Exam 1 -- Topics and Preparation

I have posted a small sample exam.

Objectives

The exam will cover the following topics:

• Intelligent Agents (Chapter 2) including: specifying the nature of environments and the structure of agents. Review problems from the book including 2.1, 2.2, and 2.4.
• Searching (Chapter 3) including uninformed (BFS, DFS, DLS, IDS) and informed search strategies (A*Star), and heuristic functions. Review problems from the book include 3.3, 3.6a,b, 3.10, 3.14, 3.23. The problems on our quiz could also be covered.
• Local search (Chapter 4) including optimization problems, landscapes (local max, local min, global max, global mins), hill climbing, random-restart hill climbing, simulated annealing. Know the definitions for complete, optimal, and probablistically complete and know how they relate to the methods discussed in class. Review problems 4.1 and be able to describe the differences between the methods discussed in lecture.
• Adversarial search (Chapter 5) including defining a zero-sum game, minimax algorithm, and alpha-beta pruning. Problems 5.8 and also be able to draw minimax and alpha-beta pruning examples labeling V, alpha, and beta for each node.

Logistics

• This is a closed-book, closed-notes, no-calculator exam. Do not refer to any materials other than the exam itself.
• Do not look at anyone else's exam. Do not talk to anyone but the instructor during the exam. Turn off all cell phones, etc.
• Use the restroom and take care of personal needs before you arrive. If you need to leave during the exam, ask for permission.