Artificial Intelligence: Knowledge Based Agent

A Knowledge-Based Agent in Artificial Intelligence (AI) is an intelligent agent that makes decisions by reasoning over a knowledge base, which is a structured collection of facts, rules, and relationships about the world or a specific domain. These agents use their knowledge to perform tasks, solve problems, or answer queries by applying logical reasoning, inference, or other decision-making processes.

Key Components of a Knowledge-Based Agent

1. Knowledge Base (KB):
   • A repository of information, including facts (e.g., "Paris is the capital of France") and rules (e.g., "If X is a bird, then X can fly").
   • Represented using formalisms like first-order logic, semantic networks, or ontologies.
   • The KB is dynamic and can be updated as the agent learns new information.
2. Inference Engine:
   • The reasoning component that processes the knowledge base to derive new information or make decisions.
   • Uses techniques like logical inference (e.g., modus ponens), forward chaining, backward chaining, or resolution.
   • Example: If the KB contains "All humans are mortal" and "Socrates is a human," the inference engine deduces "Socrates is mortal."
3. Perception:
   • The agent gathers input from its environment through sensors (e.g., cameras, microphones, or data inputs).
   • This input is used to update the knowledge base or trigger reasoning.
4. Action:
   • The agent performs actions based on its reasoning, using actuators or output mechanisms (e.g., moving a robot arm, generating text).
5. Learning Mechanism (optional):
   • Some knowledge-based agents can learn new knowledge from experience or external sources, updating the KB over time.

How a Knowledge-Based Agent Works

1. Perceive the Environment: The agent collects data from its surroundings (e.g., user queries, sensor data).
2. Update Knowledge Base: New information is integrated into the KB, ensuring it reflects the current state of the world.
3. Reasoning: The inference engine applies logical rules to the KB to derive answers or decide on actions.
4. Act: The agent executes the appropriate action (e.g., answering a question, making a recommendation).

Example

A medical diagnosis system is a knowledge-based agent:

• Knowledge Base: Contains facts (e.g., "Fever is a symptom of flu") and rules (e.g., "If fever and cough are present, consider flu").
• Perception: Receives patient symptoms (e.g., fever, cough).
• Inference Engine: Reasons over the KB to diagnose the likely condition (e.g., flu).
• Action: Recommends treatment or further tests.

Advantages

• Explainability: Decisions are based on explicit rules and facts, making it easier to trace reasoning.
• Flexibility: The KB can be updated to handle new domains or information.
• Precision: Works well in well-defined domains with clear rules.

Limitations

• Scalability: Maintaining and querying large KBs can be computationally expensive.
• Uncertainty: Struggles with ambiguous or incomplete information unless combined with probabilistic methods.
• Knowledge Acquisition Bottleneck: Building and updating the KB requires significant effort.

Real-World Applications

• Expert Systems: Medical diagnosis, financial advisory systems.
• Natural Language Processing: Chatbots like me, which use knowledge bases to answer questions.
• Robotics: Robots reasoning about their environment to navigate or perform tasks.

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 −

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.