As part of your review
process, I recommend you study:
- Notes from class (slides are on the book's web site)
- Reread book chapters
- Homework and programming assignments
- Look over exercises at the end of the chapter in the book.
Chapter Three: Solving Problems by Searching, Focus on Sections 3.1-5
- Be able to define and identify the components of a problem
(states, initial state, successor function, goal test, path cost)
- Understand the general tree-search algorithm and the role of the
fringe
- Understand and be able to use the four ways we measure
problem-solving performance
- Describe the behavior of depth-first, breadth-first, iterative
deepening and uniform-cost search, and compare their relative
advantages and disadvantages
- Understand how to avoid repeated states
- Given an example of a state space be able to compute the fringe
Chapter Four: Informed Search and Exploration, Focus on 4.1-3
- How does “informed search” try to improve on “uninformed search”?
- Be able to discuss the role of the evaluation function in
informed search
- Describe the behavior of greedy best-first search
- Explain how A* search works, what its advantages are, and
identify any restrictions on heuristics which can be used with it
- Discuss strategies for inventing admissible heuristic functions
- Understand the state space landscape and how it impacts local
search algorithms
- Describe the ways hill climbing can be modified to avoid some of
the issues that come up in the landscape
- Describe the behavior of hill climbing, simulated annealing,
local beam, and genetic algorithms search, and compare their relative
advantages and disadvantages
- Given a problem and its corresponding state space be able to
suggest and justify a good search algorithm