Adventures in Advanced Symbolic Programming Gerald Jay Sussman

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Adventures in Advanced Symbolic Programming Gerald Jay Sussman

Adventures in Advanced Symbolic Programming Officially: Large-scale Symbolic Systems Gerald Jay Sussman Spring 2021: U:6.905, G:6.945 Goal: How to make robust systems: systems that are • general: have acceptable behavior over a much larger class of situations than was anticipated by their designers. • evolvable: can be easily adapted to new jobs with only minor modification. This kind of robustness is the holy grail of engineering design. Content We study concepts and techniques for the design and implementation of large systems that can be adapted to uses not anticipated by the designer. We learn ways to • decouple goals from strategy • implement additive data-directed invocation • work with partially-specified entities • manage multiple viewpoints We consider engineering tradeoffs: flexibility vs efficiency, correctness, security 6.945 is for People who LOVE to Program! Software Design for Fexibility (Chris Hanson and Ger- ald Jay Sussman) will serve as the basic textbook for the class. Online version will be available for rental by MIT Press. Rental for registered students will be paid for by GJS. Printed (dead tree) version willl be given to each registered student (gratis GJS). Structure and Interpretation of Computer Programs (Abel- son & Sussman with Sussman) will provide some background. There will also be readings from relevant papers and articles. Substantial weekly programming as- signments and a final project are an integral part of the subject. We will use Scheme, a dialect of Lisp. All software will be free (libre) and sharable. No proprietary software will be required for this class and all software created in this class will be free software. Prerequisites: 6.034 (or comparable programming experience).

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Reflexive Interpreters 1 the Problem of Control
Reset reproduction of a Ph.D. thesis proposal submitted June 8, 1978 to the Massachusetts Institute of Technology Electrical Engineering and Computer Science Department. Reset in LaTeX by Jon Doyle, December 1995. c 1978, 1995 Jon Doyle. All rights reserved.. Current address: MIT Laboratory for Computer Science. Available via http://www.medg.lcs.mit.edu/doyle Reflexive Interpreters Jon Doyle [email protected] Massachusetts Institute of Technology, Artificial Intelligence Laboratory Cambridge, Massachusetts 02139, U.S.A. Abstract The goal of achieving powerful problem solving capabilities leads to the “advice taker” form of program and the associated problem of control. This proposal outlines an approach to this problem based on the construction of problem solvers with advanced self-knowledge and introspection capabilities. 1 The Problem of Control Self-reverence, self-knowledge, self-control, These three alone lead life to sovereign power. Alfred, Lord Tennyson, OEnone Know prudent cautious self-control is wisdom’s root. Robert Burns, A Bard’s Epitath The woman that deliberates is lost. Joseph Addison, Cato A major goal of Artificial Intelligence is to construct an “advice taker”,1 a program which can be told new knowledge and advised about how that knowledge may be useful. Many of the approaches towards this goal have proposed constructing additive formalisms for trans- mitting knowledge to the problem solver.2 In spite of considerable work along these lines, formalisms for advising problem solvers about the uses and properties of knowledge are rela- tively undeveloped. As a consequence, the nondeterminism resulting from the uncontrolled application of many independent pieces of knowledge leads to great inefficiencies.
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Reports Assigned an "AD"
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