Which approach is grounded in representing knowledge with symbols and using logical inference?

• A. Symbolic AI
• B. Markov Chains
• C. Unsupervised Learning
• D. Probabilistic Reasoning

Answer: (A)

Representing knowledge with symbols and applying logical rules to infer new information is the hallmark of symbolic AI. In this approach, facts and relationships are encoded as symbols and predicates, and reasoning uses explicit logic—think if-then rules or first-order logic—to derive conclusions. This makes the system’s reasoning transparent and verifiable, since every result comes from known symbols and well-defined rules, such as proving that if all humans are mortal and Socrates is a human, then Socrates is mortal.

The other options align with different approaches: Markov Chains model system behavior with probabilistic state transitions rather than symbolic manipulation; Unsupervised Learning discovers patterns from unlabeled data without relying on explicit symbolic knowledge or rules; Probabilistic Reasoning handles uncertainty through probabilities and often graphical models rather than strict symbolic logic.