CSS 382: Artificial Intelligence!
Search Continued
Instructor: Roger Stanev, Ph.D.
University of Washington Bothell
(slides adapted from Anca Dragan, Dan Klein, Pieter Abbeel and others from Berkeley)
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Guidelines and tips
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community
Ask the community for help and clear up your study doubts
University Rankings
Discover the best universities in your country according to Docsity users
Free resources
Our save-the-student-ebooks!
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
Artificial Intelligence: Search Algorithms, Lecture notes of Refrigeration and Air Conditioning
An overview of various search algorithms used in artificial intelligence, including depth-first search (dfs), breadth-first search (bfs), uniform cost search (ucs), and a* search. It covers the key concepts, strategies, and properties of these algorithms, as well as their implementation details. The document also discusses the importance of heuristics in informed search algorithms like a* and the optimality guarantees of a* under certain conditions. The content is suitable for university-level courses in artificial intelligence, computer science, or related fields, and could be useful for students as study notes, lecture notes, or exam preparation material.
Typology: Lecture notes
2023/2024
1 / 51
This page cannot be seen from the preview
Don't miss anything!
Related documents
Partial preview of the text
Download Artificial Intelligence: Search Algorithms and more Lecture notes Refrigeration and Air Conditioning in PDF only on Docsity!
CSS 382: Artificial Intelligence!
Search Continued
Instructor: Roger Stanev, Ph.D.
University of Washington Bothell
(slides adapted from Anca Dragan, Dan Klein, Pieter Abbeel and others from Berkeley)
Recap: Search
Depth-First Search
S
a
b
d
p
a
c
e
p
h
f
r
q
q c G
a
q e
p
h
f
r
q
q c G
a
S
G
d
b
p
q
c
e
h
a
f
r
q
p
h
f
d
b
a
c
e
r
Strategy: expand a
deepest node first
Implementation:
Fringe is a LIFO stack
Breadth-First (Tree) Search
Iterative Deepening
o Idea: get DFS’s space advantage with
BFS’s time / shallow-solution
advantages
o Run a DFS with depth limit 1. If no
solution…
o Run a DFS with depth limit 2. If no
solution…
o Run a DFS with depth limit 3. …..
o Isn’t that wastefully redundant?
o Generally most work happens in the lowest
level searched, so not so bad!
b
Cost-Sensitive Search
ST
AR
T
G
O
AL
d
b
p
q
c
e
h
a
f
r
Uniform Cost Search
Uniform Cost Search
S
a
b
d
p
a
c
e
p
h
f
r
q
q c G
a
q e
p
h
f
r
q
q c G
a
Strategy: expand a
cheapest node first:
Fringe is a priority queue
(priority: cumulative cost)
S
G
d
b
p
q
c
e
h
a
f
r
Cost
contours
Uniform Cost Issues
o Remember: UCS explores increasing
cost contours
o The good: UCS is complete and
optimal!
o The bad:
o Explores options in every “direction”
o No information about goal location
o We’ll fix that soon!
Start Goal
c ≤ 3
c ≤ 2
c ≤ 1
[Demo: empty grid UCS (L2D5)]
[Demo: maze with deep/shallow
water DFS/BFS/UCS (L2D7)]
Video of Demo Empty UCS
Video of Demo Maze with Deep/Shallow Water --- DFS, BFS, or UCS? (part
Video of Demo Maze with Deep/Shallow Water --- DFS, BFS, or UCS? (part