Expert Systems Overview Quiz

Introduction to Expert Systems

• Expert systems are a type of artificial intelligence (AI) software designed to mimic the decision-making ability of human experts in a specific area.
• They attempt to replicate the knowledge and skills of human experts in a particular field to solve problems.
• Eleanor Roosevelt quote: "With the new day comes new strength and new thoughts. Either you run the day or the day runs you."

Agenda

• Part A: Introduction to expert systems and their elements.
• Part B: Expert system applications and domains, Development of expert systems, Advantages of expert systems, and Expert System Tools
• Part C: Design of Expert Systems

Introduction

• Expert systems are AI software that use knowledge stored in a knowledge base to solve complex problems that often require a human expert.
• They retain human expertise in their knowledge base.
• They provide advice and explanations.
• The process of developing an expert system is called Knowledge Engineering and the practitioners are Knowledge Engineers.

Historical Background

• 1943: Post proved that any computable problem can be solved using IF-THEN rules.
• 1961: Development of General Problem Solver (GPS) by Newell and Simon.
• 1969: DENDRAL, a system showing the importance of domain-specific knowledge.
• 1970s: MYCIN, a medical diagnosis system introducing certainty factors.
• 1982: R1 (XCON) was the first commercial expert system, saving DEC $40 million annually by 1986.

Why Use Expert Systems?

• Today's world needs more experts in rapidly evolving technologies.
• Expert systems in AI let computers replicate the knowledge and skills of human experts.
• Maximum efficiency, reliability, high-level understandability, and unbeatable performance are significant advantages.

Human Experts vs Expert Systems

Understanding Expert Systems

• Expert systems are AI software that uses knowledge stored in a knowledge base to solve complex problems.
• They preserve expertise within their knowledge base.
• They can advise users.
• They can offer explanations on how conclusions were formed
• The process of building an expert system is knowledge engineering.
• Knowledge Engineers build expert systems.
• Programming ensures all necessary knowledge is available to the computer for problem-solving.

Characteristics of Expert Systems

• Human experts have limits, expert systems can be permanent.
• Expert systems can distribute human expertise.
• Expert systems can incorporate knowledge from multiple experts leading to better answers.
• Expert systems minimize the cost of seeking expert advice.
• Instead of using code, expert systems infer new details from known information (if-then rules).

Types of Expert Systems

• Rule-based systems: use rules to arrive at solutions. Example: MYCIN, diagnosing bacterial infections.
• Fuzzy Logic systems: handle uncertainty and imprecision in data using fuzzy logic. Example: washing machines and air conditioners.
• Knowledge-based systems: store facts and rule about a specific domain. Example: Natural Language Processing (NLP)
• Neural network systems: learn from data by adjusting weights of their connections between neurons. Example: image and speech recognition.
• Neuro-Fuzzy systems: combine neural networks and fuzzy logic to handle complex problems with uncertain reasoning. Example: environmental conditions or financial forecasting models.

Components and Architecture of an Expert System

• Knowledge Base: Stores facts, rules, and procedures specific to a domain that helps solve a problem.
• Inference Engine: Fetches knowledge, interprets knowledge to address a user's problem, and applies rules to known facts to infer new facts. Explanation and debugging capabilities may also be available.
• Knowledge Acquisition and Learning Module: Accepts new knowledge from multiple sources and stores acquired knowledge within the knowledge base.
• User Interface: Enables interaction between the non-expert user and the system to obtain solutions.
• Explanation Module: Provides explanations to users on how the system arrives at its conclusions.

How Expert Systems Work

• Input Data: Users provide data or queries related to a specific problem.
• Processing: The inference engine processes input data using the rules defined in the knowledge base to generate conclusions or recommendations.
• Output: Results or solutions are displayed to the user through the user interface.
• Explanation: The system may explain the reasoning process used to arrive at its conclusions.

Reasoning Strategies

• Forward Chaining: Starts with available facts, applies rules to get conclusions. Used to predict outcomes.
• Backward Chaining: Starts with a hypothesis or goal and works backward to determine the conditions required to support that goal. Used in diagnosing medical conditions.

Applications and Domains of Expert Systems

• Medical diagnosis: Assists doctors with disease diagnosis from symptoms and patient data.
• Financial services: Used for loan approvals, investment analysis, and fraud detection.
• Customer support: Provide automated customer service by answering queries.
• Manufacturing and Production: Optimizing production processes and troubleshooting equipment problems.
• Weather forecasting: Analyzes meteorological data to create accurate weather predictions.
• Engineering design: Assists in design problems and technical solutions.
• Education and Training: Intelligent tutoring systems to provide personalized learning experiences.
• Agriculture: Managing crop planning, pest control, and soil management.
• Legal advisory: Provides legal expertise by analyzing laws and past cases.

Development of Expert Systems

• Problem identification: Defining the problem domain, scope, and nature of the problem the system will solve.
• Knowledge acquisition: Gathering knowledge from experts, resources, and other sources. Includes interviewing experts to collect rules, facts, and problem-solving strategies.
• Knowledge representation: Selecting an appropriate form for knowledge representation, such as rules, semantic networks, and frames.
• Building the knowledge base: Encoding collected knowledge into a structured format (facts and rules).
• Development of inference engine: Designing an inference mechanism based on the knowledge base to apply rules and reason. Includes supporting forward or backward chaining.
• Integration of explanation facility: Adding an explanation module to justify system reasoning.
• User interface design: Creating easy-to-use interface for non-expert users.
• Testing and validation: Evaluating accuracy and performance by comparing results to expert human decisions.
• Knowledge refinement and maintenance: Updating the knowledge base to accommodate new information regularly.

Advantages of Expert Systems

• Consistent decision-making
• Availability
• Cost-effective
• Speed and efficiency
• Preservation of expertise
• Handling of complex data
• Training and education support
• Reduced error rates
• Scalability
• Explanation and justification
• Absence of emotional influence

Disadvantages of Expert Systems

• Lack of common sense
• Limited to specific domains
• Inability to learn or adapt
• High development costs
• Difficulty in knowledge acquisition
• Rigid decision-making
• Absence of emotional intelligence
• Maintenance and updates
• Unsuitable for dynamic environments

Test your knowledge on expert systems, their advantages, disadvantages, and applications in various fields. This quiz covers fundamental concepts and historical milestones, helping you deepen your understanding of decision-making technologies.