Unity 2018 Artificial Intelligence Cookbook Second Edition (Jorge Palacios 著)

https://github.com/PacktPublishing/Unity-2018-Artificial-Intelligence-Cookbook-Second-Edition

1 Behaviors - Intelligent Movement

Behaviors – Intelligent Movement, explores some of the most interesting movement algorithms based on the steering behavior principles developed by Craig Reynolds and work from Ian Millington.

They act as a foundation for most of the AI used in advanced games and other algorithms that rely on movement, such as the family of path-finding algorithms.

2 Navigation

Navigation, covers path-finding algorithms for navigating complex scenarios.

It will include some ways of representing the world using different kinds of graph structure, and several algorithms for finding a path, each aimed to different situations.

3 Decision Making

Decision Making, explains different decision-making techniques that are flexible enough to adapt to different types of games, and robust enough to let us build modular decision-making systems.

4 The New NavMesh API

The New NavMesh API, shows the inner workings of the NavMesh API introduced in Unity 5.6, and explains how it enables us to grasp the power of the NavMesh engine and tune it in real time.

5 Coordination and Tactics

Coordination and Tactics, deals with a number of different recipes for coordinating different agents as a whole organism, such as formations and techniques that allow us make tactical decisions based on graphs, such as waypoints and influence maps.

6 Agent Awareness

Agent Awareness, explores different ways to simulate sense stimuli on an agent.

We will learn how to use tools we already know to create these simulations: colliders and graphs.

7 Board Games and Applied Search AI

Board Games and Applied Search AI, covers a family of algorithms for developing board games, as well as turn-based-game techniques for creating AI.

8 Learning Techniques

Learning Techniques, explores the field of machine learning. It will give us a great head-start in our endeavor to learn and apply machine learning techniques into our games.

9 Procedural Content Generation

Procedural Content Generation, explores different techniques for enabling replayability in our games by creating content procedurally.

It will give us some pointers in the right direction for different types of content.

10 Miscellaneous

Miscellaneous, introduces new techniques and uses algorithms that we will have learned in previous chapters to create new behaviors that don't quite fit in a definite category.

1 Behaviors - Intelligent Movement

In this chapter, we will develop AI algorithms for movement by covering the following recipes:

• Creating the behaviors template
• Pursuing and evading
• Adjusting the agent for physics
• Arriving and leaving
• Facing objects
• Wandering around
• Following a path
• Avoiding agents
• Avoiding walls
• Blending behaviors by weight
• Blending behaviors by priority
• Shooting a projectile
• Predicting a projectile's landing spot
• Targeting a projectile
• Creating a jump system

　　Introduction

　　Creating the behaviors template

　　Pursuing and evading

　　Adjusting the agent for physics

　　Arriving and leaving

　　Facing objects

　　Wandering around

　　Following a path

　　Avoiding agents

　　Avoiding walls

　　Blending behaviors by weight

　　Blending behaviors by priority

　　Shooting a projectile

　　Predicting a projectile's landing spot

　　Targeting a projectile

　　Creating a jump system

2 Navigation

In this chapter, we will cover the following recipes:

• Representing the world with grids
• Representing the world with points of visibility
• Representing the world with a self-made navigation mesh
• Finding your way out of a maze with DFS
• Finding the shortest path in a grid with BFS
• Finding the shortest path with Dijkstra
• Finding the best-promising path with A*
• Improving A* for memory: IDA*
• Planning navigation in several frames: time-sliced search
• Smoothing a path

　　Introduction

　　Representing the world with grids

　　Representing the world with points of visibility

　　Representing the world with a self-made navigation mesh

　　Finding your way out of a maze with DFS

　　Finding the shortest path in a grid with BFS

　　Finding the shortest path with Dijkstra

　　Finding the best-promising path with A*

　　Improving A* for memory – IDA*

　　Planning navigation in several frames – time-sliced search

　　Smoothing a path

3 Decision Making

In this chapter, we will cover the following recipes:

• Choosing through a decision tree
• Implementing a finite-state machine
• Improving FSMs: hierarchical finite-state machines
• Implementing behavior trees
• Working with fuzzy logic
• Making decisions with goal-oriented behaviors
• Implementing a blackboard architecture
• Experimenting with Unity's animation state machine

　　Introduction

　　Choosing through a decision tree

　　Implementing a finite-state machine

　　Improving FSMs: hierarchical finite-state machines

　　Implementing behavior trees

　　Working with fuzzy logic

　　Making decisions with goal-oriented behaviors

　　Implementing a blackboard architecture

　　Experimenting with Unity's animation state machine

4 The New NavMesh API

In this chapter, we will learn how to make use of the new NavMesh API,through the following recipes:

