Posted by Ehsan r | Filed under ArTiFiCiAl InTeLlIgEnCe, DaTa MiNiNg, e-BoOkS, EsSaYs, ExPeRt SyStEm, LINKS, NeWs, PrOjEcTs, PrOlOg
All my activities transfered in :
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January 30th, 2010
Posted by Ehsan r | Filed under ExPeRt SyStEm, PrOjEcTs
Dear my friends…
One year ago, I designed an expert system in spring & summer 2009. It was offered by my master (Dr.Montazeri). It is a virtual teacher. I designed it for the course of expert systems.
Explanations:
This program is written by C# language. It learns C language and uses from Access Data Base. In this program, you have one account that your information is registered in it.
After your entering, the program dispatches your information from data base and starts learning. In teaching, you can stop or continue your learning and ask from the virtual teacher some questions about the same step (lesson).
For understanding more run it…
This program can be a start of designing virtual teachers. I tried to product this program usefully and application oriented.
At the end, I thank my dear master (Dr.Montazeri) that he helped me with Artificial Intelligence & Expert Systems.
I uploaded this program for you. If you are eager to upgrade and see its codes (In C#) please contact me to sending for you …
Download (EXE file)
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January 6th, 2010
Posted by Ehsan r | Filed under e-BoOkS
Download link
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January 3rd, 2010
Posted by Ehsan r | Filed under e-BoOkS
Programming Game AI by Example
Download From myopera
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January 3rd, 2010
Posted by Ehsan r | Filed under DaTa MiNiNg
Simply stated,data mining refers to extracting or “mining” knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, “data mining” should have been more appropriately named “knowledge mining from data”, which is unfortunately somewhat long. “Knowledge mining”, a shorter term, may not reect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that nds a smallset of precious nuggets from a great deal of raw material (Figure 1.3). Thus, such a misnomer which carries both”data” and “mining” became a popular choice. There are many other terms carrying a similar or slightly dierent meaning to data mining, such as knowledge mining from databases, knowledge extraction, data/pattern analysis, data archaeology, and data dredging .
Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases “, or KDD . Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery as a process is depicted in Figure 1.4, and consists of an iterative sequence of the following steps:
data cleaning (to remove noise or irrelevant data),
data integration (where multiple data sources may be combined)
data selection (where data relevant to the analysis task are retrieved from the database),
data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)
data mining(an essential process where intelligent methods are applied in order to extract data patterns),
pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures; Section 1.5),
and
knowledge presentation (where visualization and knowledge representation techniques are used to present
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December 30th, 2009
Posted by Ehsan r | Filed under e-BoOkS
Data Mining: Concepts and Techniques
ISBN: 1558604898 | PDF | 2 Mb
Here’s the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.
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December 30th, 2009
Posted by Ehsan r | Filed under ArTiFiCiAl InTeLlIgEnCe
SYMBOL-SYSTEM AI
Consider the following question: how is it possible to learn the meaning of a public language concept? One obvious answer is “by definition.” Someone tells you the meaning, using words that you already understand. But such a model obviously cannot be invoked to explain our initial acquisition of concepts in the context of first language learning. How, then, is this to be explained?
Fodor (1975) argues that we acquire such knowledge by a process of hypothesis generation and test conducted using the resources of an innate representational base. The idea is that the child is genetically programmed to develop an initial repertoire of internally represented predicates.
The process of learning the meaning of a natural language term is then cast as a process of formulating hypotheses about possible meanings, using the innate expressive resources of the inner code (the infamous “language of thought”) and then testing those hypotheses against observed public usage.
The most that concept learning can therefore do is to promote the development of inner abbreviations, which can ultimately replace the complex defining expressions in the language of thought (see Fodor, 1975, p. 152).
What concept learning cannot do is to expand the expressive pov^er of any innate representational system. The innate system provides both a base for learning and (hence) a limit on what can be learned.
Fodor’s vision is thus symbol assuming in a very strong sense. But it is not just symbol assuming. It is also symbol-system assuming, and this feature has lately (see, e.g., Fodor, 1987; Fodor & Pylyshyn, 1988) come to dominate discussions concerning the differences between so-called classical
artificial intelligence and its rivals (see later). In essence, a symbol system comprises both a set of atomic symbols and a special kind of processing environment in which they are embedded. The relevant kind of processing environment is modeled on our understanding of formal logical systems and artificial grammars (for a nice critical discussion, see Oaksford & Chater,1991). What is important about such an environment is its provision of a fixed combinatorial framework for the embedded symbols. Such a framework is often described (a little opaquely, I feel) as quasi-linguistic. But, all this means is that 1. Atomic symbols can combine in a predetermined variety of ways.
2. The contents of the symbol strings resulting from such recombinations are systematically determined by the contents of the participating atomic symbols and the mode of combination.
Given these properties, some nice features foUow^. Strings of inner symbols will then exhibit structure that can be exploited by computational operations.
For example, it will be possible to define an operation to apply to all and only strings involving some particular atomic symbol or sequence of symbols or to apply to all and only those strings exhibiting a given combinatorial form (e.g., conjunction) and so on. As a result, it is easy to see how to implement rational (e.g., truth theoretic) processes of reasoning by allowing only certain kinds of symbol string to be created in response to other kinds (think here of the premises and conclusions in logical arguments).
The key property of what have become known as classical cognitive models (see,e.g., Clark, 1989, Chap. 1) is that, courtesy of their reliance on symbol systems, it is possible within them to define semantically well-constrained mental operations in very neat ways. As Fodor and Pylyshyn put it:
In classical models, the principles by which mental states are transformed, or by which an input selects the corresponding output, are defined over structural properties of mental representations. Because classical mental representations have combinatorial structure, it is possible for classical mental operations to apply to them by reference to their form. (1988, pp. 12-13)
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December 29th, 2009
Posted by Ehsan r | Filed under e-BoOkS
Markov Logic: An Interface Layer for Artificial Intelligence
Pedro Domingos, Daniel Lowd, “Markov Logic: An Interface Layer for Artificial Intelligence”
Morgan & Claypool | 2009 | ISBN: 1598296922 | 100 pages | PDF | 1,1 MB
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December 29th, 2009
Posted by Ehsan r | Filed under e-BoOkS
Ant Colony Optimization
Marco Dorigo, Thomas Stützle, “Ant Colony Optimization (Bradford Books)”
The MIT Press | 2004 | ISBN: 0262042193 | 319 pages | PDF | 1,9 MB
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The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.
The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
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December 29th, 2009
Posted by Ehsan r | Filed under e-BoOkS
The Fuzzy Systems Handbook
A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems
Earl Cox
Academic Press | 1994 | ISBN: 0121942708 | 512 pages | PDF | 6,7 MB
A comprehensive introduction to fuzzy logic, this book leads the reader through the complete process of designing, constructing, implementing, verifying and maintaining a platform-independent fuzzy system model. It is written in a tutorial style that assumes no background in fuzzy logic on the reader’s part. The enclosed disk contains all of the book’s examples in C++ code.
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December 29th, 2009
