The document provides an overview of expert systems and artificial intelligence. It defines key concepts such as artificial intelligence, expert systems, knowledge bases, inference engines, and knowledge representation. It also discusses applications of expert systems, the development process, benefits, limitations, and other areas of applied artificial intelligence like natural language processing, robotics, computer vision, and machine learning. Neural networks are also introduced as computing systems that mimic the human brain.
The document summarizes the digestion and absorption of carbohydrates. It discusses that carbohydrates are digested by enzymes in the mouth, stomach, and small intestine into monosaccharides like glucose, fructose, and galactose which are then absorbed. Glucose absorption occurs via active transport using sodium-glucose transporters, while fructose absorption is via facilitated diffusion. Factors like hormones, vitamins, and genetic disorders can influence the rate of absorption. Lactose intolerance occurs when there is a deficiency of the enzyme lactase, preventing the digestion of lactose in milk.
This document discusses operator overloading in C++. It defines operator overloading as allowing operators to perform special operations on user-defined types. As an example, the + operator can be overloaded to perform string concatenation in addition to its default behavior of numeric addition. Operator overloading is implemented by defining operator functions that take the class type as their first parameter. The document provides an example of overloading the unary - operator to change the sign of data members in a class.
1. Blood glucose levels are normally maintained within a narrow range through the rates of glucose entering and leaving the bloodstream.
2. When blood glucose levels drop, glucagon is secreted to stimulate glucose production and release from the liver through glycogenolysis and gluconeogenesis.
3. If levels continue to drop, epinephrine is released to further increase glucose production from the liver while also breaking down glycogen in muscle and fat cells.
4. Prolonged low blood glucose can trigger the release of cortisol and growth hormone to mobilize more long-term energy stores and decrease glucose utilization in tissues.
Lipids are hydrophobic molecules that include fatty acids, triglycerides, phospholipids, sphingolipids, and sterols. They serve important structural and metabolic roles in the body. Fatty acids are used for energy storage and signaling molecules. Triglycerides store fatty acids in adipose tissue. Phospholipids and sphingolipids are major components of cell membranes. Sterols like cholesterol are important membrane components and steroid hormones modulate physiological activity.
This document discusses lipids (fats) including their composition, classification, functions, sources, and deficiencies. Lipids are comprised of carbon, hydrogen, and oxygen and provide 9 calories per gram. They are classified as simple lipids, compound lipids, waxes, and derived lipids. The main lipids in diet are triglycerides, phospholipids, and sterols. Triglycerides make up 95% of dietary and body lipids and are broken down into glycerol and fatty acids. Phospholipids are important structures in membranes. Sterols are precursors to bile acids and sex hormones. Fats have many important functions like being an energy source and aiding vitamin absorption. Deficiencies can cause skin disorders like
This PPT contains topic on Serotonin pathway, degradations of Serotonin, functions of Serotonin, Melatonin pathway and functions of Melatonin.
Book referred: https://www.amazon.in/Biochemistry-2019-Satyanarayana-Satyanarayana-Author/dp/B07WGHCTKZ/ref=sr_1_1?dchild=1&qid=1592209115&refinements=p_27%3AU+Satyanarayana&s=books&sr=1-1
Operators-computer programming and utilzationKaushal Patel
The document discusses various types of operators in the C programming language. It describes arithmetic operators like addition, subtraction, multiplication and division. It also covers assignment operators, logical operators, increment and decrement operators, bitwise operators, and other special operators. Examples are provided to demonstrate how each operator works, including their precedence order when used together in expressions. The key operators and their uses in C programming are summarized concisely.
The document discusses different types of streams in Java including file, byte, character, and standard streams. File streams allow reading and writing of files and include classes like FileInputStream and FileOutputStream for bytes and FileReader and FileWriter for characters. Byte streams handle 8-bit bytes while character streams handle 16-bit Unicode. Standard streams in Java are System.in for input, System.out for standard output, and System.err for errors. Sample code is provided to write to and read from files.
The document summarizes the digestion and absorption of carbohydrates. It discusses that carbohydrates are digested by enzymes in the mouth, stomach, and small intestine into monosaccharides like glucose, fructose, and galactose which are then absorbed. Glucose absorption occurs via active transport using sodium-glucose transporters, while fructose absorption is via facilitated diffusion. Factors like hormones, vitamins, and genetic disorders can influence the rate of absorption. Lactose intolerance occurs when there is a deficiency of the enzyme lactase, preventing the digestion of lactose in milk.
