Conversational Expert System // GP // Dr. Hesham Mansour (2018 - 2019)
Mohamed Lashein 183361
Conversational Expert System // GP // Dr. Hesham Mansour (2018 - 2019) - GIZA MSA 2019 - 62 P. - COMPUTER SCIENCES DISTINGUISHED PROJECTS 2019 .
Computer Science
Expert Systems, a computer program that uses artificial intelligence techniques to solve
problems within a specialized field that usually requires human experience. Expert systems
rely on two components: the knowledge base and the inference engine. Knowledge Base is an
organized set of facts about the scope of the system. The inference engine interprets and
evaluates the facts in the knowledge base in order to provide an answer. Typical functions of
expert systems include classification, diagnostics, monitoring, design, scheduling and
planning of specialized endeavors. A knowledge base for a system contains thousands of
rules. Probability is often attached to each output or output from the system, because the
conclusion is not certain. For example, a system for the diagnosis of eye diseases, based on
the information provided to it, may indicate a 90% probability that a person has a blueness
and may also include conclusions with lower probability. An expert system may also show the
sequence of rules through which it has come to an end; tracking this flow helps the evaluator
evaluate the credibility of conclusions and recommendations and also serves as a learning tool
for students.
Human experts often use heuristic rules, or "thumb rules", as well as simple production rules,
such as rules derived from geometric books. Thus, the credit manager of a particular
insurance company may know that an applicant with a bad credit record, but who has a clean
record since acquiring a new job, may actually have a good credit risk.
Conversational Expert--Computer Science
005
Conversational Expert System // GP // Dr. Hesham Mansour (2018 - 2019) - GIZA MSA 2019 - 62 P. - COMPUTER SCIENCES DISTINGUISHED PROJECTS 2019 .
Computer Science
Expert Systems, a computer program that uses artificial intelligence techniques to solve
problems within a specialized field that usually requires human experience. Expert systems
rely on two components: the knowledge base and the inference engine. Knowledge Base is an
organized set of facts about the scope of the system. The inference engine interprets and
evaluates the facts in the knowledge base in order to provide an answer. Typical functions of
expert systems include classification, diagnostics, monitoring, design, scheduling and
planning of specialized endeavors. A knowledge base for a system contains thousands of
rules. Probability is often attached to each output or output from the system, because the
conclusion is not certain. For example, a system for the diagnosis of eye diseases, based on
the information provided to it, may indicate a 90% probability that a person has a blueness
and may also include conclusions with lower probability. An expert system may also show the
sequence of rules through which it has come to an end; tracking this flow helps the evaluator
evaluate the credibility of conclusions and recommendations and also serves as a learning tool
for students.
Human experts often use heuristic rules, or "thumb rules", as well as simple production rules,
such as rules derived from geometric books. Thus, the credit manager of a particular
insurance company may know that an applicant with a bad credit record, but who has a clean
record since acquiring a new job, may actually have a good credit risk.
Conversational Expert--Computer Science
005