Introduction and architecture of expert system
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An expert system is an interactive computer program that uses knowledge acquired from experts to solve complex problems in a specific domain. It consists of an inference engine that applies rules and logic to the facts contained within a knowledge base in order to provide recommendations or advice to users. The first expert system was called DENDRAL and was developed in the 1970s at Stanford University to identify unknown organic molecules. Expert systems are used in applications like diagnosis, financial planning, configuration, and more to perform tasks previously requiring human expertise. They have benefits like increased productivity and quality, reduced costs and errors, and the ability to capture scarce human knowledge. However, they also have limitations such as difficulty acquiring and representing human expertise and an inability to operate outside their
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AI: Learning in AI DataminingTools Inc Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and text-to-speech pronunciation generation. Their architecture is inspired by the human brain but neural networks are trained using computational methods while the brain uses biological processes.
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Expert Systemsosmancikk 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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Topic 8 expert systemNoreliana Md Sharif 1. The document describes an expert system and its components.
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3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
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Introduction and architecture of expert system
- 2. What is Expert System ? An expert system, is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision making problems, based on knowledge acquired from an expert. Inference engine + Knowledge = Expert system ( Algorithm + Data structures = Program in traditional computer ) First expert system, called DENDRAL, was developed in the early 70's at Stanford University.
- 3. INTRODUCTION Expert systems are computer applications which embody some non-algorithmic expertise for solving certain types of problems. For example : Diagnostic applications Play chess Make financial planning decisions Configure computers Monitor real time systems Underwrite insurance policies Perform many services which previously required human expertise.
- 4. Expert System Shells Many expert system s are built with products called expert system shells. A shell is a piece of software which contains the user interface, a format for declarative knowledge in the knowledge base, and an inference engine. The knowledge and system engineers uses these shells in making expert systems.
- 5. Knowledge engineer : uses the shell to build a system for a particular problem domain. System engineer : builds the user interface, designs the declarative format of the knowledge base, and implements the inference engine. Depending on the size of the system, the knowledge engineer and the system engineer might be the same person.
- 6. Human Expert Behaviors Recognize and formulate the problem Solve problems quickly and properly Explain the solution Learn from experience Restructure knowledge Break rules Determine relevance Degrade gracefully
- 7. User Interface Inference Engine Knowledge Base Three Major ES Components
- 8. The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems. Two Basic Knowledge Base Elements Facts & Special heuristics, or rules that direct the use of knowledge The Inference Engine, brain of the ES. The control structure (rule interpreter) Provides methodology for reasoning
- 10. The Client Interface processes requests for service from system-users and from application layer components. The Knowledge-base Editor is a simple editor that enable a subject matter expert to compose and add rules to the Knowledge-base. Rule Translator converts rules from one form to another i.e; their original form to a machine-readable form. The Rule Engine(inference engine) is responsible for executing Knowledge-base rules. The shell component, Rule Object Classes, is a container for object classes supporting.
- 11. Expert System Components And Human Interfaces
- 12. Components and Interfaces User interface : The code that controls the dialog between the user and the system. Knowledge base : A declarative representation of the expertise often in IF THEN rules . Inference engine : The code at the core of the system which derives recommendations from the knowledge base and problem specific data in working storage. Working storage : The data which is specific to a problem being solved.
- 13. Roles of Individuals who interact with the system Domain expert : The individuals who currently are experts in solving the problems; here the system is intended to solve. Knowledge engineer : The individual who encodes the expert's knowledge in a declarative form that can be used by the expert system. User : The individual who will be consulting with the system to get advice which would have been provided by the expert. System engineer : builds the user interface, designs the declarative format of the knowledge base, and implements the inference engine.
- 14. Expert System Benefits Increased Output and Productivity Decreased Decision Making Time Increased Process and Product Quality Reduced Downtime Capture Scarce Expertise Flexibility Easier Equipment Operation Elimination of Expensive Equipment
- 15. Operation in Hazardous Environments Accessibility to Knowledge and Help Desks Integration of Several Experts' Opinions Can Work with Incomplete or Uncertain Information Provide Training Enhancement of Problem Solving and Decision Making Improved Decision Making Processes Improved Decision Quality Ability to Solve Complex Problems Knowledge Transfer to Remote Locations Enhancement of Other MIS
- 16. Expert System Limitations Knowledge is not always readily available Expertise can be hard to extract from humans Each expert’s approach may be different, yet correct Hard, even for a highly skilled expert, to work under time pressure Expert system users have natural cognitive limits ES work well only in a narrow domain of knowledge
- 17. Most experts have no independent means to validate their conclusions Experts’ vocabulary often limited and highly technical Knowledge engineers are rare and expensive Lack of trust by end-users Knowledge transfer subject to a host of perceptual and judgmental biases ES may not be able to arrive at valid conclusions ES sometimes produce incorrect recommendations
- 18. References "Artificial Intelligence", by Elaine Rich and Kevin Knight, (2006), McGraw Hill "Introduction To Artificial Intelligence & Expert Systems", by Dan W Patterson. "Expert Systems: Introduction To First And Second Generation And Hybrid Knowledge Based Systems", by Chris Nikolopoulos. "Artificial intelligence and expert systems for engineers", by C. S. Krishnamoorthy. S. Rajeev, (1996), CRC Press INC, page 1-293.