This document discusses operator overloading in C++. It defines operator overloading as allowing operators to perform special operations on user-defined types. As an example, the + operator can be overloaded to perform string concatenation in addition to its default behavior of numeric addition. Operator overloading is implemented by defining operator functions that take the class type as their first parameter. The document provides an example of overloading the unary - operator to change the sign of data members in a class.
1. Blood glucose levels are normally maintained within a narrow range through the rates of glucose entering and leaving the bloodstream.
2. When blood glucose levels drop, glucagon is secreted to stimulate glucose production and release from the liver through glycogenolysis and gluconeogenesis.
3. If levels continue to drop, epinephrine is released to further increase glucose production from the liver while also breaking down glycogen in muscle and fat cells.
4. Prolonged low blood glucose can trigger the release of cortisol and growth hormone to mobilize more long-term energy stores and decrease glucose utilization in tissues.
Lipids are hydrophobic molecules that include fatty acids, triglycerides, phospholipids, sphingolipids, and sterols. They serve important structural and metabolic roles in the body. Fatty acids are used for energy storage and signaling molecules. Triglycerides store fatty acids in adipose tissue. Phospholipids and sphingolipids are major components of cell membranes. Sterols like cholesterol are important membrane components and steroid hormones modulate physiological activity.
This document discusses lipids (fats) including their composition, classification, functions, sources, and deficiencies. Lipids are comprised of carbon, hydrogen, and oxygen and provide 9 calories per gram. They are classified as simple lipids, compound lipids, waxes, and derived lipids. The main lipids in diet are triglycerides, phospholipids, and sterols. Triglycerides make up 95% of dietary and body lipids and are broken down into glycerol and fatty acids. Phospholipids are important structures in membranes. Sterols are precursors to bile acids and sex hormones. Fats have many important functions like being an energy source and aiding vitamin absorption. Deficiencies can cause skin disorders like
This PPT contains topic on Serotonin pathway, degradations of Serotonin, functions of Serotonin, Melatonin pathway and functions of Melatonin.
Book referred: https://www.amazon.in/Biochemistry-2019-Satyanarayana-Satyanarayana-Author/dp/B07WGHCTKZ/ref=sr_1_1?dchild=1&qid=1592209115&refinements=p_27%3AU+Satyanarayana&s=books&sr=1-1
Operators-computer programming and utilzationKaushal Patel
The document discusses various types of operators in the C programming language. It describes arithmetic operators like addition, subtraction, multiplication and division. It also covers assignment operators, logical operators, increment and decrement operators, bitwise operators, and other special operators. Examples are provided to demonstrate how each operator works, including their precedence order when used together in expressions. The key operators and their uses in C programming are summarized concisely.
The document discusses different types of streams in Java including file, byte, character, and standard streams. File streams allow reading and writing of files and include classes like FileInputStream and FileOutputStream for bytes and FileReader and FileWriter for characters. Byte streams handle 8-bit bytes while character streams handle 16-bit Unicode. Standard streams in Java are System.in for input, System.out for standard output, and System.err for errors. Sample code is provided to write to and read from files.
An expert system is a knowledge-based information system that uses knowledge from a specific domain to provide information to users like a human expert. Expert systems are useful when human experts are unavailable, inconsistent, or unable to clearly explain decisions. They can be applied when a problem lacks a clear algorithmic solution, is hazardous, has a scarcity of human experts, or requires standardization. Some examples of early expert systems include LITHIAN which advised archaeologists and DENDRAL which identified chemical structures. Expert systems have advantages like enhanced decision quality, reduced consulting costs, and ability to solve complex problems, but developing and maintaining them can be difficult and expensive.
An expert system is a computer program that contains knowledge about a specific domain that allows it to solve problems or provide advice like a human expert. Expert systems are made up of a knowledge base, inference engine, and user interface. They are developed through knowledge engineering, which involves knowledge engineers working with domain experts to gather knowledge about a problem domain and represent it in a way that a computer can understand. Some key applications of expert systems include medical diagnosis, mineral prospecting, and configuring computer systems.
The document discusses expert systems, which are computer programs that use knowledge and reasoning to solve complex problems like human experts. It describes key components of expert systems like the knowledge base, reasoning engine, and user interface. Examples of early medical expert systems MYCIN and Internist are provided that demonstrated how expert systems can model human diagnostic reasoning strategies. While expert systems have been effective in some domains, full integration into fields like medicine has proven challenging.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
The document introduces expert systems, which are computer programs that use specialized knowledge to solve problems like a human expert. An expert system captures knowledge from human experts to reason through problems in a specific domain. Building an expert system involves knowledge engineering to extract rules and encapsulate the expertise. Expert systems have advantages over human experts like constant availability and ability to explain their reasoning.
