Issues in Knowledge Representation

Knowledge representation and reasoning (KR², KR&R) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.

It is a device with sensors and actuators, for example : a robotic car, a camera, a PC. Agent program is an implementation of an agent function. An agent function is a map from the percept sequence(history of all that an agent has perceived till date) to an action.

• Important Attributed: Any attribute of objects so basic that they occur in almost every problem domain?
• Relationship among attributes: Any important relationship that exists among object attributed?
• Choosing Granularity:
• Set of objects:
• Finding Right structure:

 Meta-knowledge  — knowledge about what we know. 3. Thus in solving problems in AI we must represent knowledge and there are two entities to deal with:  Facts  — truths about the real world and what we represent. This can be regarded as the knowledge level  Representation of the facts  which we manipulate.

A good knowledge representation requires the following properties: Representational Accuracy. Inferential Adequacy. Inferential Efficiency.

Explanation: The components of ES include : Knowledge Base, Inference Engine, User Interface. 4. Which of the following is incorrect application of Expert System? Explanation: Time is not Benefits of Expert Systems.

An expert system generally consists of four components: a knowledge base, the search or inference system, a knowledge acquisition system, and the user interface or communication system. Knowledge systems solve difficult problems of the real woorld by performing inference processes on explicitly stated knowledge.

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