knowledge Representation part 2, Slides of Artificial Intelligence

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Knowledge Representation

Representation

of Knowledge

• Artificial Intelligence research involves building

computer systems capable of performing tasks

like talking, planning, and playing chess.

• When we talk about people doing these things,

we always talk about what they have to "know"

in order to do them.

• In other words, we describe someone's ability to

do something intelligent in terms of knowledge.

• A representation of knowledge In Al is a

combination of data structures and interpretive

programs that, if used in the right way, will lead

to "knowledgeable" behavior.

Mapping

between facts

and

representations

Approach 1: Simple relational knowledge

• It is the simplest way of storing facts which uses the relational method, and each fact about a set of the object is set out systematically in columns.
• This approach of knowledge representation is famous in database systems where the relationship between different entities is represented.
• This approach has little opportunity for inference.

Approach 1:

Simple

relational

knowledge

• The reason that these representation is simple is that standing alone it provides very weak inferential capabilities. But knowledge represented in this form miss serve as the input to more powerful inference engines.
• For example, given just a fact of figure 4.4, it is not possible even to answer the simple question, "Who is the heaviest player? ". But if a procedure for finding the heaviest player is provided, then this fact will enable the procedure to compute an answer.
• If, instead, we are provided with a set of rules for deciding which hitter to put up against given pitcher, then this same relation can provide at least some information required by the rules.

Slot-and-Filler Structure

• In order to support property inheritance, objects must be organized into classes and classes must be arranged in a generalization hierarchy. Figure 4.5 shows some additional baseball knowledge inserted into a structure that is so arranged.
• Lines represent attributes. Box notes represent objects and values of attributes of objects. These values can be viewed as objects with attributes and values and so on. The arrows on the lines point from an object to its value along with the corresponding attribute line.
• These structure shown in the figure is a slot-and-filler structure.
• It may also be called a semantic network or a collection of frames in the latter case each individual frame represents the collection of attributes and values associated with a particular node.

Semantic Nets

• The semantic net, developed by Quillian (1968) and others, was invented as an explicitly psychological model of human associative memory.
• A net consists of nodes representing objects, concepts and events, and links between the nodes, representing their interrelations.
• Consider, for example, the simple net (as shown in the picture), where where bird and wings are nodes representing sets or concepts, and HAS-PART is the name of the link specifying their relationship. Among the many possible interpretations of this net fragment is the statement All birds have wings

Advantages of

Semantic

Representation

• Semantic networks are a natural representation of knowledge.
• Semantic networks convey meaning in a transparent manner.
• These networks are simple and easily understandable.

Drawbacks in

Semantic

representation

• Semantic networks take more computational time at runtime as we need to traverse the complete network tree to answer some questions. It might be possible in the worst case scenario that after traversing the entire tree, we find that the solution does not exist in this network.
• Semantic networks try to model human-like memory (Which has 1015 neurons and links) to store the information, but in practice, it is not possible to build such a vast semantic network.
• These types of representations are inadequate as they do not have any equivalent quantifier, e.g., for all, for some, none, etc.
• Semantic networks do not have any standard definition for the link names.

Frames and

Semantic

Networks

• Frames are derived from semantic networks and later evolved into our modern-day classes and objects.
• A single frame is not much useful. Frames system consist of a collection of frames which are connected.
• In the frame, knowledge about an object or event can be stored together in the knowledge base. The frame is a type of technology which is widely used in various applications including Natural language processing and machine visions.

Advantages of

frame

representation

• The frame knowledge representation makes the programming easier by grouping the related data.
• The frame representation is comparably flexible and used by many applications in AI.
• It is very easy to add slots for new attribute and relations.
• It is easy to include default data and to search for missing values.
• Frame representation is easy to understand and visualize.

Approach 3: Inferential knowledge

• Inferential knowledge approach represents knowledge in the form of formal logics.
• This approach can be used to derive more facts.
• It guaranteed correctness.

Approach 3: Inferential knowledge

- Example: Let's suppose there are two statements:

• Marcus is a man
• All men are mortal Then it can represent as;

man(Marcus) ∀x = man (x) ----------> mortal (x)