- Unity 5.x Game AI Programming Cookbook
- Table of Contents
- Unity 5.x Game AI Programming Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Behaviors – Intelligent Movement
- Introduction
- Creating the behavior template
- Pursuing and evading
- Arriving and leaving
- Facing objects
- Wandering around
- Following a path
- Avoiding agents
- Avoiding walls
- Blending behaviors by weight
- Blending behaviors by priority
- Combining behaviors using a steering pipeline
- Shooting a projectile
- Predicting a projectile's landing spot
- Targeting a projectile
- Creating a jump system
- 2. Navigation
- Introduction
- Representing the world with grids
- Representing the world with Dirichlet domains
- 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
- Introduction
- Choosing through a decision tree
- Working a finite-state machine
- Improving FSMs: hierarchical finite-state machines
- Combining FSMs and decision trees
- Implementing behavior trees
- Working with fuzzy logic
- Representing states with numerical values: Markov system
- Making decisions with goal-oriented behaviors
- 4. Coordination and Tactics
- Introduction
- Handling formations
- Extending A* for coordination: A*mbush
- Creating good waypoints
- Analyzing waypoints by height
- Analyzing waypoints by cover and visibility
- Exemplifying waypoints for decision making
- Influence maps
- Improving influence with map flooding
- Improving influence with convolution filters
- Building a fighting circle
- 5. Agent Awareness
- 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
- 6. Board Games AI
- 7. Learning Techniques
- 8. Miscellaneous
- Index
In this chapter, you will learn a family of algorithms for developing AI for board games:
- Working with the game-tree class
- Introducing Minimax
- Negamaxing
- AB Negamaxing
- Negascouting
- Implementing a tic-tac-toe rival
- Implementing a checkers rival
In this chapter, you will learn about a family of algorithms for developing board game techniques to create artificial intelligence. They are based on the principle of a game tree (graph) that spans as we evaluate a state and decide to visit its neighbors. They also take into account board games for two rivals. But with a little bit of work, some of them can be extended to more players.
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