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Expert system

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Expert system

  1. 1. A SEMINAR PRESENTATION ON “EXPERT SYSTEM” Seminar Guide Submitted By: Mr. Mahendra Singh Sagar Deepak Kumar Assistant Professor Roll No.: TCA1405019 Master of Computer Application 4th Sem. (LT) COLLEGE OF COMPUTING SCIENCE AND INFORMATION TECHNOLOGY (Teerthanker Mahaveer University, Delhi Road, Moradabad – 244001)
  2. 2. INTRODUCTION An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain. 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, represented primarily as if-then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software.
  3. 3. HISTORY Edward Feigenbaum in a 1977 paper said that the key insight of early expert systems was that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" (as paraphrased by Hayes-Roth, et al.) Although, in retrospect, this seems a rather straight forward insight, it was a significant step forward at the time. Until then, research had been focused on attempts to develop very general-purpose problem solvers such as those described by Newell and Simon. Expert systems were introduced by the Stanford Heuristic Programming Project led by Feigenbaum, who is sometimes referred to as the "father of expert systems".
  4. 4. SOFTWARE ARCHITECTURE
  5. 5. CONTINUE  Truth Maintenance. Truth maintenance systems record the dependencies in a knowledge-base so that when facts are altered dependent knowledge can be altered accordingly. For example, if the system learns that Socrates is no longer known to be a man it will revoke the assertion that Socrates is mortal.  Hypothetical Reasoning. In hypothetical reasoning, the knowledge base can be divided up into many possible views, a.k.a. worlds. This allows the inference engine to explore multiple possibilities in parallel.
  6. 6. CONTINUE  Fuzzy Logic. One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule.  Ontology Classification. With the addition of object classes to the knowledge base a new type of reasoning was possible. Rather than reason simply about the values of the objects the system could also reason about the structure of the objects as well.
  7. 7. WHAT IS EXPERT SYSTEM In AI, 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, represented primarily as if-then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software.
  8. 8. SECURITY Experts in the field of computer security can work in a variety of positions, including those as network and computer systems administrators or information security analysts. Network and computer systems administrators maintain computer and network security and update security programs as necessary. Information security analysts develop an organization's computer security standards; install software programs to protect information stored on computers and monitor computer networks for security breaches.
  9. 9. TYPES OF EXPERT SYSTEM COMPONENTS A computer program designed to model the problem-solving ability of a human expert. 1. A knowledge base that contains the knowledge obtained from one or more experts, generally in the form of rules. 2. An inference engine that manipulates the knowledge found in the knowledge base to arrive at a solution. 3. A user interface that allows the user to query the system and obtain the solution. 4. An explanation facility that explains the working of the system: how the rules were derived, applied, and sometimes the confidence levels that can be attached to the results.
  10. 10. CONTINUE Chaining Inference rules are may forward chaining and backward chaining. Forward chaining starts with the data available, and uses the inference rules to extract more data until a desired goal is reached. Backward chaining starts with a list of goals and works backwards to see if data exist which will allow it to conclude that any of these goals is true.
  11. 11. CONTINUE Real-time Adaption Real-time expert systems, designed to adapt over time to changing input data, are commonly necessary in process control, network management and other dynamic systems. Learning Capabilities Expert systems that learn from a storied history of successful and failed solutions are more reliable, but can be challenging to program.
  12. 12. ADVANTAGE AND DISADVANTAGE ADVANTAGE Consistent answers for repetitive decisions, processes and tasks Holds and maintains significant levels of information Encourages organizations to clarify the logic of their decision-making Never "forgets" to ask a question, as a human might
  13. 13. CONTINUE DISADVANTAGE Lacks common sense Cannot make creative responses as human expert Domain experts not always able to explain their logic and reasoning Errors may occur in the knowledge base Cannot adapt to changing environments

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