5.11 expert system
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An expert system is a type of artificial intelligence software that uses a knowledge base of human expert knowledge to advise users. The expert system infers answers to user questions using an inference engine that applies rules to the knowledge base. A knowledge engineer collects information from human experts to build the initial knowledge base and rules for the system to use in providing automated expert advice where a human expert may be unavailable or too expensive.
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Expert system
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This document provides information about expert systems, including:
- Expert systems are AI programs that represent human expertise in a particular domain to help solve problems that usually require human expertise.
- They are composed of a knowledge base, inference engine, and user interface. The knowledge base contains facts and rules, the inference engine applies logic, and the user interface allows for interaction.
- Expert systems can help with business organization, providing expertise, capturing knowledge, developing competencies, and automating equipment. They act as intelligent assistants and help make expertise more accessible.Expert systems Artificial Intelligence
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The document discusses expert systems, which are computer programs that use artificial intelligence to solve complex problems that usually require human expertise. An example is a medical diagnosis expert system that allows a user to diagnose a disease without seeing a doctor. The key components of an expert system are the knowledge base, inference engine, and user interface. The knowledge base contains facts and rules acquired from human experts. The inference engine uses the rules to deduce conclusions. It can work forward or backward from the facts. The user interface allows interaction between the user and the system. The document provides examples of code for a medical diagnosis expert system and discusses some limitations of expert systems.Expert system
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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 presentation
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The scope of expert systems in different areas and different domains are increasing. We are working on development of the expert system for evaluating motivational strategy on human resources. From the literature review, we found that mainly there are three approaches used for development of the expert system: Rule base, Fuzzy and Neural network. In the first half of the case study, we explored the pros and cons of each approach and provided the comparison of applicability of which approach is most suited and when. In the second half of the case study, we explored the feasibility of the approach for our domain area of motivational strategy on human resources. At the end, we found that Neural Network approach is the most suited for our domain because of the flexibility, adaptability to the changing environment and generalisation.Lec 4 expert systems
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1. The document describes an expert system and its components.
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Expert system a branch of artificial intelligence is one of the new waves of technology that have influenced the services of the library. The concept of expert system developed from the subject domain of artificial intelligence (AI) requires a departure from conventional practices and programming techniques.
¡°Expert systems in libraries are computer-based systems that mimic the decision-making abilities of a human expert in a specific domain, using a knowledge base and inference engine to provide expert-level advice, support, and solutions to library users and staff, with the goal of improving the quality, accuracy, and efficiency of library services and operations¡±. It stores and interprets the experience and knowledge of human expert by using computer technology. Edward Albert Feigenbaum has defined an expert system as ¡°an intelligent computer programmes that uses knowledge and inference procedure to solve problems that are difficult enough to require significant human expertise for their solutions¡±
In simpler terms:
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Intro/Overview on Machine Learning Presentation -2,
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- 2. What is an Expert System? ? An expert system is computer software that attempts to act like a human expert on a particular subject area. Expert systems are often used to advise non-experts in situations where a human expert in unavailable (for example it may be too expensive to employ a human expert, or it might be a difficult to reach location).
- 3. How Do Expert Systems Work? The non-expert user queries the expert system. This is done by asking a question, or by answering questions asked by the expert system. The inference engine uses the query to search the knowledge base and then provides an answer or some advice to the user.
- 4. A rules base is a set of rules which an inference engine uses, together with the data or facts in the knowledge base, to reason through a problem. A knowledge engineer must be employed in order to create an expert system. The engineer collects information and knowledge from the expert. The engineer also gathers data from other sources. This process is often called data mining. Tell the user what facts and rules were used to find the solution Decides which rule to go with
- 5. The inference engine is able to find solutions by using a form of reasoning. This reasoning involves: FORWARD CHAINING (data driven search) BACKWARD CHAINING (goal driven search) How do we choose between backward or forward inference? Answer: Study how an expert solves a problem. If an expert needs to gather information and then tries to infer, then forward inference is more likely to be suitable. If the expert hypothesizes about solutions, then use backward inference. Attribute Forward Chaining Backward Chaining Also known as Data-driven Goal-driven Starts from New data Possible conclusion Processing Somewhat wasteful Efficient Aims for Any Conclusion(s) Necessary data Practical if Combinatorial explosion creates an infinite number of possible right answers. Number of possible final answers is reasonable or a set of known alternatives is available. Appropriate for Planning, monitoring, control and interpretation application Diagnostic, prescription and debugging application.