A Star search algorithm with example (num)
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![Optimality of A* Search Algorithm
ï‚´ Heuristic may be:
ï‚´ h(n) > actual cost
ï‚´ h(n) = actual cost
ï‚´ h(n) < actual cost
ï‚´ (1) h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not
possible]
ï‚´ (2) h(n) = actual cost [Best case scenario, if h(n) approximates actual cost, searching
uses minimum of node to the goal]
ï‚´ (3) h(n) < actual cost [ Admissible, Consistent Heuristics]](https://image.slidesharecdn.com/astarexample-250209175738-f235cc9c/85/A-Star-search-algorithm-with-example-num-12-320.jpg)
![Optimality of A* Search Algorithm
h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not
possible]](https://image.slidesharecdn.com/astarexample-250209175738-f235cc9c/85/A-Star-search-algorithm-with-example-num-13-320.jpg)
![Optimality of A* Search Algorithm
h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not
possible]](https://image.slidesharecdn.com/astarexample-250209175738-f235cc9c/85/A-Star-search-algorithm-with-example-num-14-320.jpg)
![Optimality of A* Search Algorithm
h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not
possible]
b………….> dest and cost (7+2+0=9) , or ……….>a and cost (2+9=11)](https://image.slidesharecdn.com/astarexample-250209175738-f235cc9c/85/A-Star-search-algorithm-with-example-num-15-320.jpg)
![Optimality of A* Search Algorithm
h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not
possible]
b………….> dest and cost (7+2+0=9) , or ……….>a and cost (2+9=11)
Actual cost from Sïƒ aïƒ bïƒ dest =2+3+2=7
h(n)=10 > actual cost = 7](https://image.slidesharecdn.com/astarexample-250209175738-f235cc9c/85/A-Star-search-algorithm-with-example-num-16-320.jpg)
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This PowerPoint presentation provides a comprehensive overview of the Indian Soil Classification System, widely used in geotechnical engineering for identifying and categorizing soils based on their properties. It covers essential aspects such as particle size distribution, sieve analysis, and Atterberg consistency limits, which play a crucial role in determining soil behavior for construction and foundation design. The presentation explains the classification of soil based on particle size, including gravel, sand, silt, and clay, and details the sieve analysis experiment used to determine grain size distribution. Additionally, it explores the Atterberg consistency limits, such as the liquid limit, plastic limit, and shrinkage limit, along with a plasticity chart to assess soil plasticity and its impact on engineering applications. Furthermore, it discusses the Indian Standard Soil Classification (IS 1498:1970) and its significance in construction, along with a comparison to the Unified Soil Classification System (USCS). With detailed explanations, graphs, charts, and practical applications, this presentation serves as a valuable resource for students, civil engineers, and researchers in the field of geotechnical engineering. Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...J. Agricultural Machinery
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US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...
US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...Thane Heins NOBEL PRIZE WINNING ENERGY RESEARCHER
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A Star search algorithm with example (num)
- 1. A* Search Algorithm ï‚´ A* Search Algorithm solved with numeric example
- 2. A* Search Algorithm solved with numeric example
- 3. A* Search Algorithm solved with numeric example
- 4. A* Search Algorithm solved with numeric example
- 5. A* Search Algorithm solved with numeric example
- 6. A* Search Algorithm solved with numeric example
- 7. A* Search Algorithm solved with numeric example
- 8. A* Search Algorithm solved with numeric example
- 9. A* Search Algorithm solved with numeric example
- 10. Heuristic Function h(n) and how to make it
- 11. Heuristic Function h(n) and how to make it
- 12. Optimality of A* Search Algorithm ï‚´ Heuristic may be: ï‚´ h(n) > actual cost ï‚´ h(n) = actual cost ï‚´ h(n) < actual cost ï‚´ (1) h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not possible] ï‚´ (2) h(n) = actual cost [Best case scenario, if h(n) approximates actual cost, searching uses minimum of node to the goal] ï‚´ (3) h(n) < actual cost [ Admissible, Consistent Heuristics]
- 13. Optimality of A* Search Algorithm h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not possible]
- 14. Optimality of A* Search Algorithm h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not possible]
- 15. Optimality of A* Search Algorithm h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not possible] b………….> dest and cost (7+2+0=9) , or ……….>a and cost (2+9=11)
- 16. Optimality of A* Search Algorithm h(n) > actual cost [Optimum solution can be overlooked, optimum solution is not possible] b………….> dest and cost (7+2+0=9) , or ……….>a and cost (2+9=11) Actual cost from Sïƒ aïƒ bïƒ dest =2+3+2=7 h(n)=10 > actual cost = 7
- 17. Optimality of A* Search Algorithm ï‚´ h(n) < actual cost, if this relation maintains for every node, then we say this this Admissible Heuristics.
- 18. Optimality of A* Search Algorithm
- 19. Optimality of A* Search Algorithm
- 20. Optimality of A* Search Algorithm
- 21. Optimality of A* Search Algorithm
- 22. Optimality of A* Search Algorithm If Heuristic admissible but not consistent, it may not be provided optimum solution. For optimum solution, heuristic must be consistent. If it consistent, it must be admissible.
- 23. Optimality of A* Search Algorithm
- 24. Random Variable ï‚´ The domain