AI technique recognition: Which method enables a computer system to understand associations and relationships among objects and events by comparing patterns?

Introduction / Context:
Many AI systems must determine whether new inputs resemble known templates or previously seen examples. The question probes the term for recognizing associations by evaluating similarity structures in data.

Given Data / Assumptions:

• Inputs can be symbols, strings, images, or event sequences.
• The goal is to detect correspondence or similarity to known patterns.
• Associations arise from matching features, tokens, or structures.

Concept / Approach:
Pattern matching refers to techniques that check whether and how an input conforms to a defined pattern. In AI, it underpins rule firing (matching conditions), natural language parsing, vision feature detection, and event correlation. While heuristics guide search, and cognitive science studies mind mechanisms, pattern matching is the direct method for associating inputs with stored representations.

Step-by-Step Solution:

Verification / Alternative check:
Expert systems match facts to rules; NLP engines match tokens to grammars; vision pipelines match features to models—all are pattern matching instances.

Why Other Options Are Wrong:

• Heuristic processing: search/control strategies, not association per se.
• Cognitive science: academic discipline, not a matching method.
• Relative symbolism: not a standard technical term.

Common Pitfalls:
Confusing high-level disciplines or strategies with the concrete operation of matching inputs to patterns.

Final Answer:
Pattern matching