Expert Systems | IGCSE Information and Communication Technology Preparation - Year 11 PDF Download

Expert Systems

An Expert system is a software application that employs artificial intelligence to mimic the decision-making skills of a human expert within a particular domain.

Purpose of an Expert System

• Solve complex problems:
  • Expert systems are developed to tackle intricate issues that typically necessitate human expertise.
  • For instance, in healthcare, an expert system can assist in diagnosing diseases by analyzing symptoms and medical history.
•  Enhance decision-making: 
  • They aid in improving decision-making processes by offering well-informed suggestions based on available data.
  • In finance, expert systems can recommend investment opportunities by analyzing market trends and risk factors.
•  Save time and resources:
  • Expert systems have the capability to swiftly process large volumes of data, thereby reducing the time and effort needed by human professionals.
  • For instance, in manufacturing, these systems can optimize production schedules by analyzing various factors.
• Consistency and accuracy: 
  • By eliminating human errors and biases, expert systems ensure reliable and precise outcomes consistently.
  • In legal practices, these systems can help in analyzing cases and providing relevant precedents for lawyers.
• Knowledge preservation: 
  • They store and retain the expertise of professionals, ensuring that valuable knowledge is not lost when experts retire or are unavailable.
  • In education, expert systems can provide personalized learning paths for students based on their abilities and learning styles.

Uses of Expert Systems

• Mineral Prospecting: Expert systems can analyze geological data to identify potential locations for mineral deposits.
• Car Engine Fault Diagnosis: These systems are capable of diagnosing issues within engine components and suggesting repair options and maintenance schedules.
• Medical Diagnosis: By analyzing patient symptoms and medical history, expert systems can propose possible diagnoses and treatment plans.
• Chess Games: Expert systems can evaluate potential moves based on the current game state and strategize to enhance the chances of winning.
• Financial Planning: These systems assess investment options and risks to provide personalized financial advice.
• Route Scheduling for Delivery Vehicles: Expert systems calculate optimal routes considering factors like distance, traffic, and time constraints to enhance efficiency and reduce fuel consumption.
• Plant and Animal Identification: By analyzing physical characteristics and habitat data, expert systems can identify species and offer relevant information.
• Career Recommendations: Through a series of questions and analysis of existing qualifications, these systems can provide guidance on suitable career choices.

Components of an Expert System

• User Interface:
  • Allows users to engage with the expert system
  • Facilitates data input and the reception of recommendations or solutions
  • Designed to be user-friendly and easily accessible
• Inference Engine:
  • Core component conducting logical reasoning
  • Applies rules from the rules base to data from the knowledge base
  • Replicates human decision-making to draw conclusions
• Knowledge Base:
  • Depository for domain-specific information, facts, and data
  • Comprises expertise sourced from human experts or other reliable channels
  • Crucial for the inference engine to provide accurate recommendations
• Rules Base:
  • Repositories logical rules and domain relationships
  • Guides the inference engine in applying reasoning to the data
  • Rules can be altered or updated with new information
• Explanation System:
  • Ensures transparency in the decision-making process
  • Offers detailed rationales for the expert system's decisions and conclusions
  • Enhances user trust and comprehension of the system's recommendations

How an Expert System is Used to Produce Possible Solutions

• Expert systems utilize a knowledge base and rules base to analyze input data.
• The inference engine applies rules and logic to the input data for decision-making.
• Based on the applied rules, the system generates potential solutions or recommendations.
• The explanation system effectively communicates the reasoning behind the suggested solutions to users.