AI with expert system
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This document discusses AI with expert systems. It begins with an introduction to AI, noting its branches include game playing, expert systems, natural language processing, neural networks, and robotics. It then introduces expert systems, which emulate human decision making through knowledge bases and inference engines. The components of expert systems are described as the knowledge base, inference engine, and rules. Capabilities include strategic decision making, planning, and diagnosis. Expert system development involves domain experts, knowledge engineers, and knowledge users. The document traces the evolution of expert system software from traditional programming to expert system shells. It concludes with potential applications in fields like banking, healthcare, and customer service.
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Expert Systems
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.
Computer vision
Computer vision is the science and technology of machines that see. It uses theories and models from fields like physics, neurobiology, and signal processing to build artificial systems that can obtain information from images. Typical applications of computer vision include industrial inspection, medical imaging, autonomous vehicles, and visual surveillance. Common computer vision tasks involve recognition, motion analysis, scene reconstruction, and image restoration.
Ai lecture 03 computer vision
Computer vision is a field that uses methods to process, analyze and understand images and visual data from the real world in order to produce decisions or symbolic information. The goal of computer vision is to automatically extract, analyze and understand useful information from single images or sequences of images to represent real-world objects, similar to how humans use their eyes and brain for vision. Computer vision involves image acquisition, processing, analysis, and comprehension stages to sense images, improve image quality, examine scenes to identify features, and understand objects and their relationships.
Introduction to artificial intelligence lecture 1
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
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The document provides an overview of artificial intelligence including its history and key application areas. It discusses how AI research evolved from rationalist traditions in ancient Greece and developments in formal logic in the 19th century. Some of the major application areas discussed include game playing, automated reasoning, expert systems, natural language processing, robotics, and machine learning. The document also covers knowledge engineering concepts such as knowledge acquisition, analysis and representation, validation, and computer-assisted knowledge elicitation techniques.
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This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Computer vision
Computer vision analyzes visual data like images and videos to understand and interpret them similarly to humans. It works by training models on large datasets to recognize patterns and classify objects. Applications include face recognition for login, medical imaging analysis, and computer vision in autonomous vehicles. The future of computer vision may involve combining it with natural language processing for image captioning and visual assistance applications.
Neural Networks
The document discusses neural networks and their applications. It provides an overview of neural networks, including their history and how they are modeled after biological neurons. Supervised learning is described as training neural networks using labeled input-output pairs. Specific neural network concepts like the perceptron, backpropagation, and convolutional neural networks are explained. Applications mentioned include mobile computing, forecasting, character recognition, data mining, and image recognition. Both merits like flexibility and demerits like requiring large processing are noted.
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Expert Systems
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.
Computer vision
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Ai lecture 03 computer vision
Computer vision is a field that uses methods to process, analyze and understand images and visual data from the real world in order to produce decisions or symbolic information. The goal of computer vision is to automatically extract, analyze and understand useful information from single images or sequences of images to represent real-world objects, similar to how humans use their eyes and brain for vision. Computer vision involves image acquisition, processing, analysis, and comprehension stages to sense images, improve image quality, examine scenes to identify features, and understand objects and their relationships.
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The document provides an overview of artificial intelligence including its history and key application areas. It discusses how AI research evolved from rationalist traditions in ancient Greece and developments in formal logic in the 19th century. Some of the major application areas discussed include game playing, automated reasoning, expert systems, natural language processing, robotics, and machine learning. The document also covers knowledge engineering concepts such as knowledge acquisition, analysis and representation, validation, and computer-assisted knowledge elicitation techniques.
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Computer vision
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Neural Networks
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Lecture 6 expert systems
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1. What is Artificial intelligence?
2. Types of Artificial intelligence
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2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
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Learn more at: https://www.simplilearn.com
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- Application of AI
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This Presentation will give you an overview about Artificial Intelligence : definition, advantages , Categories of AI, Types of AI, disadvantages , benefits , applications .
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Computer vision is a field of artificial intelligence that uses digital image processing techniques to analyze visual content and understand scenes. The goal is to extract meaningful information from digital images and emulate some of the capabilities of human vision such as object recognition. Computer vision has applications in security, human behavior analysis, optical character recognition, special effects in movies, face filters in social media, autonomous vehicles, and scene formation. OpenCV is a popular open source library for computer vision that is written in C++ but can be used with other languages like C#, Java, and Python.
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5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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Lecture 06 production system
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6.expert systems
Expert systems are AI programs that use knowledge bases to achieve expert-level competence in solving problems in a particular task domain. They consist of two main components: a knowledge base containing factual and heuristic knowledge about the domain, and an inference engine that uses reasoning methods like backward and forward chaining to derive answers. Expert systems have applications in fields like medicine, agriculture, and education, where they can provide consistent advice and help clarify decision making. However, they lack common sense and cannot respond creatively to unusual situations.
