CSCI 450

Artificial Intelligence

Coordinator: Stephanie Schwartz

Credits: 4.0


Introduction to artificial intelligence including problem solving, search, heuristic methods, machine learning, knowledge representation, natural language processing, computer vision, expert systems, theorem proving and current applications. Concepts illustrated through programs developed in LISP or Prolog. Offered periodically.


C- or higher in CSCI 362 and ENGL 110.

Sample Textbooks

Course Outcomes

  1. Familiar with classical AI problems and solution methodologies of AI
  2. Proficient in programming in Lisp
  3. Familiar with current research topics in AI
  4. Aware of both the possibilities and the problems incumbent in AI research

Major Topics Covered

A. Introduction.

  1. AI Terminology and definitions.
  2. Brief History.
  3. Research topics in AI.

B. Lisp and Prolog.

  1. Basic functions.
  2. Using the system and interpreter.
  3. The structure of the languages.
  4. List Processing.
  5. Recursion.
  6. When to use which language, advantages and shortcomings of each.

C. Searching and Problem Representation.

  1. Hill climbing.
  2. AND/OR trees.
  3. Alpha/beta technique.
  4. Means-ends analysis.
  5. The A* algorithm.
  6. Depth-first.
  7. Breadth-first.
  8. Best-first.
  9. Heuristic search and evaluation functions.

D. Representing Knowledge.

  1. Semantic nets.
  2. Scripts.
  3. Frames.
  4. Logic and resolution.
  5. Propositional calculus and predicate logic.
  6. Predicate calculus

E. Expert systems.

  1. Introduction and example systems - Mycin, Xcon/R1.
  2. Prolog, logic programming, and expert systems.
  3. Tools, Expert Systems Shells, and Programming.

F. Computer Vision.

  1. Introduction - simple techniques.
  2. Edges, Line and Point Processing.
  3. Blocks World Heuristics.

G. Natural Language Processing.

  1. Chomsky's grammars.
  2. Syntax, semantics, pragmatics.
  3. ATN's.
  4. Machine translation.

H. Machine Learning.

  1. Learning from examples.
  2. When to apply productions.
  3. Parallel processing.

I. Other Areas.

  1. Robotics and Artificial Intelligence.
  2. Speech Understanding.
  3. Blackboard systems.
  4. Neural Networks.

J. Future of artificial intelligence.

  1. Current research areas.
  2. Fifth generation computing.
  3. Application areas.
  4. Moral and social issues.