During this period, research on AI knowledge representation centers on turning tacit human understanding into explicit, machine-readable form. Symbolic frameworks, rule-based reasoning, and structured knowledge bases became the standard approach to enable planning, explanation, and learning. Researchers emphasized principled representations that can be reasoned about, explained, and updated, signaling a shift from ad hoc problem solving to systematic knowledge engineering.