What is Knowledge Representation?
There are two primary concepts in Knowledge Representation:
Different Types of Knowledge Represented in AI
Four Fundamental Knowledge Representation Techniques in AI
Cycle of Knowledge Representation in AI
Approaches to Knowledge Representation in AI
Properties of a Good Knowledge Representation System
Acquisitional efficiency is the ability of the knowledge representation system to automatically acquire new knowledge, integrate the new information into the existing knowledge base, and use the same to improve efficiency and productivity.
Why is Knowledge Representation Important for AI Systems?
Benefits of Knowledge Representation in AI
How Can Fingent Help
Frequently Asked Questions
- >> Declarative Knowledge: Constitutes the facts, objects, and concepts that allow us to describe the world around us.
- >> Structural Knowledge: Includes the problem-solving knowledge that describes the relationship between various concepts or objects and their descriptions.
- >> Procedural Knowledge: Used to complete any task with specific rules, strategies, processes, or agendas.
- >> Meta Knowledge: Constitutes the predefined knowledge about things that we are already aware of.
- >> Heuristic Knowledge: Used in the process of reasoning as it can solve issues based on the experiences of past problems.
- >> Derive information that is implied by the AI agent,
- >> Communicate with people in natural language,
- >> Decide what to do next,
- >> Plan future activities, and
- >> Solve problems in areas that normally require human expertise.
- >> KR system should be extensive, well-represented, and easily decipherable
- >> Should cover a wide range of standard computing procedures to support large scale application
- >> Must be easy to access and provide the options to identify events and decode the reaction of different components