Means-Ends Analysis 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 4 Pages
File Size 111.9 KB
Views 4771
Downloads 0
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Description

Buy "Means-Ends Analysis in AI" now and learn how to solve complex and large problems with a mixture of forward and backward reasoning techniques. This comprehensive book introduces the concept of Means-Ends Analysis (MEA), a problem-solving strategy that limits search in AI programs by first solving major parts of a problem and then addressing smaller subproblems as they arise. With practical examples and detailed explanations, you'll explore how MEA works by evaluating differences between the current state and goal state, and applying operators to reduce these differences, making it accessible to both beginners and seasoned professionals. Dive into the fascinating world of Operator Subgoaling, where operators are selected, and subgoals are set up to establish the preconditions for solving a problem. The book covers the essential algorithm for MEA and provides real-world examples to illustrate its application in various AI-driven tasks. "Means-Ends Analysis in AI" is an essential read for anyone looking to deepen their understanding of AI's problem-solving techniques and apply these methods to tackle real-world challenges effectively.
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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.
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