• Setting up the NavMesh building components
• Creating and managing NavMesh for multiple types of agents
• Creating and updating NavMesh data at runtime
• Controlling the lifetime of the NavMesh instance
• Connecting multiple instances of NavMesh
• Creating dynamic NavMeshes with obstacles
• Implementing some behaviors using the NavMesh API

　　Introduction

　　Setting up the NavMesh building components

　　Creating and managing NavMesh for multiple types of agents

　　Creating and updating NavMesh data at runtime

　　Controlling the lifetime of the NavMesh instance

　　Connecting multiple instances of NavMesh

　　Creating dynamic NavMeshes with obstacles

　　Implementing some behaviors using the NavMesh API

5 Coordination and Tactics

In this chapter, we will learn techniques for coordination and devising tactics:

• Handling formations
• Extending A* for coordination: A*mbush
• Introducing waypoints by making a manual selector
• Analyzing waypoints by height
• Analyzing waypoints by cover and visibility
• Creating waypoints automatically
• Exemplifying waypoints for decision making
• Implementing influence maps
• Improving influence with map flooding
• Improving influence with convolution filters
• Building a fighting circle

　　Introduction

　　Handling formations

　　Extending A* for coordination – A*mbush

　　Analyzing waypoints by height

　　Analyzing waypoints by cover and visibility

　　Creating waypoints automatically

　　Exemplifying waypoints for decision making

　　Implementing influence maps

　　Improving influence with map flooding

　　Improving influence with convolution filters

　　Building a fighting circle

6 Agent Awareness

In this chapter, we will learn some algorithm recipes for simulating senses and agent awareness:

• The seeing function using a collider-based system
• The hearing function using a collider-based system
• The smelling function using a collider-based system
• The seeing function using a graph-based system
• The hearing function using a graph-based system
• The smelling function using a graph-based system
• Creating awareness in a stealth game

　　Introduction

　　The seeing function using a collider-based system

　　The hearing function using a collider-based system

　　The smelling function using a collider-based system

　　The seeing function using a graph-based system

　　The hearing function using a graph-based system

　　The smelling function using a graph-based system

　　Creating awareness in a stealth game

7 Board Games and Applied Search AI

In this chapter, you will learn a family of algorithms for developing board game AI:

• Working with the game-tree class
• Implementing Minimax
• Implementing Negamax
• Implementing AB Negamax
• Implementing NegaScout
• Implementing a Tic-Tac-Toe rival
• Implementing a Checkers rival
• Implementing Rock-Paper-Scissors AI with UCB1
• Implementing Regret Matching

　　Introduction

　　Working with the game-tree class

　　Implementing Minimax

　　Implementing Negamax

　　Implementing AB Negamax

　　Implementing NegaScout

　　Implementing a Tic-Tac-Toe rival

　　Implementing a Checkers rival

　　Implementing Rock-Paper-Scissors AI with UCB1

　　Implementing regret matching

8 Learning Techniques

In this chapter, we will explore the world of machine learning through the following topics:

• Predicting actions with an N-Gram predictor
• Improving the predictor – Hierarchical N-Gram
• Learning to use a Naïve Bayes classifier
• Implementing reinforcement learning
• Implementing artificial neural networks

　　Introduction

　　Predicting actions with an N-Gram predictor

　　Improving the predictor – Hierarchical N-Gram

　　Learning to use Naïve Bayes classifier

　　Implementing reinforcement learning

　　Implementing artificial neural networks

9 Procedural Content Generation

In this chapter, we will learn different techniques for procedural content generation with the following recipes:

• Creating mazes with Depth-First Search
• Implementing the constructive algorithm for dungeons and islands
• Generating landscapes
• Using N-Grams for content generation
• Generating enemies with the evolutionary algorithm

　　Introduction

　　Creating mazes with Depth-First Search

　　Implementing the constructive algorithm for dungeons and islands

　　Generating landscapes

　　Using N-Grams for content generation

　　Generating enemies with the evolutionary algorithm

10 Miscellaneous

In this chapter, you will learn different techniques for:

• Creating and managing Scriptable Objects
• Handling random numbers better
• Building an air-hockey rival
• Implementing an architecture for racing games
• Managing race difficulty using a rubber-band system

　　Introduction

　　Creating and managing Scriptable Objects

　　Handling random numbers better

　　Building an air-hockey rival

　　Implementing an architecture for racing games

　　Managing race difficulty using a rubber-band system