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History of Knowledge Representation

September 4th, 2010 No comments

In computer science, particularly artificial intelligence, a number of representations have been devised to structure information.

KR is most commonly used to refer to representations intended for processing by modern computers, and in particular, for representations consisting of explicit objects (the class of all elephants, or Clyde a certain individual), and of assertions or claims about them (‘Clyde is an elephant’, or ‘all elephants are grey’). Representing knowledge in such explicit form enables computers to draw conclusions from knowledge already stored (‘Clyde is grey’).

Many KR methods were tried in the 1970s and early 1980s, such as heuristic question-answering, neural networks, theorem proving, and expert systems, with varying success. Medical diagnosis (e.g., Mycin) was a major application area, as were games such as chess.

In the 1980s, formal computer knowledge representation languages and systems arose. Major projects attempted to encode wide bodies of general knowledge; for example the “Cyc” project (still ongoing) went through a large encyclopedia, encoding not the information itself, but the information a reader would need in order to understand the encyclopedia: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects.

Through such work, the difficulty of KR came to be better appreciated. In computational linguistics, meanwhile, much larger databases of language information were being built, and these, along with great increases in computer speed and capacity, made deeper KR more feasible.

Several programming languages have been developed that are oriented to KR. Prolog developed in 1972, but popularized much later, represents propositions and basic logic, and can derive conclusions from known premises. KL-ONE (1980s) is more specifically aimed at knowledge representation itself. In 1995, the Dublin Core standard of metadata was conceived.

In the electronic document world, languages were being developed to represent the structure of documents, such as SGML (from which HTML descended) and later XML. These facilitated information retrieval and data mining efforts, which have in recent years begun to relate to knowledge representation.

Application Areas

September 4th, 2010 No comments
Agriculture, Natural Resource Management and the Environment, Architecture & Design

Art

Artificial Noses … and Taste

Astronomy & Space Exploration

Assistive Technologies

Automatic Programming

Autonomous Vehicles, Robots, Rovers, Explorers

Marketing, Customer Relations/Service & E-Commerce, Medicine

Military

Music

Networks – including Maintenance, Security & Intrusion Detection

Petroleum Industry

Politics & Foreign Relations

Public Health & Welfare

Scientific Discovery

Banking, Finance & Investing, Bioinformatics

Business & Manufacturing

Drama, Fiction, Poetry, Storytelling & Machine Writing

Earth & Atmospheric Sciences

Engineering

Filtering

Fraud Detection & Prevention

Agents, Expert Systems

Games & Puzzles

Machine Learning

Natural Language Processing

Robots

Vision

Hazards & Disasters, Information Retrieval & Extraction

Intelligent Tutoring Systems

Knowledge Management

Law

Law Enforcement & Public Safety

Libraries

Machine Translation

Smart Rooms, Smart Houses and Household Appliances, Social Science

Sports

Telecommunications

Transportation & Shipping

Video Games, Toys. Robotic Pets & Entertainment

Artificial Intelligence in the form of expert systems and neural networks have applications in every field of human endeavor. They combine precision and computational power with pure logic, to solve problems and reduce error in operation. Already, robot expert systems are taking over many jobs in industries that are dangerous for or beyond human ability. Some of the applications divided by domains are as follows:

Heavy Industries and Space

Robotics and cybernetics have taken a leap combined with artificially intelligent expert systems. An entire manufacturing process is now totally automated, controlled and maintained by a computer system in car manufacture, machine tool production, computer chip production and almost every high-tech process. They carry out dangerous tasks like handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned spacecraft sent in space. Japan is the leading country in the world in terms of robotics research and use.

Finance

Banks use intelligent software applications to screen and analyze financial data. Software that can predict trends in the stock market have created which have known to beat humans in predictive power. Credit card providers, telephone companies, mortgage lenders, banks, and the U.S. Government employs AI systems to detect fraud and expedite financial transactions, with daily transaction volumes in the billions. These systems first use-learning algorithms to construct profiles of customer usage patterns, and then use the resulting profiles to detect unusual patterns and take the appropriate action (e.g., disable the credit card). Such automated oversight of financial transactions is an important component in achieving a viable basis for electronic commerce.

Computer Science

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

Aviation

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

Weather Forecast

Neural networks are used for predicting weather conditions. Previous data is fed to a neural network, which learns the pattern and uses that knowledge to predict weather patterns.

Swarm Intelligence

This is an approach to, as well as application of artificial intelligence similar to a neural network. Here, programmers study how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm or collection from simple rules at an individual level. They develop intelligent systems by creating agent programs that mimic the behavior of these natural systems… etc.