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
The document provides an overview of artificial intelligence and key developments in the field, including:
1. It discusses early definitions of intelligence and issues with defining AI, as well as tests like the Turing Test.
2. Early developments in AI focused on game playing to demonstrate problem solving abilities within limited domains.
3. Research then shifted to language processing with programs like ELIZA, which could hold basic conversations, and knowledge representation using semantic nets and logic programming.
Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P...Priti Srinivas Sajja
Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012).
She has 104 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Three of her publications have won best research paper awards. Visit pritisajja.info for material.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
An introduction to computer vision in Python, from the general concept to its implementation with some current open-source libraries. Demonstrates a selection of basic computer vision examples using SciPy, OpenCV and Pygame.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
Navigation system for blind using GPS & GSMPrateek Anand
Currently, blind people use a traditional cane as a tool for directing them when they move from one place to another. Although, the traditional cane is the most widespread means that is used today by the visually impaired people, it could not help them to detect dangers from all levels of obstacles. In this context, we propose a new intelligent system for guiding individuals who are blind or partially sighted. The system is used to enable blind people to move with the same ease and confidence as a sighted people. The system is linked with a GSM-GPS module to pin-point the location of the blind person and to establish a two way communication path in a wireless fashion. Moreover, it provides the direction information as well as information to avoid obstacles based on ultrasonic sensors. A beeper, an accelerometer sensor and vibrator are also added to the system. The whole system is designed to be small, light and is used in conjunction with the white cane. The results have shown that the blinds that used this system could move independently and safely.
Python Tricks That You Can't Live WithoutAudrey Roy
Audrey Roy gave a presentation on Python tricks for code readability and reuse at PyCon Philippines 2012. She discussed writing clean, understandable code by following PEP8 style guidelines and using linters. She also explained how to find and install reusable Python libraries from the standard library and PyPI, and how to write packages and modules to create reusable code.
The document discusses expert systems, which are designed to solve real problems in a particular domain that normally require human expertise. Developing an expert system involves extracting knowledge from domain experts. The key components of an expert system are the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. Expert systems use knowledge rather than data to solve problems and can explain their reasoning. They have limitations such as being difficult to maintain and only applicable to narrow problems.
The document discusses the cellular concept in wireless networks. Key points include:
- Cells have a hexagonal shape and neighboring cells reuse frequencies to avoid interference and increase capacity.
- Frequency reuse allows more simultaneous calls by allocating the same set of frequencies to different neighboring cells.
- Cell size is a tradeoff between interference and system capacity - smaller cells mean lower power needs but more cells and handoffs.
In this presentation, we will discuss elaborately on strategic operations management, concept of strategy, five tasks of strategic management, strategic management process and importance of strategic management. We will also talk about role of operations in strategic management and elements of operations strategy,.
To know more about Welingkar School’s Distance Learning Program and courses offered, visit: http://www.welingkaronline.org/distance-learning/online-mba.html
An expert system is a computer-based system that uses knowledge from human experts to solve problems typically requiring an expert. Expert systems model the problem-solving abilities of human experts. They are well-suited for problems that do not require complex reasoning, are well-understood, use objectively described data, require fast and accurate answers, or where human expertise is difficult to obtain. Expert systems have three main modules: a knowledge acquisition module to obtain expertise from human experts, a consultation module to provide answers to user questions, and an explanation module to explain how answers are inferred.
Lecture5 Expert Systems And Artificial IntelligenceKodok Ngorex
Expert systems aim to emulate human expertise by storing knowledge provided by human experts. They utilize various artificial intelligence techniques like rule-based reasoning, pattern recognition, and case-based reasoning to solve complex problems. An expert system consists of a user interface, knowledge base containing domain-specific knowledge, and an inference engine that applies logic and reasoning to the knowledge base. While expert systems can increase availability of expertise, there are limitations in coding human common sense and adapting to new problems.
This document provides an overview of expert systems and AI languages. It discusses the need and justification for expert systems, as well as common expert system architectures including rule-based systems and non-production systems. It also covers knowledge acquisition and case studies of expert systems. For AI languages, it mentions Prolog syntax and programming as well as Lisp syntax and programming, including backtracking in Prolog. The document includes sample questions for 2 marks and 7 marks.
This document provides an overview of artificial intelligence and its applications. It discusses the nature of intelligence and the major branches of AI, including expert systems, robotics, vision systems, natural language processing, learning systems, neural networks, and other applications. It also covers virtual reality systems and interface devices. The document is intended as a teaching tool to introduce students to the key concepts and uses of artificial intelligence.
