A* Search
a_star_search | algorithm
Coverage Score
1
Mentioned Chunks
47
Mentioned Docs
7
Required Dimensions
definitioncompletenessoptimalitytime_complexityspace_complexityheuristicspros_cons
Covered Dimensions
completenessdefinitionheuristicsoptimalitypros_consspace_complexitytime_complexity
Keywords
f(n)=g(n)+h(n)optimal with admissible heuristic
Relations
| Source | Type | Target | W |
|---|---|---|---|
| A* Search | CO_OCCURS | Informed Search | 20 |
| A* Search | CO_OCCURS | Algorithm Evaluation Criteria | 16 |
| A* Search | CO_OCCURS | State-Space Search | 16 |
| A* Search | CO_OCCURS | Heuristic Function | 12 |
| A* Search | CO_OCCURS | Breadth-First Search | 12 |
| A* Search | CO_OCCURS | Inference | 10 |
| A* Search | CO_OCCURS | Uniform-Cost Search | 9 |
| A* Search | CO_OCCURS | Uninformed Search | 9 |
| A* Search | CO_OCCURS | Greedy Best-First Search | 7 |
| A* Search | CO_OCCURS | Problem Formulation | 6 |
| A* Search | CO_OCCURS | Admissible Heuristic | 6 |
| A* Search | CO_OCCURS | Propositional Logic | 5 |
| A* Search | CO_OCCURS | Auto Topic: conference | 5 |
| A* Search | CO_OCCURS | Constraint Satisfaction Problem | 4 |
| A* Search | CO_OCCURS | Logical Agents | 4 |
| A* Search | CO_OCCURS | Auto Topic: international | 4 |
| A* Search | CO_OCCURS | Minimax | 4 |
| A* Search | CO_OCCURS | Alpha-Beta Pruning | 4 |
| A* Search | CO_OCCURS | Consistent Heuristic | 4 |
| A* Search | CO_OCCURS | Backtracking Search | 3 |
| A* Search | CO_OCCURS | Making Simple Decisions | 3 |
| A* Search | CO_OCCURS | Auto Topic: self | 3 |
| Greedy Best-First Search | CONTRASTS_WITH | A* Search | 1 |
| Heuristic Function | SUPPORTS | A* Search | 1 |
| Admissible Heuristic | SUPPORTS_PROPERTY | A* Search | 1 |
| Consistent Heuristic | SUPPORTS_PROPERTY | A* Search | 1 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.93 | 8 | ... rom n • h(n) ≥ 0 so h(G)=0 for any goal G. Example: hSLD(n) never overestimates the actual road distance Theorem: If h(n) is admissible, A* using Tree Search is optimal A* is optimal with admissible heuristic https://www.redblobgames.com/pathfinding/a-star/introduction.html Idea: ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.82 | 5 | ... 🡪 Sibiu 🡪 Rimnicu Vilcea 🡪 Pitesti🡪 Bucharest (418km) Arad 118 BFS v. Greedy Best-First Search https://www.redblobgames.com/pathfinding/a-star/introduction.html Properties of greedy best-first search Complete? • No – can get stuck in loops, • e.g., Iasi 🡪 Neamt 🡪 Iasi 🡪 Neamt 🡪… A ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.79 | 9 | ... an admissible heuristic function,h4, such thath4(n)< h1(n) for any noden. 21. (1 point) Which of the following algorithms are extended from A* to reduce memory requirements and can use all available memory? (Check all that apply) □IDA* (iterative-deepening A*) □RBFS (Recursive be ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.79 | 8 | rontier queue: Arad 366 A* search example Frontier queue: Sibiu 393 Timisoara 447 Zerind 449 We add the three nodes we found to the Frontier queue. We sort them according to the g()+h() calculation. CIS 550 | Property of Penn Engineering | 41 A* search example Frontier queue: Rim ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.79 | 8 | n path is found by A* search usingh 1? ⃝S, B, D, G ⃝S, B, C, G ⃝S, A, G ⃝None of the above 4.7.3. What solution path is found by A* search usingh 3? ⃝S, B, D, G ⃝S, B, C, G ⃝S, A, G ⃝None of the above CIS 4210/5210 - Practice Midterm 1 Page 10 of 17 Your name: 26. (1 point) In th ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.79 | 7 | ... Definition of f-cost Admissibility of h h = 0 at a goal … Slide credit: Dan Klein and Pieter Abbeel http://ai.berkeley.edu Optimality of A* Tree Search Proof: Imagine B is on the frontier Some ancestor n of A is on the frontier, too (maybe A!) Claim: n will be expanded before B • ... |
