Management information systemPresentation Transcript
EXPERT SYSTEM BY:- CHARUDATTA CHANDRASHEKHAR GANGOLI.
INTRODUCTION. An Expert system is a computer application that guides the performance of ill structured task which usually require experience and specialized knowledge (expertise). The Expert system (ES) technology basically derives from the research discipline of Artificial Intelligence a branch of computer science concerned with the design and implementation of computer science concerned with design and implementation of programmes which are capable of emulating human cognitive skills such as problem solving, visual perception and language understanding.
Definition. PETER JACKSON- An Expert System is a computer programme that represent and reasons with knowledge of some specialist subject with a view of solving the problem or giving advice. ROBERT BROWNMAN & DAVID GLOVER- highly specialized computer system capable of simulating that element of human specialist knowledge and reasoning that can be formulate into knowledge chunks characterized by set of facts and heuristic rules.
Inference Engine. User interface. Description of new course. Knowledge Base. Acquisition Facility. user Advice and explanation. Knowledge base. Expert.
APPLICATIONS-: Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts.
Advantages: Provides 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 Can work round the clock Can be used by the user more frequently A multi-user expert system can serve more users at a time
Disadvantages: Lacks common sense needed in some decision making Cannot make creative responses as human expert would in unusual circumstances Domain experts not always able to explain their logic and reasoning Errors may occur in the knowledge base, and lead to wrong decisions Cannot adapt to changing environments, unless knowledge base is changed