The midterm will be closed notes, books, laptops, and people. However, you may use a one-page cheat sheet of your own design (group design ok but not recommended). To clarify, this can now be both sides of the sheet if you like. You may also use a basic calculator, which will not be required, but which may be helpful.

Practice Exams

Practice exams:

Note that in previous years, topic were covered in varying orders, and so the midterm topics do not align perfectly.

You can also look at much older exams from other versions of the class.

Review Sessions and Office Hours

Possible Midterm Topics

Search:

• BFS, DFS, UCS, A*, Greedy search
• Search algorithms' strengths and weaknesses
• Properties: completeness, optimality
• Admissibility and heursitics
• Local search
• Basic robot motion planning (configuration spaces, etc)
• Be able to phrase search problems and create heuristics

Constraint Satisfaction Problems:

• Basic definitions and solution with DFS
• Forward checking, arc consistency
• Conditions under which CSPs are efficiently solvable
• Be able to phrase CSPs

Games:

• Minimax search
• Alpha-beta pruning
• Expectimax search
• Evaluation function design

Decisions:

• The MEU principle
• Reflex agents and policies
• Markov decision processes
• Reward functions
• Bellman Equations
• Value and policy iteration
• Be able to phrase a problem as an MDP

Reinforcement Learning:

• Exploration vs exploitation
• TD value learning / Q-learning
• Linear value function approximation