Artificial Intelligence - Knowledge Based Agents

August 02, 2025

 Knowledge-Based Agents in Artificial Intelligence

 

Artificial Intelligence - Knowledge Based Agents

Humans are naturally smart and can handle tasks that need creativity and thinking. They solve problems by using logic, learning from past events, and adapting when something new happens. The knowledge they’ve gained over time helps them manage different situations.

Artificial Intelligence tries to bring this type of smartness into machines using what are called Knowledge-Based Agents (KBAs). These agents store facts, rules, and links between things in a structured way. With the help of reasoning, they use that knowledge to make decisions. Unlike humans, they can look through a huge amount of data and still stay fast and accurate.

What Are Knowledge-Based Agents in AI

In AI, a Knowledge-Based Agent uses stored facts and logic to make decisions and solve problems. These agents are made to deal with complex tasks by working with organized knowledge and reasoning methods.

  • A KBA relies on a knowledge base, which holds rules, facts, and other helpful data about the world.
  • It also uses an inference system to think and figure out new things based on what it already knows.
  • KBAs can learn and change by adding or updating their knowledge base.
  • They are used in problem-solving, planning, and decision-making in areas like education, medicine, and law.
  • Because they work with large sets of data, they help improve both accuracy and scale of decision-making.

 

Examples of Knowledge-Based Agents

Healthcare: KBAs can look at a patient’s history and symptoms to help suggest possible treatments or diagnoses. This helps doctors make better decisions. For example, AI tools can notice patterns related to conditions like diabetes or cancer.

Business: In marketing or operations, these agents go through huge amounts of company and customer data to give insights about trends or habits. Businesses use this to make smarter choices.

Architecture of a Knowledge-Based Agent

First, the agent gets data from its surroundings using sensors. It pulls related facts and rules from its knowledge base to understand what’s going on. Then, it uses reasoning methods like forward or backward chaining to process this information and figure out the best step to take.

After that, it takes action using actuators to interact with the world. If it can learn, it updates its knowledge base with the new details so it can do better in the future.

What Is a Knowledge Base

A knowledge base is a key part of the agent. It keeps all the needed information like facts and rules for reasoning.

This helps the agent think, decide, and act in line with its understanding of the environment.

Facts: These are basic truths about the world. Like: “Earth goes around the Sun in 365.25 days.”

Rules: These connect facts together. Example: “If it’s below 0°C, water will turn into ice.”

Updates: A learning agent updates its knowledge over time. The more it learns, the better its decisions get.

Example: A smart thermostat may save details like “Room should be 22°C” or “If room goes above 25°C, turn on the AC.”

Inference System

This is the thinking part of a KBA. It uses logical steps to draw conclusions from what the agent already knows. You can think of it as the part that figures out what comes next.

  • It starts from a set of known facts and then works to get new ones or decide on an action.
  • This system also creates new facts by thinking logically and adds them to the knowledge base.

 

It often uses propositional or first-order logic, depending on how complex the reasoning needs to be.

These systems are used in many tools, including expert systems, language understanding tools, and reasoning engines.

Two main ways of reasoning used:

  • Forward Chaining: Starts from known facts and keeps applying rules until it reaches a conclusion.
  • Backward Chaining: Starts with the goal and works backward to see what facts must be true to get there.

 

Example: A smart thermostat sees the room is 27°C, applies its rule, and decides it needs to turn on the cooler.

Sensors and Actuators

A KBA gets information using sensors—for example, detecting light, motion, or temperature. Then it acts using actuators, which might move something, turn it on, or take some other action.

A robot vacuum, for example, uses sensors to detect dirt or obstacles, and actuators to move and clean.

What a KBA Can Do: TELL, ASK, PERFORM

To work properly, a knowledge-based agent goes through three actions:

  • TELL: It adds new information to the knowledge base. Like, “The door is open.”
  • ASK: It queries the knowledge base to figure out what to do next. For example: “Should I close the door?”
  • PERFORM: It does what the knowledge base suggests, like closing the door or moving forward.

 

These steps let the agent learn, decide, and act in ways that suit the current environment.

A Basic Example of a Knowledge-Based Agent

A Knowledge-Based Agent works step-by-step. It takes in input, reasons with its knowledge base, and makes a decision. Below is a simple pseudocode for such an agent:

function KB-AGENT(percept):  
persistent: KB, a knowledge base   
          t, a counter, initially 0, indicating time   
TELL(KB, MAKE-PERCEPT-SENTENCE(percept, t))   
Action = ASK(KB, MAKE-ACTION-QUERY(t))   
TELL(KB, MAKE-ACTION-SENTENCE(action, t))  
 t = t + 1  
 return action

 

How It Works Step-by-Step

  • MAKE-PERCEPT-SENTENCE: It observes something at time t and puts that into the knowledge base.
  • MAKE-ACTION-QUERY: Based on what it knows, it asks the system what action it should take.
  • MAKE-ACTION-SENTENCE: After choosing, it updates the knowledge base to show what action it’s about to do.

 

It then performs the action.

Different Levels of a Knowledge-Based Agent

KBAs can be seen from three different viewpoints:

1. Knowledge Level

This is what the agent knows—the rules, facts, and logical links it has.
Example: A sprinkler system knows “the soil is dry” and that “dry soil means turn on the sprinkler.”

2. Logical Level

At this level, the focus is on how the knowledge is structured and used for reasoning—usually with logic.
Example: From “soil is dry” and “dry → sprinkler on”, the system concludes: turn on the sprinkler.

3. Implementation Level

This is about the real-world setup—how it's built in software and hardware.
Example: The system includes a soil sensor, a sprinkler actuator, and a small program (like in Python) to run the logic.

What’s Ahead for KBAs

As AI grows, KBAs are also getting better. They now combine logic with tools like machine learning and natural language understanding. This lets them handle more data, learn as they go, and make even smarter decisions.

In the near future, they may start using deep learning to deal with uncertainties and even harder problems. We’ll likely see them used more in fields like healthcare, finance, cybersecurity, and smart automation.

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