By George F. Luger, William A Stubblefield
AI Algorithms, info buildings, and Idioms in Prolog, Lisp, and Java
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Extra resources for AI algorithms, data structures, and idioms in Prolog, Lisp, and Java
First and foremost, is its direct and transparent representation and interpretation of predicate calculus expressions. The predicate calculus has been an important representational scheme in AI from the beginning, used everywhere from automated reasoning to robotics research. A second contribution to AI is the ability to create meta-predicates or predicates that can constrain, manipulate, and interpret other predicates. This makes Prolog ideal for creating meta-interpreters or interpreters written in Prolog that can interpret subsets of Prolog code.
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.
Although Common Lisp has emerged as the lingua franca of Lisp dialects, a number of simpler dialects continue to be widely used. One of the most important of these is SCHEME, an elegant rethinking of the language that has been used both for AI development and for teaching the fundamental concepts of computer science. The dialect we use throughout the remainder of our book is Common Lisp. All our code may be run on a current public domain interpreter built by Carnegie Mellon University, called CMUCL (Google CMUCL).