Knowledge Based Agent-Artificial Intelligence-Unit-II-Logical Reasoning

Introduction

Welcome friends! In this test session, we focus on the second unit of our intelligence class, which centers around logical reasoning. Here, we explore how logic and technology achieve the best results by analyzing logical sentences and propositional logic.

We cover the basics of logical reasoning, including collecting sentences in a formal language and using these sentences to inform the actions of an agent. These logical sentences help the agent decide its actions by informing it of the knowledge necessary to achieve its goals.

A knowledge base is a critical component, consisting of a collection of formal sentences. These sentences are added and queried using specific functions like "add new sentences" or simply making queries on stored information. An inference engine utilizes this knowledge base to react to new situations with appropriate actions.

Let's detail the components of a knowledge-based agent. The agent relies on a structured collection of statements, making informed decisions based on the current state and executing corresponding actions. This creates an environment where the agent updates its internal representation and uses this to achieve desirable outcomes.

Key Functions of a Knowledge-Based Agent

1. Add Sentences: This function is used to add new sentences to the knowledge base.
2. Query Function: This function queries the knowledge stored in the database to retrieve and act on information.

The knowledge-based agent, therefore, continuously updates its representations and knowledge to improve its functionality, ensuring it reacts appropriately to given circumstances.

To illustrate, the agent must be able to incorporate new knowledge, update its previous information, and use this to perform actions influencing the world.

The concept of declarative and procedural knowledge is crucial. Declarative knowledge involves facts and information, while procedural knowledge relates to the steps required to perform various actions.

For a practical example, a knowledge-based agent could be made to maintain records in various fields such as educational institutions, business, and professional software. By utilizing functions like "telling," these agents update their knowledge base, allowing them to perform efficiently and provide accurate results.

This structured approach allows the agent to perform intelligent tasks based on logical reasoning and the current state of affairs.

Keywords

• Logical Reasoning
• Knowledge Base
• Formal Language
• Agent Actions
• Inference Engine
• Procedural Knowledge
• Declarative Knowledge
• Add Sentences
• Query Function

FAQ

Q1: What is a knowledge-based agent?
A knowledge-based agent relies on a structured collection of statements to inform its actions, updating its internal representation and using this to achieve desirable outcomes.

Q2: What functions does a knowledge-based agent perform?
The key functions include adding new sentences to the knowledge base and querying the stored information to make decisions.

Q3: What is the difference between declarative and procedural knowledge?
Declarative knowledge involves facts and information, whereas procedural knowledge relates to the steps required to perform various actions.

Q4: How does a knowledge-based agent update its knowledge?
Agents use specific functions like "telling" to add new information to their knowledge base and update their internal representation.

Q5: How is logical reasoning applied in knowledge-based agents?
Logical reasoning involves using formal sentences to inform the agent's actions and decisions, ensuring successful task execution based on current states and goals.

Q6: What does an inference engine do in a knowledge-based agent?
The inference engine processes the knowledge base to react to new situations with appropriate actions, ensuring the system's goals are met efficiently.