In this article Learn about Knowledge-Based Agents in Artificial Intelligence — their structure, working, and components such as the Knowledge Base, Inference Engine, and Learning Module.
Knowledge-Based Agents in Artificial Intelligence
Introduction
A Knowledge-Based Agent is an intelligent agent that makes decisions using its knowledge and reasoning.
This type of agent uses a knowledge-based system to understand its environment, make logical decisions, and take appropriate actions.
In other words —
“A Knowledge-Based Agent is one that can draw new conclusions and make correct decisions using its stored knowledge and reasoning.”
Core Idea
The main purpose of Knowledge-Based Agents is to enable machines not only to store data, but also to understand, reason, and make decisions based on that data.
Such agents behave like humans — just as we make decisions based on our experience and knowledge.
Structure of a Knowledge-Based System
A Knowledge-Based Agent is primarily composed of two key components:
- Knowledge Base (KB)
- It contains a collection of facts and rules.
- This is where the agent stores all its information.
- Example:
- “All humans are mortal.”
- “Rama is a human.”
- From these, the agent can infer: “Rama is mortal.”
- Inference Engine (Logic Engine)
- This component draws new inferences from the facts and rules stored in the Knowledge Base.
- It performs the reasoning or thinking process of the agent.
Working of a Knowledge-Based Agent
A Knowledge-Based Agent works in the following four steps: 👇
- Knowledge Base Creation:
The agent is provided with initial knowledge — such as facts, rules, and conditions. - Perception:
The agent receives new information from the environment through its sensors. - Inference:
The agent combines the new information with existing knowledge to form a conclusion — “What should be done?” - Action:
The agent takes the appropriate action based on its decision.
This process repeats continuously, enabling the agent to learn and improve over time.
Components of a Knowledge-Based Agent
| Component | Description |
| Knowledge Base | A collection of facts and rules (such as IF–THEN statements). |
| Inference Engine | The mechanism that derives conclusions or decisions using the stored knowledge. |
| Percept | Information received from the environment. |
| Action | The operation performed in response to a decision. |
| Learning Module | Updates the Knowledge Base by learning new information. |
Knowledge Representation
For effective reasoning, the agent must represent and store knowledge in an understandable form.
Several techniques are used for this purpose, including:
- Propositional Logic
- Predicate Logic
- Semantic Networks
- Frames and Ontologies
These techniques help organize facts and define relationships among them.
Advantages of Knowledge-Based Agents
Reasoning Ability: Can derive new conclusions using existing knowledge.
Flexibility: Can update knowledge when new information is acquired.
Human-like Decision Making: Makes decisions based on knowledge and experience.
Learning Ability: Learns from new experiences and continuously improves performance.
Limitations of Knowledge-Based Agents
Knowledge Acquisition Problem: Gathering sufficient and accurate knowledge is time-consuming.
Complexity: Managing and processing large amounts of knowledge can be difficult.
Handling Uncertainty: The agent may not always make the correct decision in uncertain situations.
Examples
- Medical Diagnosis System:
Identifies diseases based on doctors’ knowledge and medical data. - Expert System:
Acts as an expert in a specific domain (e.g., law, engineering, education). - Wumpus World Agent:
A game-based example where the agent uses knowledge to avoid hazards and reach its goal (gold).
Conclusion
A Knowledge-Based Agent is an intelligent system that makes decisions through knowledge and reasoning, not just raw data.
Its most significant characteristic is its ability to learn new knowledge, apply existing knowledge, and adapt to changing environments.
This capability makes Knowledge-Based Agents one of the foundational models in the field of Artificial Intelligence (AI).
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