04-05-2012, 04:50 PM
Artificial Intelligence
AI.docx (Size: 239.39 KB / Downloads: 46)
INTRODUCTION
Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines."
The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of optimism, but has also suffered setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still among the field's long term goals.
What is Artificial Intelligence?
Quite simple human behaviour can be intelligent yet quite complex behaviour performed by insects is unintelligent. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumon us. When the female wasp brings food to her burrow, she deposits it on the threshold, goes inside the burrow to check for intruders, and then if the coast is clear carries in the food. The unintelligent nature of the wasp's behaviour is revealed if the watching experimenter moves the food a few inches while the wasp is inside the burrow checking. On emerging, the wasp repeats the whole procedure: she carries the food to the threshold once again, goes in to look around, and emerges. She can be made to repeat this cycle of behaviour upwards of forty times in succession. Intelligence-conspicuously absent in the case of Sphex- the ability to adapt one's behaviour to fit new circumstances.
Mainstream thinking in psychology regards human intelligence not as a single ability or cognitive process but rather as an array of separate components. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and language-understanding.
LEARNING
Learning is distinguished into a number of different forms. The simplest is learning by trial-and-error. For example, a simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate. The program remembers the successful move and next time the computer is given the same problem it is able to produce the answer immediately. The simple memorising of individual items--solutions to problems, words of vocabulary, etc. is known as rote learning.
Rote learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalisation. Learning that involves generalisation leaves the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of e.g. "jump" until presented at least once with "jumped", whereas a program that is able to generalise from examples can learn the "add-ed" rule, and so form the past tense of "jump" in the absence of any previous encounter with this verb. Sophisticated modern techniques enable programs to generalise complex rules from data.
REASONING
To reason is to draw inferences appropriate to the situation in hand. Inferences are classified as either deductive or inductive. An example of the former is "Fred is either in
the museum or the cafŽ; he isn't in the cafŽ; so he's in the museum", and of the latter "Previous accidents just like this one have been caused by instrument failure; so probably this one was caused by instrument failure". The difference between the two is that in the deductive case, the truth of the premisses guarantees the truth of the conclusion, whereas in the inductive case, the truth of the premiss lends support to the conclusion that the accident was caused by instrument failure, but nevertheless further investigation might reveal that, despite the truth of the premiss, the conclusion is in fact false.
AI.docx (Size: 239.39 KB / Downloads: 46)
INTRODUCTION
Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines."
The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of optimism, but has also suffered setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still among the field's long term goals.
What is Artificial Intelligence?
Quite simple human behaviour can be intelligent yet quite complex behaviour performed by insects is unintelligent. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumon us. When the female wasp brings food to her burrow, she deposits it on the threshold, goes inside the burrow to check for intruders, and then if the coast is clear carries in the food. The unintelligent nature of the wasp's behaviour is revealed if the watching experimenter moves the food a few inches while the wasp is inside the burrow checking. On emerging, the wasp repeats the whole procedure: she carries the food to the threshold once again, goes in to look around, and emerges. She can be made to repeat this cycle of behaviour upwards of forty times in succession. Intelligence-conspicuously absent in the case of Sphex- the ability to adapt one's behaviour to fit new circumstances.
Mainstream thinking in psychology regards human intelligence not as a single ability or cognitive process but rather as an array of separate components. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and language-understanding.
LEARNING
Learning is distinguished into a number of different forms. The simplest is learning by trial-and-error. For example, a simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate. The program remembers the successful move and next time the computer is given the same problem it is able to produce the answer immediately. The simple memorising of individual items--solutions to problems, words of vocabulary, etc. is known as rote learning.
Rote learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalisation. Learning that involves generalisation leaves the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of e.g. "jump" until presented at least once with "jumped", whereas a program that is able to generalise from examples can learn the "add-ed" rule, and so form the past tense of "jump" in the absence of any previous encounter with this verb. Sophisticated modern techniques enable programs to generalise complex rules from data.
REASONING
To reason is to draw inferences appropriate to the situation in hand. Inferences are classified as either deductive or inductive. An example of the former is "Fred is either in
the museum or the cafŽ; he isn't in the cafŽ; so he's in the museum", and of the latter "Previous accidents just like this one have been caused by instrument failure; so probably this one was caused by instrument failure". The difference between the two is that in the deductive case, the truth of the premisses guarantees the truth of the conclusion, whereas in the inductive case, the truth of the premiss lends support to the conclusion that the accident was caused by instrument failure, but nevertheless further investigation might reveal that, despite the truth of the premiss, the conclusion is in fact false.