1. “If I Only had a Brain” Search
Lecture 3-1
January 19th, 1999
CS250
Lecture 3-1
CS250: Intro to AI/Lisp

2. Blind Search No information except
Initial state
Operators
Goal test
If we want worst-case optimality, need exponential time
Lecture 3-1
CS250: Intro to AI/Lisp

3. “How long ‘til we get there?”
Add a notion of progress to search
Not just the cost to date
How far we have to go
Lecture 3-1
CS250: Intro to AI/Lisp

4. Best-First Search Next node in General-Search
Queuing function
Replace with evaluation function
Go with the most desirable path
Lecture 3-1
CS250: Intro to AI/Lisp

5. Heuristic Functions Estimate with a heuristic function, h(n)
Problem specific (Why?)
Information about getting to the goal
Not just where we’ve been
Examples
Route-finding?
8 Puzzle?
Lecture 3-1
CS250: Intro to AI/Lisp

6. Greedy Searching Take the path that looks the best right now
Lowest estimated cost
Not optimal
Not complete
Complexity?
Time: O(bm)
Space: O(bm)
Lecture 3-1
CS250: Intro to AI/Lisp

7. Best of Both Worlds? Greedy Uniform cost
Minimizes total estimated cost to goal, h(n)
Not optimal
Not complete
Uniform cost
Minimizes cost so far, g(n)
Optimal & complete
Inefficient
Lecture 3-1
CS250: Intro to AI/Lisp

8. Greedy + Uniform Cost Evaluate with both criteria
f(n) = g(n) + h(n)
What does this mean?
Sounds good, but is it:
Complete?
Optimal?
Lecture 3-1
CS250: Intro to AI/Lisp

9. Admissible Heuristics
Optimistic: Never overestimate the cost of reaching the goal
A* Search = Best-first + Admissible h(n)
Lecture 3-1
CS250: Intro to AI/Lisp

10. A* Search Complete Optimal, if: Heuristic is admissible Lecture 3-1
CS250: Intro to AI/Lisp