IDA* is a variant of an algorithm called Iterative Deepening Depth-First Search. Its memory usage is lower than A* because it doesn't make use of data structures to store the looked-up and explored nodes.
Improving A* for memory – IDA*
by Jorge Palacios
Unity 2018 Artificial Intelligence Cookbook - Second Edition
- 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
Improving A* for memory – IDA*
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