An expert system is a knowledge-based information system that uses knowledge from a specific domain to provide information to users like a human expert. Expert systems are useful when human experts are unavailable, inconsistent, or unable to clearly explain decisions. They can be applied when a problem lacks a clear algorithmic solution, is hazardous, has a scarcity of human experts, or requires standardization. Some examples of early expert systems include LITHIAN which advised archaeologists and DENDRAL which identified chemical structures. Expert systems have advantages like enhanced decision quality, reduced consulting costs, and ability to solve complex problems, but developing and maintaining them can be difficult and expensive.
An expert system is a computer program that contains knowledge about a specific domain that allows it to solve problems or provide advice like a human expert. Expert systems are made up of a knowledge base, inference engine, and user interface. They are developed through knowledge engineering, which involves knowledge engineers working with domain experts to gather knowledge about a problem domain and represent it in a way that a computer can understand. Some key applications of expert systems include medical diagnosis, mineral prospecting, and configuring computer systems.
The document discusses expert systems, which are computer programs that use knowledge and reasoning to solve complex problems like human experts. It describes key components of expert systems like the knowledge base, reasoning engine, and user interface. Examples of early medical expert systems MYCIN and Internist are provided that demonstrated how expert systems can model human diagnostic reasoning strategies. While expert systems have been effective in some domains, full integration into fields like medicine has proven challenging.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
The document introduces expert systems, which are computer programs that use specialized knowledge to solve problems like a human expert. An expert system captures knowledge from human experts to reason through problems in a specific domain. Building an expert system involves knowledge engineering to extract rules and encapsulate the expertise. Expert systems have advantages over human experts like constant availability and ability to explain their reasoning.
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
The document provides an overview of artificial intelligence and key developments in the field, including:
1. It discusses early definitions of intelligence and issues with defining AI, as well as tests like the Turing Test.
2. Early developments in AI focused on game playing to demonstrate problem solving abilities within limited domains.
3. Research then shifted to language processing with programs like ELIZA, which could hold basic conversations, and knowledge representation using semantic nets and logic programming.
Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P...Priti Srinivas Sajja
Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012).
She has 104 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Three of her publications have won best research paper awards. Visit pritisajja.info for material.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
An introduction to computer vision in Python, from the general concept to its implementation with some current open-source libraries. Demonstrates a selection of basic computer vision examples using SciPy, OpenCV and Pygame.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
Navigation system for blind using GPS & GSMPrateek Anand
Currently, blind people use a traditional cane as a tool for directing them when they move from one place to another. Although, the traditional cane is the most widespread means that is used today by the visually impaired people, it could not help them to detect dangers from all levels of obstacles. In this context, we propose a new intelligent system for guiding individuals who are blind or partially sighted. The system is used to enable blind people to move with the same ease and confidence as a sighted people. The system is linked with a GSM-GPS module to pin-point the location of the blind person and to establish a two way communication path in a wireless fashion. Moreover, it provides the direction information as well as information to avoid obstacles based on ultrasonic sensors. A beeper, an accelerometer sensor and vibrator are also added to the system. The whole system is designed to be small, light and is used in conjunction with the white cane. The results have shown that the blinds that used this system could move independently and safely.
Python Tricks That You Can't Live WithoutAudrey Roy
Audrey Roy gave a presentation on Python tricks for code readability and reuse at PyCon Philippines 2012. She discussed writing clean, understandable code by following PEP8 style guidelines and using linters. She also explained how to find and install reusable Python libraries from the standard library and PyPI, and how to write packages and modules to create reusable code.
The document discusses expert systems, which are designed to solve real problems in a particular domain that normally require human expertise. Developing an expert system involves extracting knowledge from domain experts. The key components of an expert system are the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. Expert systems use knowledge rather than data to solve problems and can explain their reasoning. They have limitations such as being difficult to maintain and only applicable to narrow problems.
The document discusses the cellular concept in wireless networks. Key points include:
- Cells have a hexagonal shape and neighboring cells reuse frequencies to avoid interference and increase capacity.
- Frequency reuse allows more simultaneous calls by allocating the same set of frequencies to different neighboring cells.
- Cell size is a tradeoff between interference and system capacity - smaller cells mean lower power needs but more cells and handoffs.
In this presentation, we will discuss elaborately on strategic operations management, concept of strategy, five tasks of strategic management, strategic management process and importance of strategic management. We will also talk about role of operations in strategic management and elements of operations strategy,.
To know more about Welingkar School’s Distance Learning Program and courses offered, visit: http://www.welingkaronline.org/distance-learning/online-mba.html
An expert system is a computer-based system that uses knowledge from human experts to solve problems typically requiring an expert. Expert systems model the problem-solving abilities of human experts. They are well-suited for problems that do not require complex reasoning, are well-understood, use objectively described data, require fast and accurate answers, or where human expertise is difficult to obtain. Expert systems have three main modules: a knowledge acquisition module to obtain expertise from human experts, a consultation module to provide answers to user questions, and an explanation module to explain how answers are inferred.
