Types of Knowledge Artificial Intelligence


Types of knowledge – Artificial Intelligence

Knowledge can be defined as the body of facts and principles accumulated by humankind or the act, fact, or state of knowing.

For example, in biological organisms, knowledge is stored as complex structures of interconnected neurons. The structures correspond to symbolic representations of the knowledge possessed by the organism, the facts, rules and so on.

Please note that an average human brain weighs about 3.3 pounds and contains an estimated number of 1012 neurons.  Also, note that these neurons and their interconnection capabilities provide about 1014 bits of potential storage capacity. On the other hand, in computers knowledge is stored as symbolic structures but in form of collections of magnetic spots and voltage states. 

Knowledge is of three types as shown below:

Typesof Knowledge

Let us define these types now. 

1. Procedural or Operational Knowledge  It is defined as compiled knowledge related to the performance of some task. For example, steps to solve a quadratic equation are expressed as procedural knowledge. 

2. Declarative or Relational Knowledge  It is passive knowledge expressed as statements of facts about the world. For example, personnel data in a database. Such data are explicit pieces of independent knowledge. 

3. Heuristic Knowledge  Heuristics means using some rules of thumb or tricks or strategies to simplify the solution to problems. We acquire this after much experience.  Some terminologies related to knowledge  We define certain terms that will be used again and again here.

They are as follows: 

1. Knowledge and data: A doctor has both knowledges as well as data. Here, data is the patient’s record whereas knowledge is what he has learned in his medical college. This was explained by Feigenbaum and McCorduck. 

2. Belief: It is defined as essentially any meaningful and coherent expression that can be represented. It may be true or false. 

3. Hypothesis: It is defined as a justified belief that is not known to be true. It is backed up with some supporting evidence but it may still be false. 

4. Knowledge: It is a true justified belief. 

5. Meta knowledge: It is the knowledge about the knowledge.  6. Epistemology: It is the study of the nature of knowledge. 

AI technique is a method that exploits knowledge that should be represented in such a way that: 

1. It captures generalizations. 

2. It can be understood by people who must provide it.

3. It can be easily modified to correct errors and incorporate changes. 

4. It can be used in many situations even if it is not totally accurate or complete. 

5. It can be used to help overcome its own sheer bulk by helping to narrow the range of possibilities that must usually be considered. 

Please note that it is possible to solve AI problems without using AI techniques but the solutions would be inefficient. Also, it is possible to apply AI techniques to the solution of non-AI problems and this will be a good thing for those problems that possess the same characteristics as AI problems.  Those systems that depend on a rich base of knowledge to perform difficult tasks are known as knowledge-based systems.  A knowledge-based system (KBS) consists of three main components as shown in Fig.

Components of a KBS


KBSs get their power from the expert knowledge that has been coded into facts,  rules, heuristics, and procedures. The knowledge is stored in a knowledge base only. Since it is stored separately (from the I/O unit and Inference CU) so we can easily add new knowledge to this knowledge base or refine existing knowledge without recompiling the control and inferencing programs. So,  now the construction, as well as the maintenance of KBS, becomes very simple.  Knowledge-base is different from the database. Let us tabulate the differences between them now.

Difference between Data Base (DB) and Knowledge Base (KB)

Data Base (DB)Knowledge Base (KB)
1. It is defined as a collection of data representing facts.1. It has information at a higher level of abstraction. 
2. It is larger than a KB.2. It is smaller than a DB.
3. Changes are fast.3. Changes are gradual.
4. All information needs to be stated explicitly.4. It has the power of inferencing.
5. It is maintained for operational purposes.5. It is used for data analysis and planning.
6. Knowledge is represented by a relational,  network, or hierarchical model.6. Knowledge is represented by logic or rules.

Knowledge may be represented at various levels as shown in Fig.

Levels of knowledge representation


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