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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*

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- 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))

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