Common Lisp: Tutorial or Review.
If a tutorial is required, optional topics may be sacrificed.
Introduction to AI
Natural Intelligence
Artificial Intelligence
Brief History of AI
PROBLEM SOLVING AND SEARCH
State Space
Blind Search
Heuristic Search (including A*)
Adversary Search
KNOWLEDGE REPRESENTATION TOOLS
Logic
Semantic Nets
Frames
Probability
Fuzzy Logic
One or more of the following optional topics:
Transition Nets (including ATNs)
Inductive Logic
Non-monotonic Logic
Neural Nets
INTEGRATED AI SYSTEMS
Planning Systems
Rule-based Expert Systems
Constraint Propagation Systems
Truth Maintenance Systems
Learning Systems
One or more of the following optional topics:
Robotic Systems
Vision Systems
Natural Language Systems
Neural Network Systems
Connectionist Systems
Theorem-proving Systems
EVALUATION AND OVERVIEW
Ethical Issues in AI
What Computers Can Do
What Computers Still Can't Do
Laboratory Component
This course should include:
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)
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)
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)
oral presentations by anyone taking the course for graduate credit (e.g., a tutorial and live
demonstration of an existing AI program or system)