Some sample exam 1 questions:
- BRIEFLY define the following terms and give an example of how each term is used.
- The Turing Test
- Rational Agent
- Percept
- PEAS
- Uninformed Search
- Depth-Limited Search
- Iterative Deepening
- Best-first Search
- Greedy Search
- Heurstic
- Admissible Heuristic
- Consistency for a Heuristic
- Dominance for Heuristic
- Iterative Improvement Algorithm
- Global Maximum (Minimum)
- Local Maximum (Minimum)
- Simulated Annealing Algorithm
- Beam Search
- Constraint Satisfaction Problem
- Forward Checking
- Arc Consistency
- Perfect Information Game
- Imperfect Information Game
- Deterministic Game
- Chance Game
- Game versus Search
- Evaluation Functions
- Knowledge Based
- Inference Engine
- Knowledge-Based Agent
- Syntax of a Logic
- Semantics of a Logic
- Entailment
- Model
- Inference
- Sound Inference
- Complete Inference

- Pick an agent problem and explain the terms observable, deterministic, episodic, static, discrete and single-agent with respect to that problem.
- Give algorithms for the following (in addition, for each algorithm indicate whether the search is complete, optimal and give some estimate of the cost of the algorithm):
- Breadth-First Search
- Depth-First Search
- A* Search
- Simulated Annealing
- Hill Climbing
- Minimax Search
- Minimax Search with alpha-beta pruning

- Define a graph with costs and a heuristic cost to the goal for each node and show the order the nodes would be visited in a depth-first search, breadth-first search and an A* search.
- Why is it important for a heuristic to be admissible for A* search to be optimal. Give an example where a heuristic is not admissible and would result in a non-optimal path.
- What is a constraint satisfaction problem? Give an example and show a solution to that problem.
- What form of search is used to solve constraint satisfaction problem? Define the following heuristics and how they relate to this search: minimum remaining values, degree heuristic, and least constraining value.
- In constraint satisfaction, what is forward checking, constraint propagation and arc consistency?
- Show minimax search for some game tree. How would alpha-beta pruning apply?
- Does alpha-beta pruning effect the optimality of minimax search? Explain.
- Why is minimax search generally not used for real games? How is the search algorithm altered for such games?
- How is chance incorporated into minimax search? Give an example.
- How is a logic described? Give an example of a logic and explain all important aspects.