Expert 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.
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The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
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Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
Artifical Intelligence
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
Lecture 1- Artificial Intelligence - Introduction
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , Categories of AI, Types of AI, disadvantages , benefits , applications .
We hope it to be useful .
Computer vision
Computer vision is a field of artificial intelligence that uses digital image processing techniques to analyze visual content and understand scenes. The goal is to extract meaningful information from digital images and emulate some of the capabilities of human vision such as object recognition. Computer vision has applications in security, human behavior analysis, optical character recognition, special effects in movies, face filters in social media, autonomous vehicles, and scene formation. OpenCV is a popular open source library for computer vision that is written in C++ but can be used with other languages like C#, Java, and Python.
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This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Artificial intelligence in software engineering ppt.
The document discusses artificial intelligence and defines it as the science and engineering of making intelligent machines, especially intelligent computer programs. It notes two main approaches to AI: engineering and cognitive modeling. Intelligence is defined as the ability to learn and solve problems, specifically the ability to solve novel problems, act rationally, and act like humans. The document also discusses various applications and techniques in AI, including search algorithms, expert systems, fuzzy logic, robotics, and genetic algorithms.
Lecture 06 production system
Production systems provide a structure for modeling problem solving as a search process. A production system consists of rules, knowledge databases, a control strategy, and a rule applier. The rules take the form of condition-action pairs. The control strategy determines the order of rule application and resolves conflicts. Production systems can be classified based on whether rule application is monotonic or non-monotonic. They provide modularity and a natural representation but can suffer from opacity, inefficiency, and lack of learning abilities. Choosing the right production system depends on characteristics of the problem such as decomposability and predictability.
Ensemble Learning and Random Forests
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Expert Systems
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.
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Expert systems are AI programs that use knowledge bases to achieve expert-level competence in solving problems in a particular task domain. They consist of two main components: a knowledge base containing factual and heuristic knowledge about the domain, and an inference engine that uses reasoning methods like backward and forward chaining to derive answers. Expert systems have applications in fields like medicine, agriculture, and education, where they can provide consistent advice and help clarify decision making. However, they lack common sense and cannot respond creatively to unusual situations.
Expert 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.
Topic 8 expert system
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.
Expert systems
An expert system is problem solving software that uses rules and a knowledge base to reason through problems in a specific domain, similar to how a human expert would. It consists of a knowledge base containing data and rules, an inference engine that uses the rules to solve problems, and a user interface for humans to input questions. Examples of expert systems include medical diagnosis systems that can identify potential illnesses based on entered symptoms, and animal/plant identification systems that ask questions to narrow down possible results. Advantages are reducing time on common issues and leveraging collective expert knowledge, while disadvantages include high setup costs and need for ongoing updates to stay current.
Heuristic evaluations.
Este documento presenta el desarrollo de una aplicación móvil llamada "HEURISTIC EVALUATIONS" que permite automatizar evaluaciones heurísticas de usabilidad y calidad de software desde la perspectiva del usuario y experto. El proyecto utiliza metodologías como RUP para el desarrollo del software y realiza pruebas para validar la funcionalidad, seguridad y rendimiento de la aplicación. El objetivo es contar con una herramienta que facilite evaluaciones sistematizadas de usabilidad para aplicaciones web y móviles
Expert System
The document discusses different approaches to developing expert systems, including object models, reformulation approaches, and low road, high road, and middle road approaches. It analyzes the advantages of each approach and concludes that the reformulation approach is best. This approach separates knowledge from problem solving procedures and uses transformation processes to map the expert's knowledge structure to input problems.
Expert System
An expert system is a type of artificial intelligence software that uses human knowledge to solve complex problems. It consists of a knowledge base that contains facts and rules, an inference engine that applies logic, a user interface for interaction, and a working memory to solve problems. The document provides examples of expert systems that have been used for air traffic control, power plant monitoring, medical diagnosis, and more. It explains that an expert system captures human expertise in a specific domain and uses it to provide solutions or explanations, similar to a human expert.
AI and Expert Systems
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.
Expert Systems
An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that require significant human expertise. It emulates the decision-making ability of a human expert in a narrow problem domain. The basic components of an expert system are a knowledge base containing the expert knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems have advantages over human experts such as increased availability, reduced costs, reliability, and ability to provide detailed explanations. However, they are limited compared to human experts in areas such as causal knowledge, knowledge depth, and analogical reasoning.
