Title expert system.docx ✅

What is an Expert System?  An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert.  It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries. Below are some popular examples of the Expert System: CaDeT: The CaDet expert system is a diagnostic support system that can detect cancer at early stages. PXDES: It is an expert system that is used to determine the type and level of lung cancer. Characteristics of Expert System  High Performance: The expert system provides high performance for solving any type of complex problem of a specific domain with high efficiency and accuracy.  Understandable: It responds in a way that can be easily understandable by the user. It can take input in human language and provides the output in the same way.  Reliable: It is much reliable for generating an efficient and accurate output.  Highly responsive: ES provides the result for any complex query within a very short period of time. Architecture of an Expert System
Knowledge Base – The knowledge base represents facts and rules. It consists of knowledge in a particular domain as well as rules to solve a problem, procedures and intrinsic data relevant to the domain. Inference Engine – The function of the inference engine is to fetch the relevant knowledge from the knowledge base, interpret it and to find a solution relevant to the user’s problem. The inference engine acquires the rules from its knowledge base and applies them to the known facts to infer new facts. Inference engines can also include an explanation and debugging abilities. Knowledge Acquisition and Learning Module – The function of this component is to allow the expert system to acquire more and more knowledge from various sources and store it in the knowledge base. User Interface – This module makes it possible for a non-expert user to interact with the expert system and find a solution to the problem. Explanation Module – This module helps the expert system to give the user an explanation about how the expert system reached a particular conclusion. The Inference Engine generally uses two strategies for acquiring knowledge from the Knowledge Base, namely – Forward Chaining – Forward Chaining is a strategic process used by the Expert System to answer the questions – What will happen next. This strategy is mostly used for managing tasks like creating a conclusion, result or effect. Example – prediction or share market movement status.
Backward Chaining – Backward Chaining is a storage used by the Expert System to answer the questions – Why this has happened. This strategy is mostly used to find out the root cause or reason behind it, considering what has already happened. Example – diagnosis of stomach pain, blood cancer or dengue, etc. Steps to Develop an Expert System: Step1: Identification: Determining the characteristics of the problem.
Step2: Conceptualization: Finding the concept to produce the solution. Step3: Formalization: Designing structures to organize the knowledge. Step4: Implementation: Formulating rules which embody the knowledge. Step5: Testing: Validating the rules. Testing includes are: i. The system implements correctly or incorrectly. ii. Rules implement correctly or not. iii. The System uses for testing for both simple and complex problems by domain experts to uncover more defects. iv. An Expert System is finally tested to be successful only when it is operated at the level of a human expert.