19-08-2011, 10:53 AM
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Abstract:
The term artificial intelligence is used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. Constructing robots that perform intelligent tasks has always been a highly motivating factor for the science and technology of information processing. Unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities such as robots as well as understand them. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization Neural Networks have been proposed as an alternative to Symbolic Artificial Intelligence in constructing intelligent systems. They are motivated by computation in the brain. Small Threshold computing elements when put together produce powerful information processing machines. In this paper, we put forth the foundational ideas in artificial intelligence and important concepts in Search Techniques, Knowledge Representation, Language Understanding, Machine Learning, Neural Computing and such other disciplines.
Artificial Intelligence
Starting from a modest but an over ambitious effort in the late 50’s, AI has grown through its share of joys, disappointments and self-realizations. AI deals in science, which deals with creation of machines, which can think like humans and behave rationally. AI has a goal to automate every machine.
AI is a very vast field, which spans:
• Many application domains like Language Processing, Image Processing, Resource Scheduling, Prediction, Diagnosis etc.
• Many types of technologies like Heuristic Search, Neural Networks, and Fuzzy Logic etc.
• Perspectives like solving complex problems and understanding human cognitive processes.
• Disciplines like Computer Science, Statistics, Psychology, etc.
Requirement of an Artificial Intelligence system
No AI system can be called intelligent unless it learns & reasons like a human. Reasoning derives new information from given ones.
Areas of Artificial Intelligence
Knowledge Representation
Importance of knowledge representation was realized during machine translation effort in early 1950’s. Dictionary look up and word replacement was a tedious job. There was ambiguity and ellipsis problem i.e. many words have different meanings. Therefore having a dictionary used for translation was not enough.
One of the major challenges in this field is that a word can have more than one meaning and this can result in ambiguity.
E.g.: Consider the following sentence
Spirit is strong but flesh is weak.
When an AI system was made to convert this sentence into Russian & then back to English, following output was observed.
Wine is strong but meat is rotten.
Thus we come across two main obstacles. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem “in principle” and doing so in practice.
Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first.
A problem may or may not have a solution. This is why debugging is one of the most challenging jobs faced by programmers today. As the rule goes, it is impossible to create a program which can predict whether a given program is going to terminate ultimately or not.
Development in this part was that algorithms were written using foundational development of vocabulary and dictionary entries. Limitations of the algorithm were found out. Later Formal Systems were developed which contained axioms, rules, theorems and an orderly form of representation was developed.
For example, Chess is a formal system. We use rules in our everyday lives and these rules accompany facts. Rules are used to construct an efficient expert system having artificial intelligence. Important components of a Formal System are - Backward Chaining i.e. trying to figure out the content by reading the sentence backward and page link each word to another, Explanation Generation i.e. generating an explanation of whatever the system has understood, Inference Engine i.e. submitting an inference or replying to the problem.
Reasoning
It is to use the stored information to answer questions and to draw new conclusions. Reasoning means, drawing of conclusion from observations.
Reasoning in AI systems work on three principles namely:
DEDUCTION: Given 2 events ‘P’ & ‘Q’, if ‘P’ is true then ‘Q’ is also true.
E.g.: If it rains, we can’t go for a picnic.
INDUCTION: Induction is a process where in , after studying certain facts , we reach to a conclusion.
E.g.: Socrates is a man; all men are mortal; therefore Socrates is mortal.
ABDUCTION: ‘P’ implies ‘Q’, but ‘Q’ may not always depend on ‘P’.
E.g.: If it rains , we can’t go for a picnic.
The fact that we are not in a position to go for a picnic does not mean that it is training. There can be other reasons as well.
Learning
The most important requirement for an AI system is that it should learn from its mistakes. The best way of teaching an AI system is by training & testing. Training involves teaching of basic principles involved in doing a job. Testing process is the real test of the knowledge acquired by the system wherein we give certain examples & test the intelligence of the system. Examples can be positive or negative. Negative examples are those which are ‘near miss’ of the positive examples.
Natural Language Processing (NLP)
NLP can be defined as:
• Processing of data in the form of natural language on the computer. I.e. making the computer understand the language a normal human being speaks.
• It deals with under structured / semi structured data formats and converting them into complete understandable data form. The reasons to process natural language are; Generally - because it is exciting and interesting, Commercially – because of sheer volume of data available online, Technically – because it eases out Computer-Human interaction.
NLP helps us in
• Searching for information in a vast NL (natural language) database.
• Analysis i.e. extracting structural data from natural language.
• Generation of structured data.
• Translation of text from one natural language to other. Example: English to Hindi.
Application Spectrum of NLP
• It provides writing and translational aids.
• Helps humans to generate Natural Language with proper spelling, grammar, style etc.
• It allows text mining i.e. information retrieval, search engines text categorization, information extraction.
• NL interface to database, web software system, and question answer explanation in an expert system.
