CS2013-final-report

Introduction to Artificial Intelligence, University of Hartford
Department of Computer Science
Ingrid Russell
irussell@hartford.edu
http://uhaweb.hartford.edu/compsci/ccli/
Knowledge Areas that contain topics and learning outcomes covered in the course
Knowledge Area
Intelligent Systems (IS) 24
Programming Languages (PL) 3
Total Hours of Coverage
Where does the course fit in your curriculum
The course is typically taken in the junior or senior year as an upper level elective. It is taken mostly by Computer
Science and Computer Engineering students. The Data Structures course is the prerequisite. There is no required
course that has this course as a prerequisite. Instructors may offer independent study courses that require this
course as a prerequisite. Student enrollment range is 10-24 per offering.
What is covered in the course
The AI topics below follow the topic coverage in Russell & Norvig’s Artificial Intelligence: A Modern Approach.
• Introduction to Lisp
• Fundamental Issues
What is AI Foundations of AI, History of AI.
• Intelligent Agents
Agents and Environments, Structure of Agents.
• Problem Solving by Searching
Problem Solving Agents, Searching for Solutions, Uninformed Search Strategies:
Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening
Depth-first Search, Comparison of Uninformed Search Strategies.
• Informed Search and Exploration
Informed (Heuristic) Search Strategies: Greedy Best-first Search, A* Search, Heuristic
Functions, Local Search Algorithms, Optimization Problems.
• Constraint Satisfaction Problems
Backtracking Search for CSPs, Local Search for CSPs.
• Adversarial Search
Games, Minimax Algorithm, Alpha-Beta Pruning.
• Reasoning and Knowledge Representation
Introduction to Reasoning and Knowledge Representation, Propositional Logic, First Order
Logic, Semantic Nets, Other Knowledge Representation Schemes.
• Reasoning with Uncertainty & Probabilistic Reasoning
Acting Under Uncertainty, Bayes’ Rule, Representing Knowledge in an Uncertain
Domain, Bayesian Networks.
• Machine Learning
Forms of Learning, Decision Trees and the ID3 Algorithm, Nearest Neighbor, Statistical Learning.
- 315 -