Artificial Intelligence - Knowledge Based Agent

Quiz

Humans are intelligent and do tasks which requires creativity by using the logic to solve problems, learn form their past experiences and change when the things are new. They solve the problems or handle the situations with the knowledge stored in their minds.

On the other hand, Artificial Intelligence stores this kind of intelligence in Knowledge-Based Agents (KBAs). These agents make decisions through reasoning by storing facts, rules, and relationships in a knowledge base. Unlike humans, they can analyze vast volumes of data quickly and accurately.

Knowledge Based Agents in Artificial Intelligence

In Artificial Intelligence, Knowledge-Based Agents(KBA) use stored information and reasoning techniques to make intelligent decisions and solve problems. They are designed to do complex tasks effectively by combining logic and organized information.

Knowledge based agents depend on a knowledge base which stores facts, rules, and information about the world.
They use an inference system for reasoning and draw conclusions from the knowledge base.
KBAs can adapt to new situations by adding or updating their knowledge.
KBAs are commonly used in problem-solving, decision-making, planning tasks and in domains like healthcare, education, and legal systems.
KBAs improve the scalability and accuracy of decision-making by handling large amounts of structured data.

Example of Knowledge Based Agents

Health Care: Knowledge-based agents analyze patient's data and medical history to suggest diagnoses or treatments which helps the doctor make wiser decisions. For instance, AI tools can identify patterns for the diagnosis of diseases like cancer and diabetes.

Business Intelligence: AI tools analyze large amounts of data to provide insights on market trends, customer behavior, and productivity. Companies use them to plan marketing campaigns and improve efficiency.

Architecture of Knowledge-Based Agents

First, the knowledge based agent receives the data from environment through sensors then it retrieves the relevant information, rules, facts form the knowledge base to understand the context. It then processes the relevant information through inference system using techniques like forward or backward chaining. Based on this reasoning, agent decides the best action.

Once the decision is made, the agent executes the action using actuators to affect the environment. If the agent is capable of learning, it updates the new information to the knowledge base for future tasks.

What is Knowledge base?

Knowledge base is the critical component of the Knowledge-Based Agent, which includes all the information, rules, and facts necessary for reasoning and decision.

It gives the agent all the information it needs to reason, make informed judgments, and act appropriately with reference to its environment.

Facts are information about the world. For example, "The Earth orbits the Sun once every 365.25 days".
Rules are logical statements that relate facts. For example, "If the temperature drops below 0C, water will freeze into ice".
Updates: The knowledge base can be created or updated during the process in which an agent learns or introduces new knowledge so that over a period, its decision-making skill is increased.

For example, smart thermostat saves information as "Room temperature needs to be at 22C", "If the temperature of room has exceeded to more than 25C then turn on air conditioner".

Inference System

The inference engine proceeds to process knowledge in the knowledge base using logical reasoning to help the agent take intelligent decision and solve the problem. Imagine this as a tool to determine "what is next" from facts and regulations.

The inference system acts like the thinking part of the agent. It begins with a set of given facts and uses reasoning to obtain new facts or decide what to do.

This system also creates new knowledge from the existing information and includes them in the knowledge base to assist future decision making.

In practical, an inference system is constructed around algorithms and logical representations such as propositional or first-order logic.
Inference systems are used in many applications, such as expert systems, natural language processing, and automated reasoning. This allows machines to think logically and make judgments like humans.

This technique uses two basic approaches to introduce logic −

Forward Chaining: This is based on the assumptions given. The inference engine checks the rules, applies them, and keep inferring the new facts until it reaches a conclusion or goal.
Backward Chaining: Start from the goal and work backward. It determines which facts or conditions need to be satisfied to achieve that goal.

For example, the thermostat checks the current temperature, applies the criteria, and concludes, "The room is 27C, so I need to activate the cooling system." This decision is made by inference system.

Sensors and Actuators

In knowledge-based agent sensors collect environmental information such as temperature, light, and movement, while actuators use that information to perform actions such as moving, turning, or cleaning. Robot vacuums use sensors to look for dirt and actuators to clean it or to avoid obstacles.

Operations Performed by KBA

Knowledge-based agents utilize three key procedures to take intelligent actions −

TELL: The agent updates its knowledge base with new information from the environment. For example, if a robot sees a door open, it tells the knowledge base, "The door is open."
ASK: The agent asks the knowledge base what decision it should take. For example, it may ask, "Should I close the door or move forward?".
PERFORM: The agent will follow the guidance of the knowledge base, such as closing the door or moving forward. It performs the selected option.

These processes enable the agent to learn, make smart judgments, and efficiently adapt to its surroundings.

A Simple Knowledge Based Agent

Knowledge-Based Agent (KBA) processes perceptions, reasons using its knowledge base, and makes decisions. The following pseudocode represents a basic Knowledge-Based 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 a Knowledge-Based Agents works

A Knowledge-Based Agent continuously perceives its environment, utilizes stored knowledge for reasoning, and makes informed decisions. Below is the explanation of how it operates −

MAKE-PERCEPT-SENTENCE: The agent updates its knowledge base with whatever it observes(environment) at time t by converting the percept into a logical sentence.
MAKE-ACTION-QUERY: The agent looks in its knowledge base to determine which action it should execute given the current time step and latest knowledge.
MAKE-ACTION-SENTENCE: After choosing an action the agent updates its knowledge base with a logical statement that describes the action which the agent intends to execute.
The function returns the chosen action, which the agent will carry out in the environment.

Various levels of Knowledge Based Agent

A knowledge-based agent can be understood at multiple levels, each explaining a distinct aspect of how it perceives, processed, and acted. Following are the various level of Knowledge based agent −

Knowledge level

At this level, the focus is on what the agent knows. The agent make decisions from the facts, rules, and the logical relationship found in its knowledge base.

For example, In a sprinkler activating system the knowledge base has information like "the soil is dry" as well as "if the soil is dry then turn on sprinkler." This describes what the system knows.

Logical Level

This level describes how the knowledge is represented and processed. The agent uses logic like that of propositional or first-order logic to infer, derive new information and to make decisions. Rule: "The soil is dry, therefore turn the sprinkler on. So the logical level will conclude "Turn the sprinkler on."

Implementation level

This is the physical or software implementation of the agent. It uses programming languages, algorithms, and hardware to implement the knowledge and reasoning processes. The system is implemented with a soil moisture sensor, sprinkler actuator, and a program written in a simple language like Python that processes the sensor data and controls the sprinkler.

Future of Knowledge-Based Agents

Knowledge-Based Agents (KBAs) are changing with the evolution of artificial intelligence, using machine learning, natural language processing, and automated reasoning to reason with complex data, learn from new data, and make intelligent decisions. KBAs are used extensively in industries to automate and improve efficiency.

KBAs will use deep learning in the future to improve reasoning and manage uncertainty, and hence become indispensable in healthcare, finance, cybersecurity, and intelligent automation.

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