Lecture5 Expert Systems And Artificial IntelligenceKodok Ngorex
Expert systems aim to emulate human expertise by storing knowledge provided by human experts. They utilize various artificial intelligence techniques like rule-based reasoning, pattern recognition, and case-based reasoning to solve complex problems. An expert system consists of a user interface, knowledge base containing domain-specific knowledge, and an inference engine that applies logic and reasoning to the knowledge base. While expert systems can increase availability of expertise, there are limitations in coding human common sense and adapting to new problems.
This document provides an overview of expert systems and AI languages. It discusses the need and justification for expert systems, as well as common expert system architectures including rule-based systems and non-production systems. It also covers knowledge acquisition and case studies of expert systems. For AI languages, it mentions Prolog syntax and programming as well as Lisp syntax and programming, including backtracking in Prolog. The document includes sample questions for 2 marks and 7 marks.
This document provides an overview of artificial intelligence and its applications. It discusses the nature of intelligence and the major branches of AI, including expert systems, robotics, vision systems, natural language processing, learning systems, neural networks, and other applications. It also covers virtual reality systems and interface devices. The document is intended as a teaching tool to introduce students to the key concepts and uses of artificial intelligence.
This document discusses various technologies that can support decision making, including:
- Data mining techniques like regression, decision trees, and market basket analysis that analyze patterns in historical business data.
- Executive information systems that provide customizable, graphical interfaces for trend analysis and exception reporting.
- Expert systems that capture human expertise in a specific domain, and knowledge engineering which involves building these systems.
- Neural networks, genetic algorithms, and virtual reality technologies that can be applied to business problems and processes.
- Intelligent agents that act as software surrogates to perform tasks using learned knowledge.
The document discusses artificial intelligence and expert systems. It provides an overview of AI, its major branches including expert systems, and how expert systems work. Expert systems use a knowledge base and inference engine to mimic the decision-making of human experts in specific domains. They have benefits like preserving human expertise but also limitations like narrow applicability. The document outlines the components and applications of expert systems.
Artificial intelligence began in the 1960s with early attempts at game playing, theorem proving, and problem solving. An expert system is a type of AI that attempts to provide answers to problems where human experts would normally be consulted. Expert systems use knowledge bases, inference engines, and other components to mimic human expertise in a specific domain. Virtual reality allows users to interact with simulated environments through technologies like head-mounted displays, CAVEs, and haptic interfaces.
This document discusses expert systems, which are computer applications that embody expertise to solve problems in specific domains. It defines expert systems as consisting of an inference engine and knowledge base, similar to how traditional programs have algorithms and data structures. Some key points made:
- Expert systems can be used for tasks like diagnosis, financial planning, and configuring computers that previously required human expertise.
- The major components of expert systems are the user interface, inference engine, and knowledge base.
- Knowledge acquisition is the process of extracting an expert's knowledge and structuring it for the knowledge base.
- Example expert systems include MYCIN for medical diagnosis and DART for computer fault diagnosis.
The document discusses expert systems, which are computer systems that emulate the decision-making ability of a human expert. It describes the typical architecture of an expert system, which includes a knowledge base, inference engine, user interface, explanation facility, and knowledge acquisition system. It provides details on key components like the knowledge base, which stores rules and data, and the inference engine, which applies rules and reasoning to derive conclusions. Specific expert systems are discussed like MYCIN for medical diagnosis, DART for computer fault diagnosis, and XCON for configuring DEC computer systems. The roles of knowledge engineers and domain experts in developing expert systems are also outlined.
This document provides an overview of artificial intelligence (AI), including its history, major branches, expert systems, and applications. It discusses how AI aims to build intelligent machines that can think and act like humans. The major branches covered are perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are described as AI programs that store knowledge and make inferences to emulate human experts. The document also outlines the typical components of an expert system, including the knowledge base, inference engine, and user interface. Common AI software mentioned includes CLIPS, Weka, and MOEA Framework.
1. The document discusses the perspectives of John Huffman, CTO of Philips Healthcare Informatics, on big data, analytics, and AI in healthcare.
2. It outlines the multi-stage process for advanced analytics, including data ingestion, model training, evaluation, and production. It also discusses challenges in data collection/processing and model deployment.
3. Key points made are that data is more important than algorithms/methods; analytics requires clean, interoperable data; and the stack provides tools but not full solutions - data is the intellectual property.