Abstract:
The term artificial intelligence is used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. Constructing robots that perform intelligent tasks has always been a highly motivating factor for the science and technology of information processing. Unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities such as robots as well as understand them. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization Neural Networks have been proposed as an alternative to Symbolic Artificial Intelligence in constructing intelligent systems. They are motivated by computation in the brain. Small Threshold computing elements when put together produce powerful information processing machines. In this paper, we put forth the foundational ideas in artificial intelligence and important concepts in Search Techniques, Knowledge Representation, Language Understanding, Machine Learning, Neural Computing and such other disciplines.
Artificial Intelligence
Starting from a modest but an over ambitious effort in the late 50’s, AI has grown through its share of joys, disappointments and self-realizations. AI deals in science, which deals with creation of machines, which can think like humans and behave rationally. AI has a goal to automate every machine.
AI is a very vast field, which spans:
• Many application domains like Language Processing, Image Processing, Resource Scheduling, Prediction, Diagnosis etc.
• Many types of technologies like Heuristic Search, Neural Networks, and Fuzzy Logic etc.
• Perspectives like solving complex problems and understanding human cognitive processes.
• Disciplines like Computer Science, Statistics, Psychology, etc.
Requirement of an Artificial Intelligence system
No AI system can be called intelligent unless it learns & reasons like a human. Reasoning derives new information from given ones.
Areas of Artificial Intelligence
Knowledge Representation
Importance of knowledge representation was realized during machine translation effort in early 1950’s. Dictionary look up and word replacement was a tedious job. There was ambiguity and ellipsis problem i.e. many words have different meanings. Therefore having a dictionary used for translation was not enough.
One of the major challenges in this field is that a word can have more than one meaning and this can result in ambiguity.
E.g.: Consider the following sentence
Spirit is strong but flesh is weak.
When an AI system was made to convert this sentence into Russian & then back to English, following output was observed.
Wine is strong but meat is rotten.
Thus we come across two main obstacles. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem “in principle” and doing so in practice.
Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first.
A problem may or may not have a solution. This is why debugging is one of the most challenging jobs faced by programmers today. As the rule goes, it is impossible to create a program which can predict whether a given program is going to terminate ultimately or not.
Development in this part was that algorithms were written using foundational development of vocabulary and dictionary entries. Limitations of the algorithm were found out. Later Formal Systems were developed which contained axioms, rules, theorems and an orderly form of representation was developed.
For example, Chess is a formal system. We use rules in our everyday lives and these rules accompany facts. Rules are used to construct an efficient expert system having artificial intelligence. Important components of a Formal System are - Backward Chaining i.e. trying to figure out the content by reading the sentence backward and page link each word to another, Explanation Generation i.e. generating an explanation of whatever the system has understood, Inference Engine i.e. submitting an inference or replying to the problem.
Reasoning
It is to use the stored information to answer questions and to draw new conclusions. Reasoning means, drawing of conclusion from observations.
Reasoning in AI systems work on three principles namely:
DEDUCTION: Given 2 events ‘P’ & ‘Q’, if ‘P’ is true then ‘Q’ is also true.
E.g.: If it rains, we can’t go for a picnic.
INDUCTION: Induction is a process where in , after studying certain facts , we reach to a conclusion.
E.g.: Socrates is a man; all men are mortal; therefore Socrates is mortal.
ABDUCTION: ‘P’ implies ‘Q’, but ‘Q’ may not always depend on ‘P’.
E.g.: If it rains , we can’t go for a picnic.
The fact that we are not in a position to go for a picnic does not mean that it is training. There can be other reasons as well.
Learning
The most important requirement for an AI system is that it should learn from its mistakes. The best way of teaching an AI system is by training & testing. Training involves teaching of basic principles involved in doing a job. Testing process is the real test of the knowledge acquired by the system wherein we give certain examples & test the intelligence of the system. Examples can be positive or negative. Negative examples are those which are ‘near miss’ of the positive examples.
Natural Language Processing (NLP)
NLP can be defined as:
• Processing of data in the form of natural language on the computer. I.e. making the computer understand the language a normal human being speaks.
• It deals with under structured / semi structured data formats and converting them into complete understandable data form. The reasons to process natural language are; Generally - because it is exciting and interesting, Commercially – because of sheer volume of data available online, Technically – because it eases out Computer-Human interaction.
NLP helps us in
• Searching for information in a vast NL (natural language) database.
• Analysis i.e. extracting structural data from natural language.
• Generation of structured data.
• Translation of text from one natural language to other. Example: English to Hindi.
Application Spectrum of NLP
• It provides writing and translational aids.
• Helps humans to generate Natural Language with proper spelling, grammar, style etc.
• It allows text mining i.e. information retrieval, search engines text categorization, information extraction.
• NL interface to database, web software system, and question answer explanation in an expert system.
