing the up orientation vector is similarly provided by a GetUp helper function.

local upVector = agent:GetUp();

Location

An agent's position is the center of the mass of its...

Physics

Even though agents are simulated with physics, not all of the agent's physics parameters are enforced at the physics simulation level. The mass of an agent, for example, is the identical mass used within the physics simulation itself, while the MaxForce and MaxSpeed functions of an agent are only enforced by the agent. These two properties represent the maximum amount of force an agent can exert on itself and the max speed the agent can reach without any outside influences.

An intuitive example of why this separation is desirable when dealing with agent physics is gravity. When an agent accelerates to its max speed, we still want gravity to accelerate the agents downward in the case of falls. This acceleration can force agents to have a speed larger than their max speed property.

Mass

To access and modify the mass of our agents we can use the agent's GetMass and SetMass helper functions.

local mass = agent:GetMass();
agent:SetMass(mass);

The max force

The maximum force of our agents can...

Knowledge

Agents themselves have a very basic set of knowledge so that external Lua scripts such as the sandbox script can direct agents with some amount of persistence. For example, when we create an agent that moves to a target position, we might want the sandbox to set this position instead of the agent having to determine its target.

Target

An agent's target is a vector position. Typically, agents will use the target as a position they want to reach or the known position of another agent.

local targetVector = agent:GetTarget();
agent:SetTarget(targetVector);

Target radius

A target radius is a number value that agents use to determine whether they are close enough to their target without having to be exactly at the target position. This fudge factor helps agents avoid circling a target position due to small numerical differences in their position and target position.

local targetRadius = agent:GetTargetRadius();
agent:SetTargetRadius(targetRadius );

Path

An agent's path is a series of vector...

Agents' movement

As all agents within the sandbox are automatically simulated through the physics system, it's time to get acquainted with some basic Newtonian physics.

Mass

The mass of an agent comes into play when colliding with other objects and is based on how much the agent should accelerate based on the forces applied to the agent. All mass calculations within the sandbox occur in kilograms.

Speed

Speed defines how fast an agent is moving without considering the direction the agent is moving in. All speed values within the sandbox will be measured in meters per second and signify the magnitude of the velocity vector.

Velocity

Velocity, on the other hand, is both the speed and direction the agent is moving in. It is measured in meters per second and is represented as a vector.

Acceleration

Acceleration within the sandbox is always measured in meters per second squared and represents the change in the velocity of an agent.

Force

Force plays a large part when moving agents around and is measured...

Agent-steering forces

With some basic agent properties and a full-fledged physics system supporting the sandbox, we can begin moving agents realistically through forces. This type of movement system is best known as a steering-based locomotion system. Craig Reynolds' Steering Behaviors For Autonomous Characters (http://www.red3d.com/cwr/papers/1999/gdc99steer.html) is best known for describing this style of steering system for moving characters. Steering forces allow for an easy classification of different movement types and allow for an easy way to apply multiple forces to a character.

As the sandbox uses the OpenSteer library to steer calculations, this makes it painless for Lua to request steering forces. While the steering calculations are left to OpenSteer, the application of forces will reside within our Lua Scripts.

Seeking

Seeking is one of the core steering forces and calculates a force that moves the agent toward their target. OpenSteer combines both seeking- and arrival-steering...

Avoidance

Avoidance steering behavior involves avoiding collisions between moving agents, objects, and other moving agents. The collision avoidance calculated from ForceToAvoidAgents creates a steering force in the tangent direction of a potential agent as two agents move closer to one another. Predictive movements are used to determine whether two agents will collide within a given amount of time.

Obstacle avoidance, on the other hand, approximates sandbox objects using spheres and uses the agent's predictive movement to create a steering force tangent for the potential collision.

Collision avoidance

To calculate the force to avoid other agents, based on the minimum time, to collide with other agents, we can use the ForceToAvoidAgents function.

local avoidAgentForce = 
    agent:ForceToAvoidAgents(minTimeToCollision);

Obstacle avoidance

A similar force to avoid other dynamic moving obstacles can be calculated using the ForceToAvoidObjects function.

local avoidObjectForce =     
    agent:ForceToAvoidObjects...
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Description

If you are a game developer or a general programmer who wishes to focus on programming systems and techniques to build your game AI without creating low-level interfaces in a game engine, then this book is for you. Knowledge of C++ will come in handy to debug the entirety of the AI sandbox and expand on the features present within the book, but it is not required.

What you will learn

  • Create an animation state machine to drive AI animations within Lua
  • Build and find paths on navigation meshes
  • Write and debug Lua scripts within a fullscale Lua IDE
  • Develop decision logic with behavior trees, state machines, and decision trees to build modular, reusable AI
  • Manage short and longterm knowledge representation with blackboard data structures
  • Add sensory perception to give AIs the ability to see and hear
  • Develop highlevel tactics with multiple AIs based on influence maps
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 28, 2014
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781783281336
Category :
Data
Languages :
Lua
Concepts :
Artificial Intelligence

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ISBN-13 : 9781783281336
Category :
Data
Languages :
Lua
Concepts :
Artificial Intelligence

