A knowledge-based system (KBS) is a program that captures and uses knowledge from a variety of sources. A KBS assists with solving problems, particularly complex issues, by artificial intelligence. These systems are primarily used to support human decision making, learning, and other activities.
A knowledge-based system is a major area of artificial intelligence. These systems can make decisions based on the data and information that resides in their database. In addition, they can comprehend the context of the data being processed.
A knowledge-based system is comprised of a knowledge base and an interface engine. The knowledge base functions as the knowledge repository, while the interface engine functions as the search engine. Learning is a key element to a knowledge-based system, and learning simulation improves the system over time. Knowledge-based systems are categorized as expert systems, intelligent tutoring systems, hypertext manipulations systems, CASE-based systems, and databases having an intelligent user interface.
Knowledge-Based Systems and Artificial Intelligence
While these systems are a subcategory of artificial intelligence, traditional knowledge-based systems are different in certain ways from AI. In some ways, AI is organized in a top-down, know everything system to capture and utilize statistical pattern detection methods, big data, deep learning, and data-mining. Examples of AI include approaches that involve neural network systems, which are a category of deep learning technology concentrated on pattern recognition and signal processing.
In contrast to conventional computer-based information systems, a KBS has several advantages. They provide excellent documentation while handling large quantities of unstructured data in an intelligent way. A KBS helps improve decision making and enables users to work at greater levels of expertise, productivity, and consistency. In addition, a KBS is useful when expertise is not available, or when information must be stored effectively for future use. It also provides a common platform for integrating knowledge on a large scale. Finally, a KBS is capable of generating new knowledge by using the stored data.
The architecture of a knowledge-based system is its inference engine and knowledge base. The knowledge base holds a collection of data, and the inference engine can deduce insights from the data stored in the knowledge base.
Knowledge-based systems work across a number of applications. For instance, in the medical field, a KBS can help doctors more accurately diagnose diseases. These systems are called clinical decision-support systems in the health industry. A KBS can also be used in areas as diverse as industrial equipment fault diagnosis, avalanche path analysis, and cash management.
Types of Knowledge-based Systems
Here’s a list of the types of knowledge-based systems, based on the resources provided:
- Expert Systems: Simulate human expertise in a specific domain for decision-making.
- Case-Based Systems: Solve new problems based on solutions to past cases.
- Rule-Based Systems: Use “if-then” rules for decision-making.
- Blackboard Systems: Integrate data from multiple sources to solve problems.
- Classification Systems: Categorize data into predefined groups.
- Intelligent Tutoring Systems: Provide personalized learning support.
- Medical Diagnosis Systems: Assist in diagnosing patients based on input data.
Advantages of Knowledge-based Systems
Enhanced Decision-Making: KBS leverages expert-level knowledge, improving accuracy in decision-making by providing reliable, data-driven insights. This reduces reliance on human intuition or guesswork.
Efficiency Improvement: By automating complex processes, such as diagnostics or planning, KBS minimizes manual effort, streamlining workflows, and reducing time spent on problem-solving.
Knowledge Preservation: Captures and stores the expertise of skilled individuals, ensuring critical knowledge is retained within the system even if the experts leave or retire. This creates a lasting knowledge repository.
Consistency: Since KBS operates using predefined rules and logic, it applies the same processes uniformly across various scenarios, ensuring consistent output. This reduces variability that may arise from human judgment, leading to more reliable results.
Scalability: They can handle large amounts of data or numerous simultaneous requests without degrading performance. This allows organizations to scale their operations without needing proportionate increases in human resources.
