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Questions and Answers
Which of these components is considered an accessory to an expert system?
Which of these components is considered an accessory to an expert system?
Which component of an Expert System stores facts about the specific problem being addressed during a session?
Which component of an Expert System stores facts about the specific problem being addressed during a session?
What is the primary role of the Inference Engine in an Expert System?
What is the primary role of the Inference Engine in an Expert System?
Which component of an Expert System focuses on gathering and storing domain expertise for the system to utilize?
Which component of an Expert System focuses on gathering and storing domain expertise for the system to utilize?
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What is the primary function of the 'Why' facility in an Expert System?
What is the primary function of the 'Why' facility in an Expert System?
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How is the inference engine used in a knowledge base?
How is the inference engine used in a knowledge base?
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What is the main difference between backward and forward chaining?
What is the main difference between backward and forward chaining?
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What is the main goal of an inference engine when it is used in a backward chaining approach?
What is the main goal of an inference engine when it is used in a backward chaining approach?
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Which of the following is NOT a characteristic of an Expert System (ES)?
Which of the following is NOT a characteristic of an Expert System (ES)?
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What is the most effective way to improve the search efficiency in problem-solving?
What is the most effective way to improve the search efficiency in problem-solving?
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Study Notes
Expert System Architecture
- Expert systems aim to mimic human expert decision-making.
- Architecture is the science and method to design structure of expert systems.
- Core components include knowledge base, working memory, and inference engine.
- Accessory components include user interface, knowledge acquisition, and explanation module.
Core Components
- Knowledge Base (KB): Stores domain-specific knowledge. Contains facts, rules, and heuristics.
- Working Memory (WM): Stores temporary information during problem-solving. Holds facts relevant to the current problem.
- Inference Engine (IE): Processes information from the KB and WM. Derives conclusions to solve problems. This engine applies rules to find solutions.
Accessory Components
- User Interface: Enables interaction between user and the system.
- Knowledge Acquisition: Acquires domain knowledge from human experts. This is crucial for the accuracy and usefulness of the system.
- Explanation Facility: Explains the system's reasoning process and conclusions. This helps the user understand the system's decisions.
Expert System Definitions
- Knowledge Base (KB): Contains general knowledge.
- Working Memory (WM): Stores facts gathered by inference.
- Inference Engine (IE): Analyzes the KB and WM to reach conclusions. Uses rules and facts to reach a conclusion. This is the "reasoning" component.
Explanation Facilities
- How Facility: Shows how the system arrived at a specific conclusion, often illustrating how a conclusion is reached.
- Why Facility: Explains why a specific question was asked. Shows the relationship between the question and problem-solving strategy.
General aspects
- Expert systems need a substantial knowledge base for solving complex problems.
- They rely on efficient access to and application of this knowledge.
Inference Engine
- A computer program that directs the use of a knowledge base.
- Separates knowledge from the control process of problem-solving in the knowledge base.
- Can handle decision-making through goal-driven (backward chaining) or data-driven (forward chaining) methods.
Reasoning Mechanisms
- Backward Chaining: Starts with a potential solution (goal). The system checks the knowledge base for rules relevant to the goal. Continues until a solution or contradiction is found.
- Forward Chaining: Starts with known facts and rules. The system applies rules based on the facts until it reaches a goal or solution.
Characteristics of Expert Systems
- Separates knowledge from the control process.
- Possesses expert knowledge.
- Reasons using symbols and heuristics.
- Permits inexact reasoning.
- Limited to solvable problems.
- Works well with problems that have a reasonable level of complexity.
- Can sometimes make mistakes.
Intelligent Problem Solving
- Key to success is efficient access and application of knowledge.
- Knowledge includes facts, beliefs, and heuristics.
- A successful problem-solving process depends significantly on how well knowledge is structured and accessed.
- Factors like the quality of data, the number of potential solutions, and the complexity of procedures also affect problem solving.
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Description
Explore the fundamental components of expert systems, which are designed to replicate human decision-making processes. This quiz covers both the core components, such as the knowledge base and inference engine, and the accessory components essential for user interaction. Enhance your understanding of how these systems function and their applications.
