The architecture of a knowledge-based system (KBS) refers to the overall structure and components that make up the system. A KBS is an AI system that utilizes knowledge representation, reasoning, and inference techniques to solve problems, make decisions, and provide expert advice in a specific domain. The architecture of a KBS typically consists of the following components:
- Knowledge Base: The knowledge base is the central component of a KBS, where domain-specific knowledge is stored. It contains the facts, rules, heuristics, and other forms of knowledge that the system uses to reason and make decisions. The knowledge base is typically represented using knowledge representation formalisms such as logic-based rules, frames, ontologies, or semantic networks.
- Inference Engine: The inference engine is responsible for performing reasoning and inference over the knowledge base. It processes the input data or queries, applies the rules and knowledge in the knowledge base, and derives new information or makes conclusions. The inference engine employs various inference mechanisms, such as rule-based reasoning, forward or backward chaining, constraint satisfaction, or probabilistic reasoning, depending on the nature of the problem and the available knowledge.
- User Interface: The user interface allows users to interact with the KBS, providing input data, posing queries, and receiving outputs or recommendations. The user interface can be text-based, graphical, or even natural language-based, depending on the design and requirements of the system. The interface should be user-friendly and provide effective communication between the user and the KBS.
- Explanation and Justification: Explanation and justification mechanisms are crucial for enhancing the transparency and trustworthiness of a KBS. These mechanisms provide explanations for the system’s recommendations, decisions, or actions, allowing users to understand the underlying reasoning and the basis for the system’s outputs. This helps build user confidence and acceptance of the system.
- Knowledge Acquisition: Knowledge acquisition involves the process of acquiring and updating the knowledge base of the KBS. It may include capturing knowledge from domain experts, literature, databases, or other sources. Knowledge acquisition tools and techniques facilitate the extraction, representation, and integration of knowledge into the KBS.
- Knowledge Engineering Tools: Knowledge engineering tools assist in the development, maintenance, and testing of the KBS. These tools support activities such as knowledge base authoring, rule editing, debugging, validation, and performance analysis. They help knowledge engineers or domain experts in managing and refining the knowledge base and ensuring the correctness and efficiency of the KBS.
- Domain Model: The domain model represents the concepts, relationships, and constraints specific to the problem domain that the KBS addresses. It captures the domain-specific knowledge and provides a conceptual framework for organizing the knowledge base and reasoning about the problem domain. The domain model helps structure the knowledge and facilitates accurate representation and reasoning within the KBS.
The specific architecture of a KBS can vary depending on the problem domain, the available knowledge representation techniques, and the requirements of the system. The architecture should be designed to support effective knowledge representation, efficient reasoning, user interaction, and transparent explanations to ensure the system’s functionality, usability, and reliability.
What are knowledge-based systems?
Knowledge-based systems (KBSes) are computer programs that use a centralized repository of data known as a knowledge base to provide a method for problem-solving. Knowledge-based systems are a form of artificial intelligence (AI) designed to capture the knowledge of human experts to support decision-making. An expert system is an example of a knowledge-based system because it relies on human expertise.
KBSes can assist in decision-making, human learning and creating a companywide knowledge-sharing platform, for example. KBS can be used as a broad term, but these programs are generally distinguished by representing knowledge as a reasoning system to derive new knowledge.
A basic KBS works using a knowledge base and an interface engine. The knowledge base is a repository of data that contains a collection of information in a given field — such as medical data. The inference engine processes and locates data based on requests, similar to a search engine. A reasoning system is used to draw conclusions from data provided and make decisions based on if-then rules, logic programming or constraint handling rules. Users interact with the system through a user interface.
What are knowledge-based systems used for?
Knowledge-based systems are commonly used to aid in solving complex problems and to support human learning. KBSes have been developed for numerous applications. For example, an early knowledge-based system, Mycin, was created to help doctors diagnose diseases. Healthcare has remained an important market for knowledge-based systems, which are now referred to as clinical decision support systems in the health sciences context.
Knowledge-based systems have also been used in applications as diverse as avalanche path analysis, industrial equipment fault diagnosis and cash management.
