Foundations of AI: A classic definition emphasizes problem-solving methods that operate on which kind of internal representations?

Introduction / Context:
One influential view of artificial intelligence, sometimes called the Physical Symbol System Hypothesis, frames intelligence in terms of manipulating formal symbols according to rules. Understanding this framing clarifies how early AI systems represented knowledge and performed search, planning, and reasoning.

Given Data / Assumptions:

• We are selecting the type of internal representation emphasized by classic AI.
• The focus is on problem-solving methods (search, inference).
• We are not discussing sensory modalities directly (smell, touch).

Concept / Approach:
Symbolic AI represents entities, relations, and operations as symbols and structures (lists, trees, graphs) and applies algorithms to transform these symbols. While algorithms are the procedures, the key representational substrate is symbols. Thus the question’s emphasis is on representational form rather than the procedures per se.

Step-by-Step Solution:

Verification / Alternative check:
Historical AI textbooks discuss rule-based systems, theorem proving, and search operating over symbolic structures, confirming this emphasis.

Why Other Options Are Wrong:

• Smell and touch: sensory modalities, not representational substrates.
• Algorithms: methods that operate on representations; the question asks about the objects being processed.
• None of the above: incorrect because symbols fit exactly.

Common Pitfalls:
Confusing the role of algorithms with the nature of representations; overlooking that modern AI also includes subsymbolic representations, but the classic definition names symbols explicitly.

Final Answer:
symbols