To represent varies kinds of real –world knowledge. We need to discuss several issues. They are as follows.
1) Important attributes.
2) Relationship among attributes.
3) Choosing the granularity of representation. 4) Representing set of objects.
5) Finding the right structure as needed. Important attributes:
Isa and instance attributes are important because they are present in almost every problem domain. These attributes are important because they support property inheritance.
Relationship among Attributes: The four properties of attributes are:
Inverse.
Existence in an Isa hierarchy.
Techniques for reasoning about values. Single value attributes.
Inverse: -
We can represent the relationship between entities in two ways.
a) The first is to represent both the relationship in a single representation. Ex: team (pee-wee, Brooklyn dodgers).
b) Here we use attributes that focus on a single entity but use them in a pairs. One is the inverse of the other. For example,
One associated with pee-wee-Reese
Team = Brooklyn-dodgers. One associated with Brooklyn-dodgers
Team-members = pee-wee-Reese As our primitives in a series of situations. Existence in an isa hierarchy:
There are attributes and specification of attributes. For example the attribute height is a specialized of general attribute physical size which is in turn a specialization of physical attribute. This generalization-specialization relationship supports property inheritance.
Techniques for Reasoning About values:
The reasoning system must reason about values that are not given explicitly. Information that helps us in reasoning includes.
a. Information regarding a type of value
Example: Length must be a number.
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Example: Age of a person cannot be greater than the age of the person’s parent.
c. Rule for computing the value whenever it is required. They cannot be backward rules or they can also be forward rules.
Single value attributes:
A specific attribute may sometime take unique value. For example, a base ball player can be in only one team at one time. Similarly another example, a person can have only one height at a given time.
Choosing the Granularity of Representation:
We can break the details into lower level primitives. For example, john spotted sue can be represented as
Spotted ((john)) object (sue) this may give answer to the question. Who spotted sue?
But it cannot be answer: Did john see sue? The obvious answer that may give is ‘yes’ But for A.I to reason it out we need to add fact.
Spotted (x, y) saw (x, y) Advantages:
The rules that are used to derive inferences from that knowledge must be written only in the form of primitives rather than in the form of many other ways.
Disadvantages:
Simple high level facts may requires a lot of storage when broken down into low level primitives.
Example: john punched Mary and Mary punched john.
Substantial work must be done to reduce the knowledge into primitive’s form, if knowledge I in high level form.
In many domains, it is not clear what the low level primitives should be
Representing Sets of Objects:-
We need to represent sets of object because there may be some properties which are true of sets but are not true of individual members and also to see that if a property is true of all elements of a set, then it is more efficient to associate it once with the set rather than to associate it explicitly with every element of set.
There are two ways to define a set and its elements.
Extensional definition: - This definition deals with listing the members.
Intentional definition: - It provides a rule such that, when a particular object is evaluated, returns true or false depending on weather the object s belong to the set or not.
Ex: an extensional definition of the set of our sun’s planets on which people live is [earth] an intentional description is given as
{X: sun-planet(x) ^ human-inhabited (x)}
An extensional definition and intentional definition may not correspond one to one with each other. In the above example the extensionally defined set {earth} may have any intentional definitions.
The advantages that an intentional definition has over extensional definition are…. a. Intentional representations’, can be describe infinite sets and sets not all of
whose elements are explicitly know. Ex: Sets of prime numbers or kings of England.
Intentional definition allows us to depend on parameters that can be change with time or location.
Ex: “The president of U.S used to be pemcrot”.
Finding the right structures as needed:
In order to access the right structure for describing a situation, we need to solve the following problems:
i. How to perform an initial selection of the most current structure? ii. How to fill in the details?
iii. If the one chosen doesn’t turn out to be appropriate, then how do we go for a better one?
iv. What to do if none of the structures that are available is appropriate? v. When we can go for a new structure?
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In order to solve the problems given above, there are two methods. Selecting an initial structure:
There are two important structures for selection of an initial structure. a. Index structure directly by specific English words.
Ex: john flew to New York.
b. Consider a major concept as a pointer to all of the structure
Ex: the concept steak might point to two scripts, one for restaurant and other for super script.
c. Locate one major clue and use that to select an initial structure. Revising the choice when necessary:-
Once we find a structure and if it doesn’t seem to be appropriate then we would opt another choice. The different ways in which this can be done are:
Choose the best one.
Refer to specify links between the structures in order to follow new directions to explore.