This document discusses principles of knowledge management and specialized information systems. It defines knowledge as awareness and understanding of information that can be useful for tasks or decisions. Knowledge management systems organize people, processes, and technologies to create, store, share, and use organizational knowledge. Artificial intelligence uses computer systems that can mimic human decision making, like expert systems. Expert systems apply rules to arrive at conclusions like a human expert. Virtual reality immerses users in simulated 3D environments using displays and interfaces. Specialized systems provide unique functions for industries, individuals, inventory control and more.
This document provides information about an Intelligent Systems unit, including:
- The unit aims to provide an understanding of intelligent systems technologies and their applications.
- The unit will be delivered through 3 hours of weekly lectures and workshops discussing topics related to intelligent systems.
- Assessment includes workshop participation, a project, and a closed-book exam evaluating students' understanding of intelligent systems methodologies and applications in business.
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This document provides an overview of artificial intelligence and expert systems. It discusses key concepts in artificial intelligence including machine learning, problem solving, and visual processing. The major branches of AI are described as perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as storing knowledge and making inferences. The components, advantages, and applications of expert systems are also summarized.
An expert system is a computer program that simulates human intelligence and behavior in specific domains using a knowledge base, inference engine, and user interface. Expert systems are useful for limited domains where expert knowledge is available and can enhance tasks by applying heuristic knowledge. The key components are the knowledge base containing rules and facts, the inference engine that controls reasoning, and the user interface. Knowledge engineering is used to acquire and encode human domain knowledge.
Gary Paek from Intel presented this deck at the HPC User Forum in Tucson.
Learn more: https://software.intel.com/en-us/tags/18892
and
http://hpcuserforum.com
Watch the video presentation: http://wp.me/p3RLHQ-fdt
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document provides an overview and introduction to the Drools5 Community Training course. It discusses the theoretical background of artificial intelligence, expert systems, and rule-based production systems. It also introduces the main modules of the Drools Business Logic Integration Platform including Drools Expert, jBPM5, Drools Fusion, and Drools Guvnor.
Machine Learned Relevance at A Large Scale Search EngineSalford Systems
The document discusses machine learned relevance at a large scale search engine. It provides biographies of the two authors who have extensive experience in machine learning and search engines. It then outlines the topics to be covered, including an introduction to machine learned ranking for search, relevance evaluation methodologies, data collection and metrics, the Quixey search engine system, model training approaches, and conclusions.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
The document provides an introduction to expert systems. It defines an expert system as a computer system that emulates the decision-making abilities of a human expert. The key components of an expert system are a knowledge base containing the expertise knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems offer advantages like increased availability, reduced costs, reliability, and the ability to explain their reasoning. However, they also have limitations like dealing with uncertainty and an inability to generalize knowledge like humans.
Cleades Robinson, a respected leader in Philadelphia's police force, is known for his diplomatic and tactful approach, fostering a strong community rapport.
The E-Way Bill revolutionizes logistics by digitizing the documentation of goods transport, ensuring transparency, tax compliance, and streamlined processes. This mandatory, electronic system reduces delays, enhances accountability, and combats tax evasion, benefiting businesses and authorities alike. Embrace the E-Way Bill for efficient, reliable transportation operations.
MUTUAL FUNDS (ICICI Prudential Mutual Fund) BY JAMES RODRIGUESWilliamRodrigues148
Mutual funds are investment vehicles that pool money from multiple investors to purchase a diversified portfolio of stocks, bonds, or other securities. They are managed by professional portfolio managers or investment companies who make investment decisions on behalf of the fund's investors.
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2. 11- 2
Chapter Objectives
1. Define the field of artificial intelligence (AI)
2. Define an expert system (ES).
3. Specify and discuss the areas of ES application.
4. Specify the components of an expert system.
5. Define knowledge base and knowledge representation.
6. Explain what rule-based expert systems are.
7. Define fuzzy logic.
8. Specify the categories of expert system technology.
3. 11- 3
Chapter Objectives, cont.
9. Define the roles in expert system development.
10. Specify the principal benefits and limitations of expert systems.
11. Name other applied fields of artificial intelligence and discuss
their potential role in information systems.
