Ai with expert system


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Artificial Intelligence

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Ai with expert system

  1. 1. AI With Expert System By:TAUSEEF JAMAL
  2. 2. Contents  Introduction to AI  Overview of AI  Introduction to Expert Systems  Components of Expert Systems  Capabilities of Expert Systems  Participants in Expert Systems  Expert System Development  Evolution of Expert System Software  Advantages and Disadvantages.  When to Use an Expert system
  3. 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 situations. ◦ natural language : programming computers to understand natural human languages 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).
  4. 4. Introduction to Expert Systems In artificial intelligence an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert. Do not follow the procedure of a developer as is the case in conventional Programming. Divided into two parts, one fixed, independent of the expert system: the inference engine, 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 later called "conversational".
  5. 5. Components of Expert Systems 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 Backward chaining : A method of reasoning that starts with conclusions and works backward to the supporting facts Forward chaining : A method of reasoning that starts with the facts and works forward to the conclusions
  6. 6. Participants in Expert System Domain expert : The individual or group whose expertise and knowledge is captured for use in an expert system. Knowledge user :The individual or group who uses and benefits from the expert system. Knowledge engineer : Someone trained or experienced in the design, development, implementation, and maintenance of an expert system.
  7. 7. Expert Systems Development Determining requirements Identifying experts Construct expert system components Implementing results Maintaining and reviewing system Domain • The area of knowledge addressed by the expert system.
  8. 8. Evolution of Expert Systems Software Expert system shell : Collection of software packages & tools to design, Ease of use develop, implement, and maintain expert systems high low Traditional programming languages Before 1980 Special and 4th generation languages 1980s Expert system shells 1990s