Concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Means for decoupling goals from strategy. Mechanisms for implementing additive data-directed invocation. Working with partially-specified entities. Managing multiple viewpoints. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Substantial weekly programming assignments are an integral part of the subject.

There will be extensive programming assignments, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or other "functional" language.

This subject awards H-LEVEL Graduate Credit, applicable to the AI Engineering Concentration. However, the subject is appropriate for undergraduates who have the prerequisite experience. Undergraduates are welcome.

Prerequisites: 6.034, or comparable programming experience.
Time: MWF 2:00PM - 3:00PM
Room: 26-302

Class materials:

• Red Tape Memo
• Readings
• Request for Project Proposals
• Programming Assignments (a.k.a. problem sets).
• code samples.

Reference documentation:

• Structure and Interpretation of Computer Programs (SICP)
• MIT/GNU Scheme documentation