Class Policies



Students with documented disabilities who may need accommodations, who have any emergency medical information the instructor should be aware of, or who need special arrangements in the event of evacuation, should make an appointment with the instructor as early as possible, and no later than the first week of the term. Class materials will be made available in accessible format upon request.

Grading and Exams

An approximate weighting of the marks in the course is listed below. This may change slightly depending on the number and size of the assignments.

The midterm and exam are are open-book. Do not rely too heavy on your notes during an exam! Use them as a safety net.

Policy on collaboration

In solo assignments, collaboration is limited to verbal discussion of general approaches and strategies for the assignment. You can give each other examples that are not in the assignment. If you collaborate in this way, you will be asked to declare your collaborators.

Things not allowed:

For assignments done in teams, team members within the same team may explicitly discuss answers. However, the rules above apply between teams. For further details, please refer to the OSU Academic dishonesty policy and the CS Academic dishonesty policy.

Late Policy

Assignments are due at the start of class. The late policy is as follows:

If you hand in a late written assignment, please slip the assignment under my office door (KEC 2075).


I will use Blackboard for two purposes in this course:

  1. Storing and distributing your grades. Let us know if there are any mistakes in your grades. Please check them after each assignment and exam.
  2. Discussion board. If you have questions about assignments or exams, the following options are available:

Learning Objectives

  1. Analyze the dimensions along which agents and environments vary, along with key functions that must be implemented in a general agent.
  2. Implement agents using search algorithms such as uninformed search, informed search or local search.
  3. Develop strategies for agents in games of perfect and imperfect information.
  4. Represent knowledge of the world using logic and infer new facts from that knowledge.
  5. Use a Bayesian network to make quantitative (probabilistic) and qualitative inferences.
  6. Implement a Bayesian network that solves a simple version of a problem such as text categorization or object recognition.

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