quizzes Quizes/Week 3 Informed Search With Answers.pdf | 0.79 | 7 | Multiple Answer 1 point Question Which of the following algorithms search algorithms utilize a priority queue? A* Search Breadth-First Search Greedy Best-First Search Uniform Cost Search 1 Multiple Choice 1 point Question Which of the following is true about greedy best-first sea ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.77 | 6 | ... h • Notice: hmd dominates hoop Typical search costs (average number of nodes expanded): d=12Iterative Deepening Search = 3,644,035 nodes A*(hoop) = 227 nodes, A*(hmd) = 73 nodes d=24 IDS = too many nodes A*(hoop) = 39,135 nodes, A*(hmd) = 1,641 nodes The best and worst admissible ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.77 | 6 | t-optimal ⃝Using an open-set approach, A* search with an admissible heuristic is guaranteed cost-optimal ⃝Beam search is a memory efficient search algorithm. ⃝Every consistent heuristic is admissible. CIS 4210/5210 - Practice Midterm 1 Page 9 of 17 Your name: 25. (3 points) Consi ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.74 | 14 | eley.edu … Properties of A* Slide credit: Dan Klein and Pieter Abbeel http://ai.berkeley.edu Properties of A* …b …b Uniform-Cost A* Slide credit: Dan Klein and Pieter Abbeel http://ai.berkeley.edu UCS vs A* Contours Uniform-cost expands equally in all “directions” A* expands main ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.73 | 4 | ... eady(n)= hteleportation(n)=0 Admissible: both yes!!! h*(n) dominates all other heuristics hteleportation(n) is dominated by all heuristics A* search is Optimal AIMA 3.5 A* search Best-known form of best-first search. Key Idea: avoid expanding paths that are already expensive, but ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.69 | 2 | ... 8 (not), 235 ∨ (or), 235 ≻ (preferred), 520 σ (sigma) standard deviation, 1079 T (matrix transpose), 1077 A A(s) (actions in a state), 552 A* search, 103–108 Aaronson, S., 1035, 1085 Aarts, E., 190, 1085 Aarup, M., 402, 1085 Abbas, A., 549, 550, 1085 Abbeel, P., 80, 516, 551, 638 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.69 | 2 | tions in a state), 552 A* search, 103–108 Aaronson, S., 1035, 1085 Aarts, E., 190, 1085 Aarup, M., 402, 1085 Abbas, A., 549, 550, 1085 Abbeel, P., 80, 516, 551, 638, 737, 841, 868, 872, 873, 985, 986, 1055, 1085, 1091, 1095, 1097, 1099, 1102, 1103, 1112 Abbott, L. F., 839, 873, 1 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.69 | 2 | ... 27, 1111 Wei, X., 906, 1109 Weibull, J., 638, 1116 Weidenbach, C., 330, 1116 weight, 694 weight decay, 822 WEIGHTED -S AMPLE , 458 weighted A* search, |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.69 | 2 | ... 27, 1111 Wei, X., 906, 1109 Weibull, J., 638, 1116 Weidenbach, C., 330, 1116 weight, 694 weight decay, 822 WEIGHTED -S AMPLE , 458 weighted A* search, 109 weighted linear function, 203 weight space, 695 Weinstein, S., 734, 1108 Weiss, D., 904, 1085 Weiss, G., 80, 640, 1116 Weiss, ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.69 | 2 | ... w idea!) o Implementation: Sort frontier queue by this new f(n). ▪ Special cases: greedy search, and A* search Simple, useful estimate heuristic: straight-line distances Arad 118 |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.69 | 2 | distance to goal (new idea!) o Implementation: Sort frontier queue by this new f(n). ▪ Special cases: greedy search, and A* search Simple, useful estimate heuristic: straight-line distances Arad 118 Heuristic (estimate) functions [dictionary]“A rule of thumb, simplification, or ed ... |
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.69 | 2 | ... doesn’t end until we “expand” the goal node – it has to be at the top of the Frontier queue. CIS 550 | Property of Penn Engineering | 44 A* search example Frontier queue: Bucharest 418 Timisoara 447 Zerind 449 Bucharest 450 Craiova 526 Sibiu 553 Sibiu 591 Rimricu Vicea 607 Craiov ... |