Knowledge-Based Systems (KBS) are computer programs that use knowledge and inference to solve complex problems that typically require human expertise. They consist of a knowledge base, which contains domain-specific knowledge, and an inference engine, which applies logical rules to the knowledge base to deduce new information or make decisions.
1- Knowledge Base: A repository of facts, rules, and relationships about a specific domain.
2- Inference Engine: The core component that applies logical rules to the knowledge base to deduce new information or make decisions.
3- User Interface: Allows users to interact with the system, input data, and receive outputs or recommendations.
4- Explanation Facility: Provides users with explanations of the reasoning process behind the system's conclusions or recommendations.
5- Learning Component: Some KBS can learn from new data and experiences to improve their performance over time.
Applications of KBS include medical diagnosis, financial forecasting, troubleshooting, and more. They are valuable in areas where expert knowledge is scarce or expensive to obtain.
1- Expert Systems: These are designed to emulate the decision-making ability of a human expert. They are often used in fields like medicine (e.g., diagnosing diseases) and engineering (e.g., fault diagnosis).
2- Decision Support Systems (DSS): These systems help in making decisions by analyzing data and providing recommendations. They often integrate data from various sources.
3- Natural Language Processing (NLP) Systems: These systems understand and process human language, allowing users to interact with the KBS in a more intuitive way.
4- Fuzzy Logic Systems: These systems handle uncertainty and imprecision, making them useful in situations where binary true/false logic is insufficient.
1- Knowledge Representation: This involves how knowledge is stored and organized. Common methods include:
1- Semantic Networks: Graph structures representing knowledge in terms of entities and their relationships.
2- Frames: Data structures for representing stereotypical situations.
3- Rules: If-then statements that define the logic of the system.
2- Knowledge Acquisition: The process of gathering and refining knowledge from various sources, including human experts, databases, and literature.
3- Reasoning Mechanisms: Techniques used by the inference engine to derive conclusions from the knowledge base. Common methods include:
1- Forward Chaining: Starting with known facts and applying rules to infer new facts.
2- Backward Chaining: Starting with a goal and working backward to see if the known facts support it.
1- Consistency: They provide consistent answers for repetitive decisions, reducing human error.
2- Availability: KBS can be available 24/7, providing support whenever needed.
3- Scalability: They can handle large amounts of data and complex decision-making processes.
4- Cost-Effectiveness: Reduces the need for human experts in certain areas, saving costs.
1- Knowledge Acquisition Bottleneck: Gathering and structuring knowledge can be time-consuming and difficult.
2- Maintenance: Keeping the knowledge base up-to-date with new information can be challenging.
3- Complexity: Developing a KBS can be complex, requiring significant expertise in both the domain and system design.
4- User Trust: Users may be hesitant to rely on automated systems for critical decisions.
1- Healthcare: Diagnosis and treatment recommendations.
2- Finance: Risk assessment and investment analysis.
3- Manufacturing: Process control and quality assurance.
4- Customer Support: Automated troubleshooting and FAQs.
Knowledge-Based Systems continue to evolve with advancements in artificial intelligence, machine learning, and data analytics, making them increasingly powerful tools across various industries.
Read more from here (MIT OpenCourseWare) : https://ocw.mit.edu/courses/6-871-knowledge-based-applications-systems-spring-2005/pages/lecture-notes/