CSC481 Syllabus

  1. Course number and name

    CSC481 – Artificial Intelligence

  2. Credits and contact hours

    3 Credit Hours

  3. Instructor’s or course coordinator’s name

    Instructor: Dr. Richard Wyatt, Associate Professor of Computer Science

  4. Text book, title, author, and year

    • Recommended text for AI: Artificial Intelligence, Elaine Rich and Kevin Knight, 1991.
    • Optional text for Lisp: any standard Common Lisp text, such as: An Interactive Approach, Shapiro, S. C., 1992. (Available for free from the author's web site)

    Other Supplemental Materials

    A number of papers by/on: Jack Copeland, Stuart Shapiro, Drew McDermott, Allen Newell and Herbert Simon, Eliza.

  5. Specific course information

    1. brief description of the content of the course (catalog description)

      Artificial Intelligence (AI) is concerned with the replication or simulation on a machine of the complex behaviors associated with intelligence. Topics will be drawn from any of those comprising the field of AI such as agent architectures, automatic truth maintenance, constraint satisfaction, expert systems, fuzzy logic, games, genetic algorithms, knowledge representation, machine learning, neural networks and connectionism, natural language processing, planning, reasoning, robotics, search, theorem proving, and vision. Projects requiring coding will focus on an AI language such as Common Lisp or Prolog.

    2. prerequisites or co-requisites

      Prerequisite: CSC 220: Foundations of Computer Science and CSC 241: Data Structures and Algorithms

    3. indicate whether a required, elective, or selected elective course in the program

      Elective course.

    1. specific outcomes of instruction Students will demonstrate:

      • a basic understanding of the central areas of artificial intelligence.
      • a basic understanding of the philosophical issues associated with artificial intelligence.
      • a basic understanding of the strengths and weaknesses the three main AI architectures (PSSH, neural nets, genetic algorithms).
      • an understanding of rule-based systems, including matching and unification.
      • an understanding of AI problem solving approaches based upon state space search, exhaustive search, and heuristic search.
      • an understanding of AI problem solving approaches based upon state space search, exhaustive search, and heuristic search.
      • some exposure to actual Artificial Intelligence systems (CYC and SNePS).
      • a more detailed understanding of some AI areas: NLP and Inference/Resolution.
      • a passing acquaintance with an "AI language" (Common Lisp).

    2. explicitly indicate which of the student outcomes listed in Criterion 3 or any other outcomes are addressed by the course.

      Course addresses Student Outcomes (a), (b), (c), and (i).

  7. Brief list of topics to be covered
    • Introduction: what is Artificial Intelligence? Can machines really think?
    • Artificial intelligence Architectures
    • Production systems and matching
    • Blind search and heuristic search
    • KR: general issues, semantic networks, frames, inheritance, scripts, primitive acts
    • Logic: propositional and predicate, resolution
    • CYC
    • Natural language processing
    • Neural nets and genetic algorithms
    • Additional topics, such as Planning, if time permits