12. Define neural networks and their capabilities.
4. 11- 4
Defining Artificial Intelligence
• AI deals with methods of developing systems that
display aspects of intelligent behavior
• AI systems imitate human capabilities of thinking
and sensing
5. 11- 5
Defining Artificial Intelligence: AI Systems
1. Symbolic Processing
– Computers process symbols
– AI applications process strings of characters that represent the
real world
– Symbols are arranged as lists, hierarchies, or networks and
their interrelations
2. Nonalgorithmic Processing
– Specified step by step procedures
6. 11- 6
Defining Artificial Intelligence
• Science and Technology
• Computer Science
• Biology
• Psychology
• Linguistics
• Mathematics
• Engineering
• Goal: Develop computers that think (reasoning,
learning, and problem solving), sense (see, hear,
talk, feel), and walk
7. 11- 7
Defining Artificial Intelligence:
History and Evolution of AI
• 1950- Turing Test- General problem solving test
• 1960- AI as a field- Knowledge based expert
systems
• 1970- AI commercialization- Transaction
processing and decision support systems
• 1980- Artificial neural networks- Resembling the
human brain
• 1990- Intelligent Agents- Software that performs
assigned tasks
8. 11- 8
Capabilities of Expert Systems: General View
• Expert System (ES)
– Knowledge based system
– Uses inferencing or reasoning procedure to solve problems
that require human expertise
• Knowledge Base
– Domain of knowledge of the expert system
• Heuristic Knowledge
– Rules used by humans
9. 11- 9
Applications of Expert Systems:
Generic Categories of Expert System Applications
• Classification
– Identify an object
• Diagnosis Systems
– Infer malfunction from observable data
• Monitoring
– Continually observe behavior
• Process Control
– Control a physical process based on monitoring
• Design
– Configure a system according to specifications
• Scheduling and Planning
– Plan of action
• Generation of Options
– Alternative solutions to problems
10. 11- 10
How Expert Systems Work
• Knowledge Base
– Organized collection of facts and heuristics about the systems
domain
• Knowledge Representation
– Method to organize the knowledge base
11. 11- 11
Structure of an Expert System
Consultation Environment Development Environment
(Use) (Knowledge Acquisition)
User Expert
Facts of Recommendation,
the Case Explanation
User Interface
Explanation Knowledge
Facility Engineer
Inference Engine
Facts of
the
Knowledge
Case Acquisition
Facility
Working Memory
Knowledge Domain Knowledge
Base (Elements of
Knowledge Base)
12. 11- 12
How Expert Systems Work:
Knowledge Representation
• Frame Based Systems
– Build powerful expert systems
– The frame specifies the attributes of a complex object and its
relationships
• Production Rules
– Rule Based expert systems
– Knowledge is represented by production rules
– Most common method of knowledge representation
– IF part (Condition or Premise) and THEN part (Action or
Conclusions_
– Explanation facility
» How the system arrived at the recommendation
» Uses natural language or numbers
13. 11- 13
How Expert Systems Work:
Inference Engine
• Combines the facts of a specific case with the
knowledge in the knowledge base to decide upon
a recommendation
– Reasoning in Rule Based systems
» In rule based expert system, the inference engine controls
the order in which the production rules are applied or
“fired” and resolves conflicts for more than one applicable
rule
• Directs the user interface to query the user for
further information
– The facts are entered into working memory
– Rules are applied by the inference engine until a goal state is
produced or confirmed
14. 11- 14
How Expert Systems Work:
Inference Engine: Strategies
• Forward Chaining
– A data driven strategy
– Inference from the facts of a case to a conclusion
– Match the IF part with the facts available
– Used to solve open ended problems of a design or planning
• Backward Chaining
– The inference engine matches the assumed hypothesis or
conclusion which is the goal state with the conclusion or THEN
part
– If the hypothesis is not supported, then the system will attempt
to prove another goal state
– Used for limited in number and well defined problems
– Use classification or diagnosis systems
15. 11- 15
Inferencing Strategies
Conclusion
(Goals)
Input
Data
Few Items Many Possibilities
(For Example, User (For Example, a
Specifications for Computer
a Computer Configuration)
System)
(a) Forward Chaining: IF - Part Matches Shown
16. 11- 16
Inferencing Strategies (Cont.)
