Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- The AI Rush by Jean-Baptiste Dumont 1146259 views
- AI and Machine Learning Demystified... by Carol Smith 3650296 views
- 10 facts about jobs in the future by Pew Research Cent... 677608 views
- 2017 holiday survey: An annual anal... by Deloitte United S... 1093286 views
- Harry Surden - Artificial Intellige... by Harry Surden 639193 views
- Inside Google's Numbers in 2017 by Rand Fishkin 1221142 views

174 views

Published on

Outline:

1. Technologies behind self-driving vehicles

- Motion Planning

- Decision Making

2. Graph Search Algorithms

- Depth-First Search

- Breadth-First Search

- A* Search

3. Incremental Heuristic Search Algorithms

- Repeated A* Search

- Adaptive A*

- Generalized Fringe-Retrieving A*

Published in:
Technology

No Downloads

Total views

174

On SlideShare

0

From Embeds

0

Number of Embeds

0

Shares

0

Downloads

18

Comments

0

Likes

1

No embeds

No notes for slide

- 1. HEURISTIC SEARCH AI Frontiers 2018
- 2. St. Louis Indianapolis Columbus Louisville Nashville Chicago Cleveland Pittsburgh 297 309 261 243 183 346 133 144 185 176 206114 175
- 3. UNINFORMED SEARCH Generic uninformed search pseudo-code: (1) Start with a tree that contains only the start state (2) Pick a fringe node n (3) If fringe node n represents a goal state, then stop (4) Expand fringe node n* (5) Go to (2) *generate neighboring nodes that aren’t ancestors
- 4. BREADTH-FIRST SEARCH Breadth-ﬁrst search pseudo-code: (1) Start with a tree that contains only the start state (2) Pick a fringe node n with the smallest depth (3) If fringe node n represents a goal state, then stop (4) Expand fringe node n* (5) Go to (2) *generate neighboring nodes that aren’t ancestors
- 5. DEPTH-FIRST SEARCH Depth-ﬁrst search pseudo-code: (1) Start with a tree that contains only the start state (2) Pick a fringe node n with the largest depth (3) If fringe node n represents a goal state, then stop (4) Expand fringe node n* (5) Go to (2) *generate neighboring nodes that aren’t ancestors
- 6. UNIFORM-COST SEARCH Uniform-cost search pseudo-code: (1) Start with a tree that contains only the start state (2) Pick a fringe node n with the smallest g(n) (3) If fringe node n represents a goal state, then stop (4) Expand fringe node n* (5) Go to (2) *generate neighboring nodes that aren’t ancestors
- 7. Videos are from slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu.
- 8. Videos are from slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu. BFS DFS UCS COMPARISONS
- 9. St. Louis (0) Indianapolis (120) Columbus (210) Louisville (130) Nashville (150) Chicago (150) Cleveland (280) Pittsburgh (300) 297 309 261 243 183 346 133 144 185 176 206114 175
- 10. A* SEARCH A* search pseudo-code: (1) Start with a tree that contains only the start state (2) Pick a fringe node n with the smallest g(n) + h(n) (3) If fringe node n represents a goal state, then stop (4) Expand fringe node n* (5) Go to (2) *generate neighboring nodes that aren’t ancestors
- 11. Videos are from slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu.
- 12. Videos are from slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu. BFS DFS A* UCS
- 13. HEURISTICS • Heuristics are admissible if they do not over-estimate the true distances h(n) ≤ c(n, goal) • Heuristics are consistent if they satisfy the triangle inequality h(n) ≤ c(n, n’) + h(n’) h(goal) = 0 n n’ goal c(n, n’) h(n) h(n’)
- 14. HEURISTICS • Heuristics need to be computed very quickly • Typically computed by relaxing the problem: add some states or actions. • Examples: • Zero heuristics • Straight-line heuristics • Manhattan heuristics
- 15. PROPERTIES OF A* • Consistent heuristics Admissible heuristics • Consistent heuristics ⇍ Admissible heuristics
- 16. PROPERTIES OF A* • Consistent heuristics Admissible heuristics • Consistent heuristics ⇍ Admissible heuristics • If A* uses consistent heuristics, then it is correct
- 17. PROPERTIES OF A* • Consistent heuristics Admissible heuristics • Consistent heuristics ⇍ Admissible heuristics • If A* uses consistent heuristics, then it is correct • If A* uses admissible but inconsistent heuristics, then it is correct if it re-expand states
- 18. PROPERTIES OF A* • If A* uses consistent heuristics, then it is efﬁcient No other search algorithm that uses the same heuristics, which is correct and complete, can expand fewer nodes than A* (except for breaking ties at nodes n with f(n) = f(goal)) • If h1(n) ≥ h2(n) for all states n, then A* using h1(n) is at least as efﬁcient as using h2(n) A* using h1(n) expands at most the same number of states as A* using h2(n) (except for breaking ties at nodes n with f(n) = f(goal))

No public clipboards found for this slide

Be the first to comment