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Application of AI techniques for Computer Games BSc Computer Games Programming, 2006 Julien Delezenne GAMES ARTIFICIAL INTELLIGENCE.

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Presentation on theme: "Application of AI techniques for Computer Games BSc Computer Games Programming, 2006 Julien Delezenne GAMES ARTIFICIAL INTELLIGENCE."— Presentation transcript:

1 Application of AI techniques for Computer Games BSc Computer Games Programming, 2006 Julien Delezenne GAMES ARTIFICIAL INTELLIGENCE

2 Overview of the demo

3 In-game editor

4 Pathfinding demo

5 Design The AI component is part of the game engine Contains a generic pathfinding system Object-oriented state machines Data-driven with the reflection system

6 AI and architecture AI can be divided in two parts –The game object system –The AI techniques The object system becomes very complex in commercial games AI techniques need to be integrated in a flexible way

7 Game system

8 Reflection For each game entity: –Instantiation from its name –Exposition of its properties –(De)Serialization including with files such as XML (used in the demos) –Editable attributes in the editor. The property grid of the editor is generated automatically

9 Abstract controller Two types of controllers –Player –AI Only the input is different The game logic should only know about the abstract controller

10 AI techniques Game developers always have: –A* for pathfinding –FSM for decision-making They are the two techniques of the stealth demo in a game environnment

11 Pathfinding F.E.A.R. Environnment represented as a graph A* is independent of the environnment C&C Generals

12 Pathfinding A* is divided in three modules –Map –Goal –Storage The client application implement these objects The A* algorithm will send request to them

13 Pathfinding

14 State machine Object-oriented States are independent of each others State changes are requested In the demo, the state machine is mostly driven by the the field of view of the characters

15 State machine

16 Results Actual game demo achieved using the two most common AI tehcniques: A* and FSM in a game scenario

17 Conclusion Pros –Objectives achieved –Overall methodology –Reflection system Cons –Data-driven system not complete –Some performance issues –Need different behaviours –Need an event-system

18 Future enhancements Scripting More behaviours Agents’ memory Dynamic pathfinding Navigational meshes More advanced Toolsets …


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