Course Description

Intermediate AI programming with application to representative problems requiring searching, reasoning, planning, matching, deciding, parsing, seeing and learning. Prerequisite: Junior or senior standing.

Course Syllabus

1. Course Description
2. Standard Syllabus
   1. INTRODUCTION
      1. Common Lisp: Tutorial or Review.
         If a tutorial is required, optional topics may be sacrificed.
      2. Introduction to AI
         1. Natural Intelligence
         2. Artificial Intelligence
         3. Brief History of AI
   2. PROBLEM SOLVING AND SEARCH
      1. State Space
      2. Blind Search
      3. Heuristic Search (including A*)
      4. Adversary Search
   3. KNOWLEDGE REPRESENTATION TOOLS
      1. Logic
      2. Semantic Nets
      3. Frames
      4. Probability
      5. Fuzzy Logic
      6. One or more of the following optional topics:
         1. Transition Nets (including ATNs)
         2. Inductive Logic
         3. Non-monotonic Logic
         4. Neural Nets
   4. INTEGRATED AI SYSTEMS
      1. Planning Systems
      2. Rule-based Expert Systems
      3. Constraint Propagation Systems
      4. Truth Maintenance Systems
      5. Learning Systems
      6. One or more of the following optional topics:
         1. Robotic Systems
         2. Vision Systems
         3. Natural Language Systems
         4. Neural Network Systems
         5. Connectionist Systems
         6. Theorem-proving Systems
   5. EVALUATION AND OVERVIEW
      1. Ethical Issues in AI
      2. What Computers Can Do
      3. What Computers Still Can't Do
3. Laboratory Component
   This course should include:
   1. at least one programming assignment in which students design and implement a non-trivial AI algorithm or program from scratch (e.g., branch-and-bound search)
   2. at least one assignment in which students solve a problem using a "real" AI system (e.g., a system from PAIL, the Portable AI Laboratory)
   3. at least one assignment in which students use an AI language or tool to improve or modify an AI system (e.g., add additional rules to an existing expert system, then revalidate the system)
   4. oral presentations by anyone taking the course for graduate credit (e.g., a tutorial and live demonstration of an existing AI program or system)