George F. Luger, William A Stubblefield's AI algorithms, data structures, and idioms in Prolog, Lisp, PDF

By George F. Luger, William A Stubblefield

ISBN-10: 0136070477

ISBN-13: 9780136070474

AI Algorithms, facts buildings, and Idioms in Prolog, Lisp, and Java

Rarely used booklet, in good shape.

Show description

Read Online or Download AI algorithms, data structures, and idioms in Prolog, Lisp, and Java PDF

Best algorithms books

How to Solve It: Modern Heuristics (2nd Edition) - download pdf or read online

Uploader's observe: Ripped from SpringerLink.

Amazon hyperlink: http://www. amazon. com/How-Solve-It-Modern-Heuristics/dp/3540224947

This booklet is the one resource that offers finished, present, and proper info on challenge fixing utilizing glossy heuristics. It covers vintage tools of optimization, together with dynamic programming, the simplex process, and gradient concepts, in addition to fresh thoughts comparable to simulated annealing, tabu seek, and evolutionary computation. built-in into the discourse is a chain of difficulties and puzzles to problem the reader. The e-book is written in a full of life, enticing type and is meant for college kids and practitioners alike. an individual who reads and is aware the cloth within the e-book should be armed with the main robust challenge fixing instruments at the moment known.

This moment variation comprises new chapters, one on coevolutionary structures and one on multicriterial decision-making. additionally a few new puzzles are extra and numerous subchapters are revised.

Get Geometric approximation algorithms PDF

Distinctive algorithms for facing geometric items are complex, demanding to enforce in perform, and gradual. over the past twenty years a idea of geometric approximation algorithms has emerged. those algorithms are typically easy, speedy, and extra strong than their specified opposite numbers. This e-book is the 1st to hide geometric approximation algorithms intimately.

New PDF release: Dynamic Reconfiguration Architectures and Algorithms

Dynamic Reconfiguration: Architectures and Algorithms bargains a entire therapy of dynamically reconfigurable desktop architectures and algorithms for them. The assurance is large ranging from primary algorithmic ideas, ranging throughout algorithms for a big selection of difficulties and purposes, to simulations among versions.

Additional resources for AI algorithms, data structures, and idioms in Prolog, Lisp, and Java

Example text

Robinson designed a proof procedure called resolution, which is the primary method for computing with Prolog. For a more complete description of resolution refutation systems and of Prolog as Horn clause refutation, see Luger (2009, Chapter 14). Because of these features, Prolog has proved to be a useful vehicle for investigating such experimental programming issues as automatic code generation, program verification, and design of high-level specification languages. As noted above, Prolog and other logic-based languages support a declarative programming style—that is, constructing a program in terms of high-level descriptions of a problem’s constraints—rather than a procedural programming style—writing programs as a sequence of instructions for performing an algorithm.

Hasprop indicates that Object has Property with Value. Object and Value are nodes in the network, and Property is the name of the link that joins them. 1 is: isa(canary, bird). hasprop(tweety, color, white) isa(robin, bird). hasprop(robin, color, red). isa(ostrich, bird). hasprop(canary, color, yellow). isa(penguin, bird). hasprop(penguin, color, brown). isa(bird, animal). hasprop(bird, travel, fly). isa(fish, animal). hasprop(ostrich, travel, walk). isa(opus, penguin). hasprop(fish, travel, swim).

Not that this pattern will match no matter what X is bound to: an atom, a list, whatever! If the two are not identical, then it is natural to check whether X is an element of the rest (T) of the list. This is defined by: member(X, [Y | T]) :- member(X, T). The two lines of Prolog for checking list membership are then: member(X, [X | T]). member(X, [Y | T]) :- member(X, T). This example illustrates the importance of Prolog’s built-in order of search with the terminating condition placed before the recursive call, that is, to be tested before the algorithm recurs.

Download PDF sample

AI algorithms, data structures, and idioms in Prolog, Lisp, and Java by George F. Luger, William A Stubblefield

by Robert

Rated 4.52 of 5 – based on 44 votes