Artificial Intelligence AI Topics History and Overview
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
Application of expert system
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.
Expert system
The document discusses expert systems, which are a branch of artificial intelligence that uses specialized knowledge to solve problems like a human expert. It defines expert systems and their key components, including the knowledge base, inference engine, working memory, and user interface. The document also covers knowledge representation, problem solving techniques like forward and backward chaining, development life cycles, applications, advantages and limitations of expert systems.
Lecture5 Expert Systems And Artificial Intelligence
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.
Artificial Intelligence
This document discusses various techniques for understanding natural language including:
1) Symbolic and statistical approaches to analyzing syntax and semantics including parsers using context free grammars, transition networks, and augmented transition networks.
2) The role of knowledge in mapping syntax to meaning using type hierarchies and case frames.
3) Techniques for querying databases and extracting information from natural language texts.
Computer Vision
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
Artificial intelligence Speech recognition system
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
Game Playing in Artificial Intelligence
Game Playing in Artificial Intelligence, a Computer Science fields, presented by Mwendwa Kivuva at Catholic University of Eastern Africa
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The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
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Expert system prepared by fikirte and hayat im assignment
The document discusses expert systems, their components, types, and uses. An expert system is an intelligent system that can perform complex tasks like a human expert. It consists of a knowledge base, inference engine, user interface, interpreter, and blackboard. Expert systems are classified based on their function, such as for interpretation, prediction, diagnosis, design, or planning. They can benefit industries and countries by advancing fields like agriculture, education, medicine, and more.
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This document provides an overview of expert systems, including their history, types, components, applications, and development process. Some key points:
- Early expert systems included DENDRAL for chemical analysis and MYCIN for medical diagnosis. Expert systems were pioneered in the 1970s and 1980s.
- Expert systems can advise on complex problems like a human expert through rule-based or frame-based knowledge representation.
- The main components are the knowledge base containing domain expertise, an interface engine that uses rules to solve problems, and a user interface.
- Expert systems have various applications in fields like design, medicine, monitoring, and finance. However, they also have limitations like knowledge acquisition difficulties
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Chapter 6 expert system
The document discusses the history and development of expert systems. It describes some of the earliest expert systems like DENDRAL and MYCIN, which demonstrated that expert knowledge could be encoded in a computer system. The key aspects of expert systems are discussed, including what characterizes a human expert, how expert knowledge is represented in an expert system, and the typical components of an expert system like the knowledge base, inference engine, and user interface. The roles of domain experts, knowledge engineers, and system engineers in building expert systems are also outlined.
Expert system
An expert system is a computer program that contains knowledge from human experts to solve complex problems in a specific domain. It has four main components: a knowledge base of facts and rules, an inference engine that applies rules to facts to derive new facts, a user interface, and an explanation facility. Expert systems were developed in the 1970s to apply human expertise to problems. They have limitations but can also explain their reasoning, draw complex conclusions, and provide portable knowledge to help humans. Common applications of expert systems include medical diagnosis, materials identification, and credit approval.
Expert system
An expert system is a computer program that uses knowledge and inference procedures to solve problems that ordinarily require human expertise. It attempts to reason about problems like a human expert in a specific domain. The key components are a knowledge base that contains the expert knowledge in the form of rules, and an inference engine that applies the rules to deduce solutions. Expert systems have advantages like quick availability and reliability, but also disadvantages like difficulty collecting and interpreting expert knowledge into rules. They are applied in fields like medical diagnosis, process control, and education.
expertsystem.pptx email
This document provides an agenda on expert systems that includes an introduction, definition, history, components, advantages, disadvantages and applications. It defines an expert system as a computer program that simulates human judgment to solve complex problems. The key components are a knowledge base that stores information and rules, and an inference engine that applies rules to deduce answers. Expert systems emerged in the 1970s and proliferated in the 1980s, being among the earliest successful forms of artificial intelligence. They are used in fields like healthcare, manufacturing and games.
AI Expert Systems.pptx
AI Expert Systems Description
AI Expert Systems are a class of artificial intelligence applications designed to replicate the decision-making capabilities of human experts in specific domains. These systems rely on a combination of domain-specific knowledge, reasoning, and rule-based inference to make informed judgments and solve complex problems.
Key Components of AI Expert Systems:
- Knowledge Base: This serves as the repository of domain-specific knowledge, facts, and rules. The more extensive and accurate the knowledge base, the better the system's performance.
- Inference Engine: The inference engine is the "brain" of the system. It applies reasoning and inference rules to the knowledge stored in the knowledge base to arrive at conclusions and make decisions.
- User Interface: The user interface facilitates interaction with the system, allowing users to input queries and receive responses in a user-friendly manner.
