CS 6200 : Advanced Topics in Artificial Intelligence

CS 6200: Advanced Topics in Artificial Intelligence

Semester Hours:   3.0
Contact Hours:   3
Text:   TBD
Author(s):   TBD
Year:   TBD

SPECIFIC COURSE INFORMATION

Catalog Description

Intensive study of a major sub-field such as neural networks, expert systems, machine learning/tutoring, natural language processing, pattern recognition, robotics, or others.

Course Type: ELECTIVE

SPECIFIC COURSE GOALS

• TBD

LIST OF TOPICS COVERED

• Introduction
  • Definitions
    • AI, Expert System, Rule-Based Expert System (RBES)
  • How an RBES works
  • Brief history of RBES
  • Applications of RBES
• Foundation of REBES: Rule-Based Production Systems (RBPS)
  • Production system formalisms
  • Operational principles of RBPS
  • Evaluation of RBPS
    • Advantages
    • Disadvantages
• Inference Engines (Automated RBPS)
  • Search
  • Chaining
  • Conflict resolution
  • Success and failure
• Development of RBES using CLIPS (NASA’s RBES tool)
  • Tutorial on CLIPS
  • Preconditions
  • Stages
    • Problem selection
    • Knowledge acquisition: elicitation and induction
    • Knowledge representation: facts and rules
    • Design of the human interface
    • Design of the production system
    • Design of the explanation system
    • Iterative prototyping
    • Verification: consistency and completeness
    • Validation
    • Application
  • Problems and pitfalls
• Fuzzy Logic
  • Representation of uncertainty
    • Abstraction as a solution
    • Bayesian logic as a solution
    • Certainty factors as a solution
    • Fuzzy logic as a solution
  • Tutorials on fuzzy logic
    • Classical Set Theory (Cantor)
    • Multi-Valued Logic (Lukasiewics)
      • Relationships: complement, containment, intersection, union
      • Formal definitions
      • Membership graphs: S, Z, and Pi
      • Linguistic Variables, Values, and Modifiers (Hedges)
• Development of RBES Using Fuzzy CLIPS
  • Tutorial on Fuzzy CLIPS (an extension of CLIPS)
  • Design considerations
    • Preconditions for a “Fuzzy” solution
    • Methods of representing uncertainty in Fuzzy CLIPS
  • Major application areas for fuzzy expert systems
  • Advantages of Fuzzy Inference Control
• Case Studies of Successfully Deployed Expert Systems
  • MACSYMA
  • MYCIN
  • XCON
  • PROSPECTOR
• Evaluation of Expert Systems
  • Ethical issues in expert systems
  • Benefits of expert systems compared to human experts
  • Limitations of expert systems