Definition and Importance of Knowledge and Learning

Knowledge:

Knowledge is understood as the fact or condition of knowing something with familiarity gained through experience or association. Knowledge is something that we come to know by seeing, hearing, touching, feeling and tasting. Knowledge is the fact or condition of being aware of something. Knowledge can be referred to as the information that we receive or acquire through any medium. It gives us the power to explore things and to take decisions accordingly. Knowledge can also be considered as the knowledge about a domain that can be used to solve problems in that domain. In order to solve many problems, it requires much knowledge and this knowledge must be represented or stored in a trusted device.

Knowledge storing:

To acquire knowledge we need to understand it first. We learn and gain knowledge in our own preferable language. Every human or domain has their own way of understanding the knowledge and stores it as per required. It is a natural language for people to understand knowledge.

• Symbols for computer: The knowledge in a computer is stored as a number or character string which represents an object or idea (Internal representation of the knowledge).
  
  
• The core concepts: It means the mapping from facts to an internal computer representation and also to a form that people can understand.

Knowledge Representation:

There are the unlimited amount of knowledge in the whole world which holds meaning and are valuable in their own way. Each and every knowledge is equally important and may come to demand or existence with time. Knowledge can be of various types. It can be a simple factor a complex relationship. Mathematical formulas or rules for natural language syntax also represents knowledge. Associations between related concepts provide knowledge which helps a lot to solve many problems.Inheritance hierarchies between classes of objects have made the concept of programming easier.

Knowledge is more compact and faster to manipulate. Knowledge is more general which means it may be applied to situations we have not been programmed for. The important feature of intelligence is to create knowledge from data.

Importance of Knowledge:

Sufficient amount of knowledge preludes to intelligence. If we have enough knowledge then we can achieve intelligence. That is to exhibit intelligence knowledge is the must. Knowledge plays a major role in building intelligent systems. Not only that, knowledge is very important even in our daily lives. It makes us superior and gives us wisdom.

Learning:

In simple terms, learning refers to the cognitive process of acquiring skill or knowledge. It is a scholarly profound knowledge. Learning is making useful changes in our minds. Learning can simply be understood as the process or phase of gaining knowledge or skills. Learning means to construct or modify the representations of what is being experienced.

According to Herbert Simon (1983), "Learning is the phenomenon of knowledge acquisition in the absence of explicit programming". Learning denotes changes in the system that are adaptive in such a way that they enable the system to do the same task or tasks which are drawn from the same population more efficiently and more effectively next time.

Learning basically involves three factors and they are presented below:

• Changes: Learning changes the learner. For machine learning, the problem is determining the nature of the following changes and how to represent them in the best form.
• Generalization: Learning leads to generalization where performance must improve not only on the same task but on the similar tasks.
• Improvement: Learning leads to improvements. Machine learning must address the possibility that the changes may degrade in terms of performance and find ways to prevent it.

References:

1. Elaine Rich, Kevin Knight 1991, "Artificial Intelligence".
2. Nilsson, Nils J. Principles of Artificial Intelligence, Narosa Publishing House New Delhi, 1998.
3. Norvig, Peter & Russel, Stuart Artificial Intelligence: A modern Approach, Prentice Hall, NJ, 1995
4. Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India Private Limited New Delhi, 1998