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Chapter 1
Chapter 1

... must know an algorithm that tells the computer what to do. That is how to get the output from a given input. (Examples: C++, C#, Pascal, Algol, … etc.) Declarative languages: Declare the logic by which the program solves a problem (the logic of problem solving is declared in the program). The progra ...
Intelligent System for Information Security Management: Architecture
Intelligent System for Information Security Management: Architecture

... be integrated with traditional procedural and statistical methods to analyze the collected data by sensors, recognize reconnaissance patterns, filter and correlate events to support security event management and prevention of intrusions. These techniques improve the ability of security management sy ...
A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks
A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks

Agents and e
Agents and e

... • Main problems: 1. Whose agent are they? 2. What do they compare? ...
decision support
decision support

... Modified from Decision Support Systems and Business Intelligence Systems 9E. ...
1pp
1pp

... • The first, which you, as computer scientists, should be familiar with, is computational complexity. We can solve useful problems in polynomial time, but most interesting AI problems — certainly the ones we looked at — are NP-hard. We will be constantly straddling the boundary between polynomial ti ...
now
now

... Modified from Decision Support Systems and Business Intelligence Systems 9E. ...
Intelligent Agent in Education
Intelligent Agent in Education

... Teaching and Learning Environment on the Web, University of Girona, Spain. 2002. Russell, S.J. and P. Norvig. "Artificial intelligence: a modern approach." Prentice Hall series in artificial intelligence. Prentice Hall, N.J. 1995 Slater, D. (2000). Interactive Animated Pedagogical Agents: An introdu ...
Competence and Performance-Improving approach for maintaining
Competence and Performance-Improving approach for maintaining

... (CBM) becomes required. Recently, the CBM issue has drawn more and more attention to two major gauges that supply to the evaluation of a CB. The first one is the CB's performance [4, 9] which is the answer time that is needed to calculate a solution for case targets. The second one is the CB's compe ...
Chapter 12: Artificial Intelligence and Expert Systems Turban
Chapter 12: Artificial Intelligence and Expert Systems Turban

... Is a computer program that attempts to imitate expert’s reasoning processes and knowledge in solving specific problems Most Popular Applied AI Technology ...
DSS Chapter 1
DSS Chapter 1

... Is a computer program that attempts to imitate expert’s reasoning processes and knowledge in solving specific problems Most Popular Applied AI Technology ...
An Abstract View on Modularity in Knowledge Representation
An Abstract View on Modularity in Knowledge Representation

... base design, and (ii) reasoning where they exploit modular structure to improve the performance of solvers. To identify and study general principles of modularity in knowledge representation, researchers proposed several general frameworks such as: • Abstract multi-context systems (Brewka and Eiter ...
PDF
PDF

... In this talk, we introduce our robot learning framework which follows a similar timeline with human infant development. In the initial stages of the development, the robot organizes its action parameter space to form behavior primitives, and explore the environment with these primitives to learn bas ...
Chapter 1: Introduction to Expert Systems
Chapter 1: Introduction to Expert Systems

... • During the 20th Century various definitions of AI were proposed. • In the 1960s, a special type of AI called expert systems dealt with complex problems in a narrow domain, e.g., medical disease diagnosis. • Today, expert systems are used in a variety of fields. • Expert systems solve problems for ...
Simple Algorithmic Theory of Subjective Beauty, Novelty
Simple Algorithmic Theory of Subjective Beauty, Novelty

Integrating AI Techniques in Requirements Phase: A Literature Review
Integrating AI Techniques in Requirements Phase: A Literature Review

... different activities of the requirements phase of SDLC, but at the same time, there are certainly new research questions and issues too. One of the prominent issues is the maximum human intervention in the requirements phase due to being a conceptual phase of SDLC. Research studies reveal that Artif ...
Chapter 1: Introduction to Expert Systems
Chapter 1: Introduction to Expert Systems

... • During the 20th Century various definitions of AI were proposed. • In the 1960s, a special type of AI called expert systems dealt with complex problems in a narrow domain, e.g., medical disease diagnosis. • Today, expert systems are used in a variety of fields. • Expert systems solve problems for ...
Developing Backward Chaining Algorithm of Inference Engine in
Developing Backward Chaining Algorithm of Inference Engine in

... Some task that can be performed by expert system are difficult tasks to be specified, the task that may have incomplete or uncertain data, there may not always be an optimum solution, the task cannot be solved in a step-by-step manner, and solutions are often obtained by using accumulated experience ...
6pp - Stanford University
6pp - Stanford University

Determination, Uniformity, and Relevance: Normative
Determination, Uniformity, and Relevance: Normative

Strongly equivalent temporal logic programs
Strongly equivalent temporal logic programs

... or diagnostics. Default negation plays here a crucial role, as it allows representing the rule of inertia (that can be stated as “a fluent remains unchanged by default”) and avoid in this way the frame problem [11]. ASP can also be naturally used for solving other typical representational problems ...
A Planning Graph Heuristic for Forward-Chaining
A Planning Graph Heuristic for Forward-Chaining

... is not static. The exogenous dynamics can be caused by “nature” or by one or more other agents sharing the same environment. Other agents can behave neutrally (simply following their own independent agenda or otherwise acting unpredictably), adversarially, or cooperatively with respect to the protag ...
Note 1: introduction
Note 1: introduction

... • Analysis of propositional logic • Turing’s theory of computation ...
Job Shop Scheduling
Job Shop Scheduling

... • John gave up. • John’s legs gave out beneath him. • It is 300 miles, give or take 10. ...
The CHREST Architecture of Cognition The Role of
The CHREST Architecture of Cognition The Role of

... Eye Movements and the Perception-Learning Cycle The frame problem is a central issue in cognitive science and artificial intelligence: How can a system notice the relevant changes in the environment in real time whilst ignoring the indefinite number of changes that are irrelevant? CHREST’s solution ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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