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Table of Contents

10 Chapters
1. Getting Started with AI Sandbox Chevron down icon Chevron up icon
1. Getting Started with AI Sandbox
Introduction to AI sandbox
Summary
2. Creating and Moving Agents Chevron down icon Chevron up icon
2. Creating and Moving Agents
Creating a new sandbox project
Setting up the file structure
Extending the SandboxApplication class
Running your sandbox for the first time
Creating a new Decoda project
Configuring Decoda's run executable
Creating a sandbox Lua script
Shooting blocks
Creating an agent Lua script
Agent properties
Physics
Knowledge
Agents' movement
Agent-steering forces
Avoidance
Avoiding blocks and agents
Group steering
Creating a group of followers
Summing steering forces
Summary
3. Character Animations Chevron down icon Chevron up icon
3. Character Animations
Skeletons and meshes
Attaching meshes to bones
Animation clips
Soldier poses
Manipulating animations
Animation blending
Animation state machine (ASM)
Building a weapon animation state machine
Building a soldier animation state machine
Updating animation state machines
Playing with states
Summary
4. Mind Body Control Chevron down icon Chevron up icon
4. Mind Body Control
Creating a body
Adding callbacks to the animation state machine
Getting our soldier to shoot
Getting our soldier to run
Creating a brain
Direct animation control
A simple, finite state machine
Indirect animation control
Running the obstacle course
Action latency
Summary
5. Navigation Chevron down icon Chevron up icon
5. Navigation
Pathfinding
Creating a navigation mesh
Pathfinding on a navigation mesh
The path information
Adding random pathfinding to our soldier
Creating additional navigation meshes
Summary
6. Decision Making Chevron down icon Chevron up icon
6. Decision Making
Creating userdata
Agent actions
Creating actions
Evaluators
Creating evaluators
Decision structures
Decision trees
Building a decision tree
Creating a decision tree agent
Finite state machines
Building a finite state machine
Creating a finite state machine agent
Strengths of finite state machines
Pitfalls of finite state machines
Behavior trees
Actions
Conditions
Selectors
Sequences
Creating a behavior tree object
Building a behavior tree
Creating a behavior tree agent
Strengths of behavior trees
Pitfalls of behavior trees
Summary
7. Knowledge Representation Chevron down icon Chevron up icon
7. Knowledge Representation
Knowledge sources
Blackboards
Creating soldier knowledge sources
Constructing a soldier blackboard
Updating decision evaluators
Updating behavior actions
Summary
8. Perception Chevron down icon Chevron up icon
8. Perception
Events
Managing events
Creating agent senses
Agent visibility
Agent sighting events
Handling new agent sightings
Agent auditory senses
Auditory events
Handling auditory events
Decaying blackboard events
Decaying auditory events
Team communications
Updating agent behaviors
Summary
9. Tactics Chevron down icon Chevron up icon
9. Tactics
Influence maps
Constructing an influence map
Drawing influence maps
Accessing influences
Clearing influences
Spreading influences
Influence map layers
Updating the influence map
Soldier tactics
Scoring team influences
Scoring dangerous areas
Summary
Index Chevron down icon Chevron up icon
Index

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Hands-On Reinforcement Learning with Python
Hands-On Reinforcement Learning with Python
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Jun 2018 318 pages
Full star icon 2.6 (18)
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€8.99 €23.99
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LLM Engineer's Handbook
LLM Engineer's Handbook
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Oct 2024 522 pages
Full star icon 4.8 (17)
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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(3 Ratings)
5 star 33.3%
4 star 66.7%
3 star 0%
2 star 0%
1 star 0%
Jimmy franco Dec 10, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is amazing , I recomend that you buy it and buy as a gift for all ur friends it's so informational and I must say I enjoyed reading it .
Amazon Verified review Amazon
CPallini Dec 24, 2014
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book uses Lua scripting for implementing AI on a 3D game.Lua executes in a C++ framework (the sandbox) on the top of open source libraries like Ogre3D.Pros: - Lua scripting in a C++ framework is a very happy choice. - Focus is on practical AI. - The sandbox C++ code looks solid and is full accessible. - The companion code works out of the box.Cons: - In my opinion, the code/text ratio in the book is too high, more explanations would have been welcomed. To sum up, this is a good book, backed up by solid code (and good open source libraries) and you can actually script AI in a 3D game. Please note, in order to enjoy the book, you have to download the companion source code.
Amazon Verified review Amazon
Mr. Gibbins Jul 22, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is informative but hard to get you're head around unless you run lua on in c++ environment, if like me you don't have access to the c++ environment because the game engine doesn't allow you access then it's quite hard to figure out how to use the I formation provided with the system they use.
Amazon Verified review Amazon

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About the author

Profile icon David Young
David Young
David Young's first experience in documenting the open source world was the official 2003 manual for JAJC, a popular jabber client, which he authored in XML using Docbook. He followed this with the development of "Bandersnatch", the whimsically named Jabber message logger, which remains the de facto open source platform for recording and archiving messages on a Jabber server. After founding and successfully running an open source consulting company ("Funky Penguin") for several years, David "retired" to full-time employment. David now works for a respected New Zealand IT consulting firm (Prophecy Networks Ltd) and pursues the odd "Funky Penguin" project in his spare time. David's projects, notes, and ramblings can be found at: http://www.funkypenguin.co.nz.
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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  2. American Samoa
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