AI in everyday products: the first widely used commercial form of Artificial Intelligence appears in appliances, automobiles, and plug-in boards. Which AI method lets machines handle vague or imprecise information similarly to human intuition?

Introduction / Context:
Many consumer and industrial devices incorporate AI techniques to make nuanced control decisions. The earliest and most pervasive commercial success relied on handling imprecision in inputs such as temperature, speed, or user preferences, mimicking how humans reason with terms like “warm,” “fast,” or “slightly.”

Given Data / Assumptions:

• Examples include microwaves, cars, and embedded control boards.
• The desired AI method must gracefully manage vague or linguistic categories.
• We focus on a technique widely deployed decades before modern deep learning.

Concept / Approach:
Fuzzy logic extends classical Boolean logic by allowing truth values in the continuous range 0 to 1. It encodes linguistic variables and expert rules like “IF speed is high AND road is slippery THEN brake gently,” with membership functions quantifying terms like “high” or “gently.” This makes it ideal for control systems where exact thresholds are unavailable or undesirable.

Step-by-Step Solution:
Identify the property: handling vagueness and linguistic categories.Match to AI method: fuzzy sets and fuzzy inference systems.Rule out crisp Boolean systems that lack graded truth values.Confirm widespread commercial adoption historically points to fuzzy logic.

Verification / Alternative check:
Fuzzy washers, cameras (autofocus), and transmission controllers historically advertised fuzzy controllers. These applications predate mass deployment of neural networks in embedded devices and showcase robust, interpretable rule-based control.

Why Other Options Are Wrong:
Boolean logic: crisp true/false; not suited for gradual categories.

Human logic: not a formal AI method.

Functional logic: unrelated paradigm, not for vagueness.

None of the above: incorrect because fuzzy logic fits exactly.

Common Pitfalls:
Confusing fuzzy logic with probabilistic reasoning; probabilities model uncertainty about facts, fuzzy logic models graded membership in concepts. Both can co-exist but address different questions.

Final Answer:
Fuzzy logic