CSC 447 Artificial Intelligence

Course Goals

The primary objective of this course is to give the student an introduction to the theory and practice of artificial intelligence. From a theoretical standpoint, we will discuss topics such as AI knowledge representations and AI problem solving approaches. From a practical standpoint, we will consider low-level problem solving approaches (such as artificial neural networks and genetic algorithms) and as well as the high-level symbolic approach based upon state space search. Knowledge representation schemes and inference mechanisms will focus upon use of predicate logic and its variations (probabilistic reasoning, fuzzy logic, etc.), discussed primarily in the context of expert systems. An important AI programming language (Lisp) will be introduced.

Course Outcomes

A student who successfully completes this course should, at a minimum, be able to:

  1. basic understanding of artificial intelligence, including an appreciation of the central issues and problems of the field
  2. understanding of low-level AI problem solving approaches based upon artificial neural networks and genetic algorithms
  3. understanding of high-level AI problem solving approaches based upon state space search, including exhaustive search techniques (depth-first search and breadth-first search), heuristic search techniques (hill climbing, A*), and game playing (minimax with alpha-beta pruning)
  4. understanding of major AI knowledge representations (semantic nets and frames) and inference mechanisms (predicate logic, probabilistic reasoning, fuzzy logic, rule-based expert systems)
  5. experience writing AI programs (C++, Lisp)
  Computer Science Student Outcomes
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