- Knowledge Acquisition: The process of gathering, organizing, and structuring domain knowledge from human experts and other sources to populate the knowledge base.
Applications of AI Expert Systems:
- Medical Diagnosis: Expert systems aid in diagnosing medical conditions based on patient symptoms and medical knowledge.
- Financial Advisory: They provide recommendations for investments, financial planning, and risk assessment.
- Quality Control: Expert systems can ensure product quality and adherence to industry standards.
- Information Retrieval: These systems help in retrieving relevant information from large datasets or databases.
- Troubleshooting: Expert systems assist in identifying and solving problems in technical systems and machinery.
Benefits of AI Expert Systems:
- Efficiency: They provide rapid and accurate decision-making, often outperforming human experts.
- Reproducibility: Consistent and reliable performance, unaffected by emotions or fatigue.
- Knowledge Preservation: Knowledge is stored in a structured manner, ensuring it isn't lost over time.
- Wide Applicability: Can be used across diverse domains, from medicine to finance.
Challenges and Limitations:
- Knowledge Accuracy: The system's performance depends on the accuracy of the knowledge in the database.
- Development Costs: Building and maintaining expert systems can be expensive.
- Domain-Specific: Each system is typically tailored to a specific domain, limiting generalizability.
Future Trends:
- Machine Learning Integration: Combining expert systems with machine learning techniques for improved decision-making.
- Greater Automation: Expanding the use of expert systems in autonomous systems and robotics.
- Enhanced User Interfaces: Developing more intuitive and user-friendly interfaces for human interaction.
- Scalability: Improving the scalability of expert systems for handling big data and complex scenarios.
Conclusion:
AI Expert Systems play a crucial role in solving complex problems, providing decision support, and automating.
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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:
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- The major components of expert systems are the user interface, inference engine, and knowledge base.
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AI with expert system
- 1. AI with Expert System Preparead by Peshawa Q.Nabi
- 2. Content Introduction AI Overview of AI Introduction Expert System Component of Expert System Capabilities of Expert System Participants in Expert System Expert System Development Evolution of Expert System Software Summary
- 3. Introduction to AI The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes :- games playing: programming computers to play games such as chess and checkers . expert systems : programming computers to make decisions in real-life . natural language : programming computers to understand natural human languages .
- 4. Introduction to AI neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains . robotics : programming computers to see and hear and react to other sensory stimuli . Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior).
- 5. Introduction of Expert System In arterial intelligent an expert system is a computer system that emulates the decision –making ability of a human expert . Expert system are designed to solve complex problems by resoning about knowledge , like a expert. Do not follow the procedure of a developer as the case in convectional programming Divided into two parts , one fixed in depended of the expert system : the inference , and one variable : the knowledge base. In the 80s a third part appeared : a dialog interface to communicate with users. This ability to conduct a conversation with users was latter called “conversational” .
- 6. Components and Human Interfaces Knowledge base Stores all relevant information, data, rules, cases, and relationships used by the expert system Inference engine Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions in the way a human expert would Rule A conditional statement that links given conditions to actions or outcomes
- 7. Components and Human Interfaces User Interface Inference Engine Knowledge Base
- 8. Capabilities of Expert SystemCapabilities of Expert Systems Strategic goal setting Decision making Planning Design Quality control and monitoring Diagnosis Explore impact of strategic goals Impact of plans on resources Integrate general design principles and manufacturing limitations Provide advise on decisions Monitor quality and assist in finding solutions Look for causes and suggest solutions
- 9. Participants in Expert System Domain expert : The in individual or group whose experts and knowledge is captured for use in an expert system. Knowledge user : The in individual or group who uses and benefits form the expert system. Knowledge engineer : Someone trained or experienced in the design ,development, implementation , and maintenance , of an expert system.
- 10. Participants in Expert System Expert system Domain expert Knowledge engineer Knowledge user
- 11. Development of Expert System
- 12. Evolution of Expert Systems Software Expert system shell Collection of software packages & tools to design, develop, implement, and maintain expert systems Easeofuse low high Traditional programming languages Special and 4th generation languages Expert system shells Before 1980 1980s 1990s
- 13. Advantages of Expert Systems Will never “retire” . Essay to use . People can the initial diagnosis from home (Saving money) . Easy to develop and modify . The use of heuristics . Etc……..
- 14. Applications of Expert Systems Credit granting Information management and retrieval AI and expert systems embedded in products Plant layout Hospitals and medical facilities Help desks and assistance Employee performance evaluation Loan analysis Virus detection Repair and maintenance Shipping Marketing Warehouse optimization
- 15. Summary in this Video !
- 16. THANK YOU