Some sample exam 1 questions:

1. 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
2. Pick an agent problem and explain the terms observable, deterministic, episodic, static, discrete and single-agent with respect to that problem.
3. 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
4. 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.
5. 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.
6. What is a constraint satisfaction problem? Give an example and show a solution to that problem.
7. 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.
8. In constraint satisfaction, what is forward checking, constraint propagation and arc consistency?
9. Show minimax search for some game tree. How would alpha-beta pruning apply?
10. Does alpha-beta pruning effect the optimality of minimax search? Explain.
11. Why is minimax search generally not used for real games? How is the search algorithm altered for such games?
12. How is chance incorporated into minimax search? Give an example.
13. How is a logic described? Give an example of a logic and explain all important aspects.