Artificial Intelligence
Evolving Pushdown Automata.pdf
Taken from Introduction: Grammatical inference is the process of inducing constructs such as grammars and automata for a formal language from a subset of sentences belonging to the language, called positive sentences, and sentences not in the language, referred to as negative sentences ([7] and [9]). Research in this area was initiated by the study conducted by Fogel et al. [6] to evolve deterministic finite automata (DFAs) for regular languages. Since this first attempt at grammatical inferencing, research in this field has been steadily growing. Numerous studies have examined the performance of various evolutionary techniques, including genetic algorithms [5], genetic programming ([4], [1]), and gene regulation [11], as a means of evolving finite state machines.
The concept of grammatical inference has also been applied to the domain of context-free languages. Genetic algorithms ([7] and [9]) and genetic programming (GP) ([3] and [13]), have been investigated for the purpose of generating acceptors for contextfree languages. However, as can be seen from the references not much work has been done in this field since the early nineties.
The studies evaluating genetic programming in this domain have concentrated on deterministic machines only and these systems have not been widely tested.
Common LISP: A Gentle Introduction to Symbolic Computation.pdf
Prior to about 1984, the Lisps available on personal computers weren’t very good due to the small memories of the early machines. Today’s personal computers often come with several megabytes of RAM and a hard disk as standard equipment. They run full implementations of the Common Lisp standard, and provide the same high-quality tools as the Lisps in university and industrial research labs. The “Lisp Toolkit” sections of this book will introduce you to the advanced features of the Common Lisp programming environment that have made the language such a productive tool for rapid prototyping and AI programming.
Design of CMU Common Lisp.pdf
This document is a work in progress: neither the contents nor the presentation are completed. Nevertheless, it provides some useful background information, in particular regarding the CMUCL compiler
Artificial Intelligence Course Material.pdf
41 Lessons in 13 modules of Artificial Intelligence ebook, pictures and words work together to explain as concise as possible everything about Artificial Intelligence, you can find logic, fuzzy, agent, single agent search and more
Artificial Intelligence and Responsive Optimization.pdf
The purpose of this book is to apply the Artificial Intelligence and control systems to different real models. It has been designed for graduate students and researchers who are active in the applications of Artificial Intelligence and Control Systems in modeling.
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