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Chapter: Artificial Intelligence

Type of Knowledge

The categorisation of knowledge is very much large and interesting. They can be of following types:



The categorisation of knowledge is very much large and interesting. They can be of following types:


Declarative knowledge


It is the passive knowledge expressed as statements of facts about the world. It gives the simple facts and ideas about any phenomenon. It means just the representation of facts or assertions. This tells the total description about the situation. For example, the facts about an organization may be its buildings, location, no. of departments, no. of employees etc. The facts may be of two types i.e. static and dynamic. The static facts do not change with time where as the dynamic facts change with time. For example, the name and location of an organization is permanent. But some additional departments may be added.


Procedural knowledge


Procedural knowledge is the compiled knowledge related to the performance of some task. For example the steps used to solve an algebric equation can be expressed as procedural knowledge. It also eradicates the limitations of declarative knowledge i.e. declarative knowledge tells about the organization but it cannot tell how the employees are working in that organization and how the products are developed. But procedural knowledge describes everything about the organization by using production rules and dynamic attributes.



For example, If: All the employees are very hardworking


They are very punctual


They have productive ideas.


Then: Large no. of products can be produced within a very limited time period.


The advantages of using procedural knowledge are as follows:


1)    Domain specific knowledge can be easily represented.

2)    Extended logical inferences, such as default reasoning facilitated.

3) Side effects of actions may be modeled. Some disadvantages of procedural knowledge are


1)    Completeness:  In procedural knowledge not all cases may be represented.


2)    Consistency:Not all deductions may be correct.


3) Modularity: Changes in knowledge base might have far-reaching effects.


Inheritable knowledge


There are many situations in the world, where the object of an event inherits some properties of that particular event or any other event.


For example, consider a college. A college has certain features like classrooms, teachers, play ground, furniture, students etc. Besides these, there will be some general concepts regarding the functioning of the college, like it will have time table for each class, a fee deposit plan, examination pattern, course module etc. It can have many more deep concepts like placement of students etc. Now, if we say “A is a Colleg e”, then A will automatically inherits all the features of the college. It may be possible that X has some additional features. The inheritable knowledge is diagrammatically represented below. Here, the relationship ‘has’ indicates the silent features or attributes and ‘is a’ represents the variable or instance of that type. A inherits all the properties of college and has one additional feature of having male students. In this type of knowledge, data must be organized into a hierarchy of classes. The arrows represent the point from object to its value in the diagram. Boxed nodes represent the objects and values of attributes of objects.



Relational Knowledge


Relational knowledge is made up of objects consisting of attributes and corresponding associated values. In this type of knowledge, the facts are represented as set of relations in a tabular form. The table stores or captures all the hidden attributes of objects.


For example the knowledge about doctors may be as mentioned in figure .



Figure Knowledge about Doctor


This form of representation is the simplest and can be used in database systems. But this representation cannot store any semantic, information. For example, from this information we cannot answer the questions like “What is the name of the doctor”? or “How many doctors are in eye department”?


Inferential Knowledge


The knowledge, which can use inference mechanism to use this knowledge is called inferential knowledge. The inheritance property is a very powerful form of inferential knowledge. The inference procedures implement the standard logic rules of inference. There are two types of inference procedures like forward inference and backward inference. Forward inference moves from start state to goal state whereas backward inference moves from goal state to start state. In this type of knowledge several symbols are generally used like " (universal quantifier), $(existential quantifier), ® (arrow indicator) etc.


For example: All cats have tails


" X: cat (x) ® has tail (x)




1)                A set of strict rules are defined which can be used to derive more facts.


2)                Truths of new statements can be verified.


3)                It gives guarantee about the correctness.


4)                Many inference procedures available to implement standard rules of logic.


Heuristic Knowledge


This type of knowledge is fully experimental. This knowledge requires some judgments about any performance. One can guess a good thing and also one can think bad thing. But good performances are generally taken in heuristic knowledge. For example, suppose it is asked that “Ram will score how much percentage in his final semester?” Then the answer might be 80%, 70%, 30% or 95%. The individual answers of this question based on the heuristic knowledge. The answer would be based on various factors such as past performance, his talent etc. If his previous semester percentage was 78%, then if one will say he will secure 10% in this semester then obviously he has not any knowledge about Ram.


Tacit Knowledge


This kind of knowledge is acquired by experience. Tacit knowledge is subconsciously understood and applied, difficult to articulate and formalize. This type of knowledge is developed from direct experience and action. This knowledge is usually shared through highly interactive conversation, story telling and experience. It also includes cognitive skills such as intuition as well as technical skills such as craft and know-how. Tacit knowledge cannot be transmitted before it is converted into words, models or numbers that can be understood. Tacit knowledge can be defined in two dimensions, such as technical dimension and cognitive dimension. In technical dimension highly subjective and personal insights, intuitions and inspirations derived from long experience. The dimensions such as beliefs, ideals, principles, values and emotions fall in the category of cognitive dimension.


Explicit Knowledge


This knowledge is formalized, coded in several natural languages (English, Italian and Spanish) or artificial languages (UML, Mathematics etc). This knowledge can be easily transmitted. It includes theoretical approaches, problem solving, manuals and database. As explicit knowledge, it was the first to be or, at least, to be archived. Tacit and explicit knowledge are not totally separate, but mutually complementary entities. Without any experience, we cannot truly understand. Explicit knowledge is playing an increasingly large role in organization and it is considered by some to be the most important factor of production in the knowledge economy. Imagine an organization without procedure manuals product literature or computer software. Also with explicit knowledge, some tacit knowledge is required to run the business in an organization. Without explicit knowledge, the organization is simply has a zero performance.



Research Knowledge


There are many standards for the generation and critical appraisal of research knowledge, but judging the quality of knowledge in this source is not without difficulty. There are disputes about the nature and content of standards in areas such as qualitative research, and the implementation of standards is sometimes weak so that conformity with them is not necessarily a guarantee of quality. This type of knowledge is very useful for researchers to improve the research quality.


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