Early AI systems: which program, developed by Daniel G. Bobrow, read and solved algebra word problems by interpreting natural-language statements into equations?

Introduction / Context:
Classic AI included landmark systems that demonstrated specific capabilities. One early achievement was parsing plain-language algebra problems and computing solutions—bridging natural language understanding and symbolic mathematics. Daniel G. Bobrow’s system is often cited in AI histories for this feat.

Given Data / Assumptions:

• The system read algebra word problems.
• It translated natural language into formal equations and solved them.
• We compare well-known AI names from the same era.

Concept / Approach:
Bobrow’s program named STUDENT interpreted textual algebra problems into simultaneous equations and solved them, demonstrating early semantic parsing. SHRDLU (Winograd) worked in the Blocks World for dialog and manipulation. BACON (Langley) explored scientific discovery heuristics. SIMD is a hardware parallelism acronym, not an AI program title.

Step-by-Step Solution:

Verification / Alternative check:
AI textbooks and retrospectives consistently attribute algebra word-problem parsing to Bobrow’s STUDENT, validating the identification.

Why Other Options Are Wrong:

• SHRDLU: natural-language dialog in a microworld, not algebra text problems.
• BACON: scientific discovery heuristics, not algebra word problems.
• SIMD: Single Instruction, Multiple Data—an architecture class, not the system in question.

Common Pitfalls:
Assuming any famous early NLP system solved algebra word problems; SHRDLU is often misattributed.

Final Answer:
STUDENT