Given a list of agents, we take into consideration which one is closest, and, if it is close enough, we make it so that the agent tries to escape from the expected route of that first one according to its current velocity, so that they don't collide.
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Contributors
- Preface
- Behaviors - Intelligent Movement
- 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
- Navigation
- 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
- Decision Making
- 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
- The New 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
- Coordination and Tactics
- 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
- 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
- Board Games and Applied Search AI
- Learning Techniques
- Procedural Content Generation
- Miscellaneous
- Other Books You May Enjoy
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