"Expert Systems are interactive, trustworthy computer-based decision-making tools that utilize data and heuristics to address challenging decision-making issues. It is regarded as representing the pinnacle of human knowledge and wisdom. An expert system's job is to resolve the trickiest problems in a specific field."
An expert system is a software that provides knowledge of an expert to a less experienced person in the form of a dialog. Expert systems work rule-based, with an inference engine. Rules and propositions can have graded truth values, such as probabilities or as in fuzzy logic. The underlying knowledge is in a formal language and was accumulated by a so-called knowledge enterer who interviewed the expert, and who is different from the user of the expert system. Expert systems are deployed in the form of an expert system shell, which has a user interface, a knowledge base, an inference engine, and a component that allows for entering new knowledge.
An expert system in AI is a computer program designed to mimic the decision-making ability of a human expert in a specific domain. Here are some important traits of expert systems:
Knowledge Base: Expert systems contain a knowledge base that stores domain-specific information, facts, rules, and heuristics. This knowledge represents the expertise of human specialists in a particular field.
Inference Engine: The inference engine is the reasoning component of an expert system. It uses the knowledge base to draw conclusions, make decisions, and solve problems based on the input provided by users or sensors.
Rule-Based System: Expert systems often operate on a rule-based paradigm, where a set of "if-then" rules guides the decision-making process. These rules encode the logical relationships within the domain.
User Interface: Expert systems include a user interface to facilitate interaction with end-users. This interface allows users to input information, receive advice, and understand the system's reasoning.
Explanation Facility: Many expert systems provide an explanation facility to justify their conclusions. This helps users understand how the system arrived at a specific decision, enhancing transparency and trust.
Learning Capability: Some expert systems incorporate learning mechanisms to improve their performance over time. This may involve updating the knowledge base based on user feedback or adapting to changing conditions in the domain.
Limited Domain: Expert systems are designed for specific domains of expertise. They excel in well-defined, narrow areas but may struggle when faced with problems outside their designated scope.
Symbolic Reasoning: Expert systems often rely on symbolic reasoning rather than numeric computation. They manipulate symbols and use logic to derive conclusions, making them suitable for knowledge-intensive tasks.
Problem-Solving Ability: Expert systems are built to solve complex problems by applying human-like reasoning processes. They excel in tasks that involve decision-making, diagnosis, planning, and interpretation of information.
Expert systems have been applied in various fields, including medicine, finance, engineering, and troubleshooting, providing valuable insights and recommendations within their specified domains of expertise.
An artificial Intelligence based system which infere its decision on the basis of expert expert system generally consists of four components: a knowledge base, the search or inference system, a knowledge acquisitionn expert system is system, and the user interface or communication system. reasons on the basis of knowledge it has.
An expert system is a branch of AI, it's a computer program designed to emulate the decision-making ability of a human expert in a specific domain. it uses a knowledge base, which contains rules and facts, and an inference engine to reason and make decisions based on the input it receives.