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  • Book Overview & Buying Learning game AI programming with Lua
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Learning game AI programming with Lua

Learning game AI programming with Lua

By : David Young
4.3 (3)
Buy this Book
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Learning game AI programming with Lua

Learning game AI programming with Lua

4.3 (3)
By: David Young
Buy this Book

Overview of this book

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.
Table of Contents (11 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with AI Sandbox
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1. Getting Started with AI Sandbox
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Introduction to AI sandbox
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Summary
2
2. Creating and Moving Agents
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2. Creating and Moving Agents
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Creating a new sandbox project
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Setting up the file structure
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Extending the SandboxApplication class
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Running your sandbox for the first time
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Creating a new Decoda project
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Configuring Decoda's run executable
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Creating a sandbox Lua script
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Shooting blocks
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Creating an agent Lua script
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Agent properties
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Physics
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Knowledge
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Agents' movement
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Agent-steering forces
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Avoidance
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Avoiding blocks and agents
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Group steering
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Creating a group of followers
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Summing steering forces
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Summary
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3. Character Animations
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3. Character Animations
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Skeletons and meshes
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Attaching meshes to bones
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Animation clips
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Soldier poses
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Manipulating animations
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Animation blending
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Animation state machine (ASM)
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Building a weapon animation state machine
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Building a soldier animation state machine
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Updating animation state machines
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Playing with states
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Summary
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4. Mind Body Control
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4. Mind Body Control
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Creating a body
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Adding callbacks to the animation state machine
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Getting our soldier to shoot
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Getting our soldier to run
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Creating a brain
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Direct animation control
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A simple, finite state machine
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Indirect animation control
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Running the obstacle course
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Action latency
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Summary
5
5. Navigation
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5. Navigation
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Pathfinding
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Creating a navigation mesh
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Pathfinding on a navigation mesh
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The path information
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Adding random pathfinding to our soldier
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Creating additional navigation meshes
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Summary
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6. Decision Making
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6. Decision Making
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Creating userdata
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Agent actions
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Creating actions
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Evaluators
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Creating evaluators
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Decision structures
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Decision trees
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Building a decision tree
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Creating a decision tree agent
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Finite state machines
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Building a finite state machine
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Creating a finite state machine agent
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Strengths of finite state machines
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Pitfalls of finite state machines
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Behavior trees
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Actions
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Conditions
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Selectors
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Sequences
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Creating a behavior tree object
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Building a behavior tree
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Creating a behavior tree agent
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Strengths of behavior trees
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Pitfalls of behavior trees
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Summary
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7. Knowledge Representation
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7. Knowledge Representation
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Knowledge sources
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Blackboards
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Creating soldier knowledge sources
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Constructing a soldier blackboard
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Updating decision evaluators
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Updating behavior actions
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Summary
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8. Perception
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8. Perception
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Events
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Managing events
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Creating agent senses
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Agent visibility
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Agent sighting events
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Handling new agent sightings
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Agent auditory senses
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Auditory events
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Handling auditory events
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Decaying blackboard events
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Decaying auditory events
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Team communications
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Updating agent behaviors
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Summary
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9. Tactics
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9. Tactics
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Influence maps
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Constructing an influence map
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Drawing influence maps
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Accessing influences
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Clearing influences
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Spreading influences
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Influence map layers
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Updating the influence map
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Soldier tactics
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Scoring team influences
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Scoring dangerous areas
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Summary
10
Index
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Index

Creating evaluators

Creating evaluators relies on simple functions that perform isolated operations on the agent's userData table. Typically, most evaluators will only perform calculations based on userData instead of modifying the data itself, although there is no limitation on doing this. As the same evaluator might appear within a decision structure, care must be taken to create consistent decision choices.

Tip

Evaluators that modify userData can easily become a source of bugs; try to avoid this if at all possible. Actions should modify the agent's state, when evaluators begin to modify the agent's state as a decision structure is processed; it becomes very difficult to tell how and why any eventual action was chosen, as the order of operations will affect the outcome.

Constant evaluators

First, we'll need to create the two most basic evaluators; one that always returns true, and another that always returns false. These evaluators come in handy as a means of enabling or disabling actions...

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Tech Concepts
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Programming languages
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