Input
Data
Conclusion
(Goals)
Extensive;
Much of the Data Few Possibilities
Obtained by the (Known in Advance
System Querying ((For Example,
the User (For Investment Options)
Example,
Investor’s Profile)
(b) Backward Chaining: THEN - Part Matches Shown
17. 11- 17
How Expert Systems Work:
Uncertainty and Fuzzy Logic
• Resembles human reasoning
• Allows approximate values or inferences and
incomplete or ambiguous data
• Handles uncertainty
• More flexible
• Creative
• Can be used to control manufacturing processes
18. 11- 18
Expert System Technology
• The tool selected for the project must match the
capability of the projected expert system
• Must be able to integrate with other subsystems
and databases
• The tool must match the qualifications of the
project team
19. 11- 19
Expert System Technology
• Specific Expert Systems
– Provide recommendations for a specific task domain
• Expert System Shells
– Shell without a knowledge base
– Furnishes the ES developer with the inference engine, user
interface, and the explanation and knowledge acquisition
facilities
– Domain specific shells
» Incomplete specific expert systems
• Expert System Development Environments
– Run on engineering workstations, minicomputers, or
mainframes
– Integration with databases
• High Level Programming Languages
– LISP, C, C++
20. 11- 20
Expert Systems Technologies
Greater
Complexity of
Greater Higher-Level
Problem and
Flexibility Programming
Environment
Language
Expert System
Development
Environment
Generic Shell
Domain-Specific
Shell
Greater Specific Expert
Ease of Use System
21. 11- 21
Roles in Expert System Development
• Expert
– Knowledge
• Knowledge Engineer
– Knowledge acquisition tactics include interviews, protocol
analysis, observation, and analysis of cases
– Must select a tool with the application of the knowledge
acquisition facility
• User
– End user with a simple shell
– Prototypes are used
22. 11- 22
Development and Maintenance of Expert Systems
1. Problem Identification and Feasibility Analysis
– The problem must be suitable for an expert to solve it.
– Find an expert for the project
– Cost effectiveness must be established
2. System Design and Expert System Technology
Identification
– The system is designed with integration other subsytems and
databases
– Domain knowledge
– Knowledge and inferencing is established with simple cases
3. Development of Prototype
– Knowledge Engineer works with the expert
– Specific Tool is chosen for the project
23. 11- 23
Development and Maintenance of Expert Systems
(Continued)
4. Testing and Refinement of Prototype
– Test with simple cases
– Deficiencies in performance are noted.
– End users test the prototypes.
5. Complete and Field the Expert System
– The interaction with the environment,, users, and other
information systems is tested
– Documented
– User training
6. Maintain the System
– The system is kept current by updating the knowledge base
– Interfaces with other information systems are maintained
24. 11- 24
Development & Maintenance of ESs
Problem Identification and
Feasibility Analysis
System Design and ES
Technology Identification
Development of
Prototype
Testing and Refinement
of Prototype
Yes
Is the Performance
Satisfactory? Complete and
No Field the ES
ES Ready for Use
Maintain ES
25. 11- 25
Expert Systems in Organizations: Benefits
1. An ES can complete its task faster than a human
2. Low error rate, and lower than human error rate
3. ESs make consistent recommendations.
4. ESs are a convenient vehicle for difficult sources
of knowledge
5. ESs bring forth expertise
6. ESs can build organizational knowledge, as
opposed to the knowledge of individuals
7. ESs can be used for training with a faster
learning curve
8. The company can operate an ES in environments
that are hazardous to humans
26. 11- 26
Expert Systems in Organizations: Limitations
1. Limitations of the technology
2. Problems with knowledge acquisition
3. Operational domains as the principal area
4. Maintaining human expertise
28. 11- 28
Overview of Applied Artificial Intelligence
• Natural Language Processing
– Talk to computers and have them “understand”
• Robotics
– Artificial Intelligence, Engineering, and Physiology
– Human like applications
• Computer Vision
– Simulation of the human senses
– Visual scene recognition
• Speech recognition
– Understand speech, also of an unknown speaker
29. 11- 29
Overview of Applied Artificial Intelligence
• Machine Learning
1. Problem Solving Learning
– Accumulate experience about rules
2. Case Based Learning
– collecting cases from a knowledge base
3. Inductive Learning
– Learning from examples
– generate knowledge using rules
30. 11- 30
Applied Fields of AI
AI
Com-
Natural puterized
Expert Computer Machine
Language Robotics Speech
Systems Vision Learning
Processing Recog-
nition
31. 11- 31
Neural Networks
• Computing systems modeled on the human
brain’s interconnected processing elements or
neurons
– 100 billion neuron brain cells
• An array of interconnected processing elements
accept inputs, processing, and then producing an
output imitating the human brain
• Requires sophisticated pattern recognition
• Does not explain the conclusions they make
• Can be made to recognize patterns, and then
apply to new cases
32. 11- 32
Key Terms in Chapter 11
Artificial Intelligence (AI) Expert System Development
Expert System (ES) Environment
Knowledge Base Knowledge Engineer
Heuristic Knowledge Knowledge Acquisition Facility
Knowledge Engineering Natural Language Processing
Knowledge Representation Robot
Rule-Based Expert System Computer Vision
IF-THEN Rule Computerized Speech
Explanation Facility Recognition
Inference Engine Machine Learning
Working Memory Neural Network
Forward Chaining
Backward Chaining
Fuzzy Logic
Expert System Shell
Domain-Specific Shell