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Chapter 30. Fuzzy Logic

Key Topics

Classical logic can only model crisp values: The enemy is either dead or not. Probabilities express the likelihood of a crisp variable having a specific value: This door has an 80-percent chance of being open. Fuzziness is conceptually different because it models degrees of truth: The enemy is mostly dead, and this door is nearly shut. At times, crisp AI suffers from clear-cut decisions and very robotic control. The fuzzy approach resolves the issue by providing shades of gray between black and white, allowing animats to attack with moderation (decision making) or turn left slightly (smooth control).

This form of knowledge representation is very intuitive, and comes surprisingly close to modeling human thoughts. Using a model closer to linguistic definitions can simplify the design of systems, enabling humans (not only experts) to add knowledge to the system.

Although the concept of fuzziness is easy to include in most architectures (with fuzzy senses or actions), it's only thanks to fuzzy expert systems that the true power of fuzzy logic is harnessed.

This chapter covers the following topics:

  • Fuzzy set theory as a logical foundation for the technique

  • The concept of representation with fuzziness, and conversions to/from crisp representations

  • Fuzzy logic as a way to manipulate fuzzy variables with operators and expressions

  • How fuzzy principles are integrated into expert systems, capable of decision making and control

The theory from this chapter can be applied to a wide variety of problems in game development, providing levels of realism that other crisp techniques struggle to reach. Fuzzy techniques in the next chapter help the animats climb ladders and use platforms.

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