Artificial Intelligence: Chapter 4
Introduction to Knowledge Base Systems
Welcome back, class! Professor Zeeshan Bhatti here from Zeeshan Academy. In our last chapter, we equipped our agents with the power of problem-solving and search, allowing them to navigate state spaces and find paths to their goals. But those agents were a bit... naive. They viewed the world in simple, atomic chunks without any real understanding.
Today, we change that. We are moving up the ladder of intelligence. Welcome to Chapter 4: Introduction to Knowledge Base Systems. This is where our agents stop just processing and start knowing. This is the foundation of systems that can diagnose diseases, provide financial advice, and understand complex human queries.
What is a Knowledge-Based System?
Let's start with a simple, powerful definition:
A Knowledge-Based System (KBS) is a system built around a knowledge base—a collection of knowledge, taken from a human expert, and stored in such a way that the system can reason with it.
Think of it as a digital library, but not just a passive one. It's a library with a brilliant librarian inside who can read the books, connect the ideas, and answer your complex questions. The key differentiator is reasoning. A KBS doesn't just retrieve stored data; it infers new facts from existing ones.
But first, what is Knowledge?
In the context of AI, knowledge is the sort of information that people use to solve problems. It's not just raw data like "the temperature is 102°F." Knowledge is the meaningful connection of that data: "A body temperature of 102°F in a human is a fever, and a fever is often a symptom of an infection."
The Knowledge-Based Agent: The "Tell-and-Ask" Architecture
So, how do we build an agent around this concept? A knowledge-based agent consists of two core components:
Knowledge Base (KB): A set of representations of facts about the world. Each individual representation is called a sentence, and these sentences are expressed in a formal knowledge representation language.
Inference System: The "reasoning engine." This is the software that can derive new sentences from existing ones in the KB.
The agent operates through a simple, elegant cycle:
It TELLs the knowledge base what it perceives. For example, a medical agent might TELL its KB: "The patient has a fever" and "The patient has a cough."
It ASKs the knowledge base what action it should perform. It queries the KB, essentially asking, "Given everything you know, what should I do now?"
It performs the chosen action. Based on the answer from the KB, it might administer medicine, schedule a test, or display a diagnosis.
This TELL and ASK interface is the fundamental interaction loop for any knowledge-based system.
The Three-Layer Architecture of a KB Agent
To truly understand how this works, we need to look at the system through three distinct levels of abstraction.
Level 1: The Knowledge Level (The "What")
This is the most abstract level. We describe the agent purely by saying what it knows. We don't care how it's coded; we only care about the knowledge it possesses.
Example: A taxi agent might know that "The Golden Gate Bridge connects San Francisco with Marin County." This is a fact in its mental model of the world.
Level 2: The Logical Level (The "How" of Representation)
At this level, we take the knowledge and encode it into formal sentences using a knowledge representation language. This is where we make the knowledge machine-readable.
Example: We encode the fact above into a logical statement like:
Connects(GoldenGateBridge, SanFrancisco, MarinCounty). This sentence is a formal, unambiguous representation of the knowledge.
Level 3: The Implementation Level (The "How" of Storage)
This is the ground level—the physical representation of the sentences in the computer's memory. It's about bits, bytes, and data structures.
Example: The sentence
Connects(GoldenGateBridge, SanFrancisco, MarinCounty)might be stored as a string of characters:'(connects goldengatebridge sanfrancisco marincounty)', or as an entry in a database table, or as a node in a graph.
The beauty of this separation is that we can reason about the agent's capabilities at the knowledge level without getting bogged down by implementation details. We can design what the agent should know before we decide how it will store it.
Expert Systems: The Celebrity of Knowledge-Based Systems
Now, let's talk about the most famous application of this technology: Expert Systems.
What is an Expert System?
An expert system is a particular kind of knowledge-based system where the knowledge in the knowledge base has been taken directly from a human expert in a specific field.
Therefore, an expert system can, to a certain extent, act as a substitute for the expert from whom the knowledge was taken. Think of it as a method for cloning expertise and making it available 24/7, anywhere in the world.
How are they built? The Role of the Knowledge Engineer
Building an expert system is a unique process. It involves a special kind of AI professional called a Knowledge Engineer. The knowledge engineer's job is not to be the expert, but to extract the knowledge from the expert.
This process, called knowledge acquisition, is often the most challenging part of building an expert system. Why? Because human experts often know more than they can easily articulate—this is known as tacit knowledge. A knowledge engineer uses interviews, case studies, and observation to convert this tacit knowledge into explicit, formal rules for the knowledge base.
Classic Examples of Expert Systems:
MYCIN: One of the earliest and most famous. Developed in the 1970s at Stanford, it diagnosed bacterial infections and recommended antibiotics. Its performance was comparable to, and sometimes better than, human doctors.
DENDRAL: Used to infer the molecular structure of unknown organic compounds based on mass spectrometry data.
XCON: Used by Digital Equipment Corporation to configure computer systems, saving the company millions of dollars per year.
Why Are Knowledge Base Systems So Powerful?
You might wonder, why go through all this trouble? The power of a KBS lies in several key advantages:
Permanence: Human knowledge can be lost when an expert retires or passes away. The knowledge base, however, is permanent.
Reproducibility and Scalability: Once captured, knowledge can be replicated across thousands of systems, providing expert-level advice to millions of people simultaneously.
Explicability: Unlike some modern machine learning models (which can be "black boxes"), a well-designed KBS can explain its reasoning. When it gives an answer, you can ASK it, "Why?" and it can trace back through the chain of inferences to show the facts and rules it used. This is critical for building trust.
Foundation for Modern AI: While pure expert systems had their limitations, the principles of knowledge representation and reasoning are fundamental. They form the bedrock of everything from semantic webs and intelligent assistants to advanced diagnostic tools.
Conclusion: From Searching to Knowing
With this chapter, we've taken a monumental step. We've moved from agents that merely search for solutions to agents that know and reason. The knowledge base system provides the "common sense" and domain-specific understanding that separates simple programs from truly intelligent behavior.
In our next chapters, we will dive into the specific languages of knowledge representation, like logic, and the algorithms that power the inference engine. We are now building the mind, not just the map.
Instructor: Prof. Dr. Zeeshan Bhatti
YouTube Channel: "Zeeshan Academy"
(https://www.youtube.com/@ZeeshanAcademy)
To Direct Download the File: https://www.scribd.com/doc/309734579/Artificial-
Intelligence-Chapter-4-Introduction-to-Knowledge-Base-Systems
Can you think of a domain or a task in your own field where an Expert System could be useful? What kind of knowledge would need to be captured from a human expert? Let's discuss it in the comments below!
https://www.scribd.com/doc/309734579/Artificial-Intelligence-Chapter-4-Introduction-to-Knowledge-Base-Systems
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