ComS 472: Principles of Artificial Intelligence
Department of Computer Science
Iowa State University
Fall 2009

Course Information

Course Prerequisites

  • Knowledge of data structures (lists, trees, graphs etc.), algorithms (design and analysis), and programming language concepts, and programming experience in a contemporary high-level language, such as C, C++, Java.
  • Knowledge of discrete mathematics, calculus, and basic probability theory. (Necessary mathematics will be reviewed as they arise.)

These topics are covered in Com S 330, 311, 342, and Stat 330. In addition, students are expected to have the writing and presentation skills necessary for preparing written reports and presentations based on term projects. These skills are taught in Engl 250, Sp Cm 212. 

Some of the laboratory assignments will require you to program in Java. If you do not know Java already, you are expected to quickly acquire a working knowledge of Java on your own. If there is sufficient interest, a few of the recitation sessions could be used to introduce Java as needed.

If you are not sure whether you have the necessary background, please talk to the instructor.

Target Audience

This course is targeted to senior undergraduate students in Computer Science at Iowa State University. The course should be of interest to graduate students in Bioinformatics and Computational Biology, Human-Computer Interaction, as well as graduate and undergraduate students from a variety of disciplines interested in learning about Artificial Intelligence.

Graduate students in Computer Science should enroll in Com S 572. Undergraduate students in the Honors program should enroll in Com S 472 Honors. All other undergraduates and graduate students should enroll in Com S 472.

Course Objectives

The primary objective of this course is to provide an introduction to the basic principles and applications of Artificial Intelligence. Programming assignments are used to help clarify basic concepts. The emphasis of the course is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments. In short, this course is about the design and implementation of intelligent agents---software or hardware entities that perform useful tasks with some degree of autonomy. Upon successful completion of the course, students will have an understanding of the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, and learning -- and their applications (e.g., data mining, information retrieval). Students will also be able to design and implement key components of intelligent agents of moderate complexity in Java and evaluate their performance.

Learning outcomes: A, B, C, D, G, H

http://www.cs.iastate.edu/gradadm/cmarquar/Undergraduate-Program-Outcomes.pdf

Textbook

The primary text for the course is: Artificial Intelligence: A Modern Approach, 2nd Edition, by Stuart Russell and Peter Norvig.

The course will sometimes draw upon additional references and readings to supplement the treatment of topics available in the primary textbook. Students should consult the weekly study guide for specific reading assignments.

Syllabus

The following gives a tentative list of topics to be covered in the course

Overview: what is artificial intelligence (AI)? foundations, history of AI.

Intelligent agents: rationality, task environments, structure of agents.

Problem-solving through Search: formulation of state-space search problems, uninformed, heuristic, and local search algorithms, constraint satisfaction problems (CSPs), games, sample applications.

Knowledge Representation and Reasoning: knowledge representation using logic, propositional logic, first-order logic, inference, sample applications.

Representing and Reasoning with Uncertain Knowledge: probability theory, Bayesian networks, probabilistic inference, sample applications.

Machine Learning and Knowledge Acquisition: supervised learning, classification, Naive Bayes, decision tree, neural networks, nearest neighbor, sample applications.

Planning: planning as search, partial order planning, construction and use of planning graphs.

Student project presentations.