How to use knowledge-based agents in AI
Posted: Mon Jan 20, 2025 6:56 am
We’re in the midst of what the internet is calling the “AI revolution.” You’ve probably noticed how artificial intelligence tools are making their way into almost every aspect of our work, from automating mundane tasks to empowering decision-making processes.
Emerging AI tools include knowledge-based agents, which use a vast knowledge base to deliver answers and actionable insights.
In this article, we’ll explore the mechanics of knowledge-based agents in AI, how they’re transforming the workplace, and why they’re poised to become an essential part of any forward-thinking team.
60 Second Summary
Knowledge-based agents are artificial intelligence systems that access relevant information from a knowledge repository, analyze it, and provide it
They are based on two main components: a knowledge base to store data and an inference system to reason.
Knowledge-based agents collect data, interpret it, retrieve relevant knowledge, and provide actionable results.
Its applications include healthcare for patient support, customer service for instant help, and finance for hospital mailing email list support management.
What is a knowledge-based agent?
A knowledge-based agent is an AI system that uses advanced AI techniques to access, interpret, and provide information from a structured knowledge repository. In addition to storing data, these agents analyze knowledge stored in databases to solve problems or provide actionable insights.
By representing knowledge in a machine-readable format using Knowledge Representation Language, they enable systems to interpret, reason and make decisions.
They include methods such as propositional logic, first-order logic, semantic networks, frameworks, and ontologies, each of which offers different ways of representing relationships and entities. KRLs are crucial for AI and information systems, as they allow machines to store knowledge, draw conclusions, and communicate across platforms.
Unlike other AI agents (such as chatbots or virtual assistants), knowledge-based agents can handle complex queries. They also facilitate significant improvements in time management and efficiency. Check out these statistics from Mckinsey Global Institute :
Use cases and advantages of knowledge-based agents in AI
Example: Rufus, Amazon's AI Shopping Assistant, works as an AI knowledge management agent by leveraging a broad knowledge base spanning product catalogs, customer reviews, Q&As, and web information.
Using natural language processing, Rufus understands customer queries and employs Retrieval Augmented Generation (RAG) to find relevant information and generate comprehensive answers. This process involves retrieving relevant data from its knowledge base and augmenting it with the context of the user’s query.
Continuous learning through user feedback and reinforcement learning allows Rufus to refine its responses and improve its ability to deliver useful answers. In essence, Rufus centralizes, organizes, disseminates, and personalizes shopping-related knowledge , empowering customers to make informed purchasing decisions.
Emerging AI tools include knowledge-based agents, which use a vast knowledge base to deliver answers and actionable insights.
In this article, we’ll explore the mechanics of knowledge-based agents in AI, how they’re transforming the workplace, and why they’re poised to become an essential part of any forward-thinking team.
60 Second Summary
Knowledge-based agents are artificial intelligence systems that access relevant information from a knowledge repository, analyze it, and provide it
They are based on two main components: a knowledge base to store data and an inference system to reason.
Knowledge-based agents collect data, interpret it, retrieve relevant knowledge, and provide actionable results.
Its applications include healthcare for patient support, customer service for instant help, and finance for hospital mailing email list support management.
What is a knowledge-based agent?
A knowledge-based agent is an AI system that uses advanced AI techniques to access, interpret, and provide information from a structured knowledge repository. In addition to storing data, these agents analyze knowledge stored in databases to solve problems or provide actionable insights.
By representing knowledge in a machine-readable format using Knowledge Representation Language, they enable systems to interpret, reason and make decisions.
They include methods such as propositional logic, first-order logic, semantic networks, frameworks, and ontologies, each of which offers different ways of representing relationships and entities. KRLs are crucial for AI and information systems, as they allow machines to store knowledge, draw conclusions, and communicate across platforms.
Unlike other AI agents (such as chatbots or virtual assistants), knowledge-based agents can handle complex queries. They also facilitate significant improvements in time management and efficiency. Check out these statistics from Mckinsey Global Institute :
Use cases and advantages of knowledge-based agents in AI
Example: Rufus, Amazon's AI Shopping Assistant, works as an AI knowledge management agent by leveraging a broad knowledge base spanning product catalogs, customer reviews, Q&As, and web information.
Using natural language processing, Rufus understands customer queries and employs Retrieval Augmented Generation (RAG) to find relevant information and generate comprehensive answers. This process involves retrieving relevant data from its knowledge base and augmenting it with the context of the user’s query.
Continuous learning through user feedback and reinforcement learning allows Rufus to refine its responses and improve its ability to deliver useful answers. In essence, Rufus centralizes, organizes, disseminates, and personalizes shopping-related knowledge , empowering customers to make informed purchasing decisions.