Informed Search Algorithms in AI

Institution Jomo Kenyatta University of Science and Technology
Course Information Technolo...
Year 3rd Year
Semester Unknown
Posted By Jeff Odhiambo
File Type pdf
Pages 7 Pages
File Size 139.29 KB
Views 3046
Downloads 0
Price: Buy Now whatsapp Buy via whatsapp
  • whatsapp
  • facebook
  • twitter

Description

Buy "Informed Search Algorithms in AI" now and learn how advanced search techniques can efficiently navigate large and complex search spaces. This comprehensive book explores informed search algorithms, such as Best-First Search and A* Search, which utilize heuristic functions to guide the search process. Through clear explanations and practical examples, you'll gain insights into how these algorithms use knowledge like path cost and proximity to the goal to find solutions more effectively. This makes the book an invaluable resource for both beginners and seasoned professionals in the field of artificial intelligence. Discover the power of heuristic functions in optimizing search strategies and minimizing exploration. The book covers essential concepts such as admissibility, heuristic cost, and evaluation functions, providing a solid foundation for understanding the mechanisms behind informed search algorithms. With detailed discussions on the advantages and limitations of these techniques, "Informed Search Algorithms in AI" equips you with the knowledge to apply these methods to solve real-world problems, ensuring you stay ahead in the ever-evolving field of AI.
Below is the document preview.

No preview available
SMA 2371: Partial Differential Equations (Complete Notes, Examples & Solutions) – JKUAT Biostatistics Year 2 Semester 2
These are comprehensive and well-organized lecture notes for SMA 2371: Partial Differential Equations (PDE) offered to BSc. Biostatistics Year 2 Semester 2 students at JKUAT. The notes are neatly arranged from lecture one to the final topics, making them ideal for class learning, revision, CAT preparation, and final examinations. The notes include detailed explanations, worked examples, step-by-step mathematical derivations, solved exercises, and applications of Partial Differential Equations. Topics covered include: • Review of basic concepts and partial derivatives • Jacobians, surfaces and curves in three dimensions • Simultaneous first-order differential equations • Methods of solving symmetric differential equations • Orthogonal trajectories • Pfaffian differential equations • Linear first-order partial differential equations • Formation of PDEs • Elimination of arbitrary constants and arbitrary functions • Heat, Wave, Laplace and Poisson equations • Separation of variables • Fourier and Laplace Transform methods • Numerous worked examples, tutorial questions and examination-style problems with solutions. These notes are suitable for JKUAT students and other university students studying Mathematics, Statistics, Biostatistics, Engineering, Applied Mathematics or related courses. They are an excellent revision resource for CATs and final examinations.
57 Pages 1335 Views 0 Downloads 1.77 MB