CS 4200 : Artificial Intelligence Methods

CS 4200: Artificial Intelligence Methods

Semester Hours:   3.0
Contact Hours:   3
Coordinator:   Qing Tian
Text:   Artificial Intelligence: A Modern Approach
Author(s):   Russell and Norvig
Year:   2010

SPECIFIC COURSE INFORMATION

Catalog Description

Intermediate AI programming with application to representative problems requiring searching, reasoning, planning, matching, deciding, parsing, seeing and learning. Prerequisite: CS 3350.

Course type: ELECTIVE

SPECIFIC COURSE GOALS

• I can explain the major challenges facing AI, both from a theoretical (research) and a practical (application) standpoint.
• I understand the properties of AI task environments well enough to give a correct PEAS (Performance, Environment, Actuators, Sensors) description of a specific task environment.
• For simple AI problems, I can formulate a correct abstract model consisting of states, actions, transitions, goals and costs.
• I can explain and implement basic AI search algorithms, including blind searches (depth-first, breadth-first) and informed searches (best-first and A*).
• I can describe and compare Hill-climbing search, simulated annealing, local beam, search and genetic algorithms.
• I can explain and implement, in script or pseudocode, the minimax algorithm and the alpha-beta pruning algorithm.
• I can describe and explain some agent-based AL architecture (e.g., game-playing agents).
• I can explain how propositional theorem-proving works.
• I understand the concepts of first-order predicate logic (FOPL) well enough to explain how forward- and backward-chaining algorithms work.

LIST OF TOPICS COVERED

• Introduction to 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