bobgian@psuvax.UUCP (01/01/84)
CMPSC 481: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
An introduction to the theory, research paradigms, implementation techniques,
and philosopies of Artificial Intelligence considered both as a science of
natural intelligence and as the engineering of mechanical intelligence.
OBJECTIVES -- To provide:
1. An understanding of the principles of Artificial Intelligence;
2. An appreciation for the power and complexity of Natural Intelligence;
3. A viewpoint on programming different from and complementary to the
viewpoints engendered by other languages in common use;
4. The motivation and tools for developing good programming style;
5. An appreciation for the power of abstraction at all levels of program
design, especially via embedded compilers and interpreters;
6. A sense of the excitement at the forefront of AI research; and
7. An appreciation for the tremendous impact the field has had and will
continue to have on our perception of our place in the Universe.
TOPIC SUMMARY:
INTRODUCTION: What is "Intelligence"?
Computer modeling of "intelligent" human performance. The Turing Test.
Brief history of AI. Relation of AI to psychology, computer science,
management, engineering, mathematics.
PRELUDE AND FUGUE ON THE "SECRET OF INTELLIGENCE":
"What is a Brain that it may possess Intelligence, and Intelligence that
it may inhabit a Brain?" Introduction to Formal Systems, Physical Symbol
Systems, and Multilevel Interpreters. Necessity and Sufficiency of
Physical Symbol Systems as the basis for intelligence.
REPRESENTATION OF PROBLEMS, GOALS, ACTIONS, AND KNOWLEDGE:
State Space, Predicate Calculus, Production Systems, Procedural
Representations, Semantic Networks, Frames and Scripts.
THE "PROBLEM-SOLVING" PARADIGM AND TECHNIQUES:
Generate and Test, Heuristic Search (Search WITH Heuristics,
Search FOR Heuristics), Game Trees, Minimax, Problem Decomposition,
Means-Ends Analysis, The General Problem Solver (GPS).
LISP PROGRAMMING:
Symbolic Expressions and Symbol Manipulation, Data Structures,
Evaluation and Quotation, Predicates, Input/Output, Recursion.
Declarative and Procedural knowledge representation in LISP.
LISP DETAILS:
Storage Mapping, the Free List, and Garbage Collection,
Binding strategies and the concept of the "Environment", Data-Driven
Programming, Message-Passing, The MIT Lisp Machine "Flavor" system.
LISP AS THE "SYSTEMS SUBSTRATE" FOR HIGHER LEVEL ABSTRACTIONS:
Frames and other Knowledge Representation Languages, Discrimination
Nets, "Higher" High-Level Languages: PLANNER, CONNIVER, PROLOG.
LOGIC, RULE-BASED SYSTEMS, AND INFERENCE:
Logic: Axioms, Rules of Inference, Theorems, Truth, Provability.
Production Systems: Rule Interpreters, Forward/Backward Chaining.
Expert Systems: Applied Knowledge Representation and Inference.
Data Dependencies, Non-Monotonic Logic, and Truth-Maintenance Systems,
Theorem Proving, Question Answering, and Planning systems.
THE UNDERSTANDING OF NATURAL LANGUAGE:
Formal Linguistics: Grammars and Machines, the Chomsky Hierarchy.
Syntactic Representation: Augmented Transition Networks (ATNs).
Semantic Representation: Conceptual Dependency, Story Understanding.
Spoken Language Understanding.
ROBOTICS: Machine Vision, Manipulator and Locomotion Control.
MACHINE LEARNING:
The Spectrum of Learning: Learning by Adaptation, Learning by Being
Told, Learning from Examples, Learning by Analogy, Learning by
Experimentation, Learning by Observation and Discovery.
Model Induction via Generate-and-Test, Automatic Theory Formation.
A Model for Intellectual Evolution.
RECAPITULATION AND CODA:
The knowledge representation and problem-solving paradigms of AI.
The key ideas and viewpoints in the modeling and creation of intelligence.
Is there more (or less) to Intelligence, Consciousness, the Soul?
Prospectus for the future.
Handouts for the course include:
1. Computer Science as Empirical Inquiry: Symbols and Search. 1975 Turing
Award Lecture by Allen Newell and Herb Simon; Communications of the ACM,
Vol. 19, No. 3, March 1976.
2. Steps Toward Artificial Intelligence. Marvin Minsky; Proceedings of the
IRE, Jan. 1961.
3. Computing Machinery and Intelligence. Alan Turing; Mind (Turing's
original proposal for the "Turing Test").
4. Exploring the Labyrinth of the Mind. James Gleick; New York Times
Magazine, August 21, 1983 (article about Doug Hofstadter's recent work).
TEXTBOOKS:
1. ARTIFICIAL INTELLIGENCE, Patrick H. Winston; Addison Wesley, 1983.
Will be available from publisher in early 1984. I will distribute a
copy printed from Patrick's computer-typeset manuscript.
2. LISP, Patrick Winston and Berthold K. P. Horn; Addison Wesley, 1981.
Excellent introductory programming text, illustrating many AI implementation
techniques at a level accessible to novice programmers.
4. GODEL, ESCHER, BACH: AN ETERNAL GOLDEN BRAID, Douglas R. Hofstadter;
Basic Books, 1979. One of the most entertaining books on the subject of AI,
formal systems, and symbolic modeling of intelligence.
5. THE HANDBOOK OF ARTIFICIAL INTELLIGENCE, Avron Barr, Paul Cohen, and
Edward Feigenbaum; William Kaufman Press, 1981 and 1982. Comes as a three
volume set. Excellent (the best available), but the full set costs over $100.
6. ANATOMY OF LISP, John Allen; McGraw-Hill, 1978. Excellent text on the
definition and implementation of LISP, sufficient to enable one to write a
complete LISP interpreter.
--
Spoken: Bob Giansiracusa
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USnail: Dept of Comp Sci, Penn State Univ, University Park, PA 16802