Demystifying Knowledge-Based Systems: A Beginner’s Guide

Knowledge-based systems (KBS) have become an integral part of our technologically driven world, yet many people don’t really understand what they are or how they work. As an avid tech follower and blogger, I want to lift the veil on these seemingly complex systems and explain KBS in simple terms even a newbie can grasp!

What Exactly is a Knowledge-Based System?

A knowledge-based system is a type of artificial intelligence program that uses a centralized database, called a knowledge base, to solve problems that would normally require human expertise. The knowledge base contains facts, rules and procedures specific to the problem domain. For example, a knowledge base for medical diagnosis would contain medical facts and rules useful for diagnosing patients.

The KBS uses an inference engine to extract new knowledge from the knowledge base You can think of the inference engine as the brains of the operation – it analyzes the data in the knowledge base using rules and logic to come up with solutions The user interacts with the KBS via a user interface that explains the solutions in a human-readable format,

Key Components of a KBS

There are two key components that make up a knowledge-based system

1. Knowledge Base

As mentioned above, this is the database of domain-specific information that the KBS uses to formulate solutions. It can contain different types of knowledge, including:

• Facts – statements considered true in the domain. For example, “High blood pressure can cause strokes.”

• Rules – Conditional statements linking facts. For example, “If blood pressure is > 140/90 mmHg, then the patient has hypertension.”

• Procedures – Step-by-step instructions for solving specific problems. For example, how to interpret blood test results.

The knowledge is typically represented using formats like production rules, semantic networks, frames or logic.

2. Inference Engine

This is the reasoning component of the KBS that draws conclusions from the knowledge base. It applies problem solving logic, either forward chaining, backward chaining or a combination of both, to provide solutions and explanations.

Forward chaining starts with the available data and uses inference rules to extract more data until a goal is reached. Backward chaining starts with the desired goal and works backward to determine if the data supports that goal.

Areas Where KBS are Used

Some common applications of knowledge-based systems include:

• Medical diagnosis – Using patient symptoms and medical knowledge to diagnose diseases.

• Technical support – Troubleshooting problems with computers, networks, or mechanical devices using domain knowledge.

• Financial services – Approving loans, detecting fraud and managing portfolios by analyzing financial data.

• Education – Creating intelligent tutoring systems that adapt to students’ learning needs.

• Gaming – Providing intelligent opponents that play against human players.

Pretty much any domain that relies on specialized knowledge and expertise is a potential application for knowledge-based systems. Their ability to mimic human-level reasoning makes them excellent options for fields like healthcare, tech support, and education.

Advantages of KBS

Knowledge-based systems offer some key benefits:

• Consistent expert-level performance – They provide reliable expertise without human limitations like fatigue, emotions or boredom.

• Cost-effectiveness – Once developed, they can deliver expertise at a fraction of the cost of human experts.

• Permanence – The knowledge persists even when human experts are unavailable.

• Fast response times – They can analyze massive amounts of data and provide solutions much faster than humans.

• Scalability – They can be distributed to multiple users without loss of quality.

• Explainability – Unlike black-box AI, KBS can explain the reasoning behind their solutions.

Of course, they aren’t perfect. KBS rely heavily on the quality of their knowledge bases. If the data is incomplete or inaccurate, they will produce poor results. They also require significant upfront investment and specialized skills to develop properly.

Architecture of a Knowledge-Based System

Let’s look under the hood to understand the key components that make up a knowledge-based system:

• User Interface – Enables the user to query the system and view explanations/solutions. Could be menu-driven, natural language, graphical, etc.

• Inference Engine – Applies problem solving rules and logic to provide solutions and explanations.

• Knowledge Base – Stores domain knowledge such as facts, rules and procedures.

• Knowledge Engineering Module – Allows new knowledge to be added to the knowledge base.

• Explanatory Module – Explains the chain of reasoning used to arrive at a solution.

• Context Memory – Stores contextual data needed for the system to understand the problem scenario.

How Are Knowledge-Based Systems Developed?

Developing a knowledge-based system involves several key steps:

• Identifying the problem domain – What type of problems will the KBS solve? This guides the knowledge that must be acquired.

• Acquiring domain knowledge – Domain experts provide the facts, rules and procedures to build the knowledge base. This knowledge is captured through interviews, surveys, data analysis, and mining texts/documents.

• Representing the knowledge – The gathered knowledge must be encoded in formats like rules, semantic networks, or frames that the KBS can interpret.

• Developing the inference engine – The reasoning component is built using forward/backward chaining methods and pattern matching algorithms.

• Testing and validation – The KBS is tested with real-world test cases to validate its performance and accuracy.

• Deployment – Once validated, the KBS is deployed into production environments and user interfaces are created.

It’s an extensive process, requiring collaboration between experts and knowledge engineers. But the end result is an AI system that can replicate human expertise!

Current Trends in KBS Technology

Some exciting new trends in knowledge-based systems include:

• Using machine learning to automatically extract knowledge from datasets rather than solely relying on human experts.

• Embedding KBS within physical devices through IoT and edge computing to make everyday objects “intelligent”.

• Using natural language interfaces to enable free-form interaction between users and KBS.

• Adopting distributed architectures like blockchain to decentralize knowledge across networks.

• Integrating KBS with robotic systems to create intelligent agents that combine knowledge with physical capabilities.

Wrap Up