Knowledge Representation In Artificial Intelligence 6y4t6w

Knowledge Representation Issues What is Knowledge?  Knowledge Representation  Issues in Knowledge Representation  Mapping between facts and representations  Prepositional Logic  Predicate Logic 

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In order to solve the complex problems encountered in artificial intelligence, we need both a large amount of knowledge and some mechanisms for manipulating that knowledge to create solutions to new problems.

Knowledge 

The psychological result of perception and learning and reasoning.

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Information, descriptions, or skills acquired through experience or education.

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The facts or truths in some relevant world that we want to represent.

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Representation of facts in some chosen formalism.

Knowledge Representation 

Representation: The way knowledge is encoded.

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Once the knowledge is acquired, it must be organized in an application knowledge base for later use.

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A knowledge base can be managed like a database.

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It can be organized in several different configurations to facilitate fast inferencing from the knowledge.

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Choosing the wrong representation Could lead to a project failing.

Issues in Knowledge Representation

Mapping Between Facts and Representations Mapping "facts to symbols" and "symbols to facts" using forward and backward representation mapping.  Consider an English Sentence: 

Four General Representation Types 

Logical Representations ◦ - Prepositional logic ◦ - Predicate logic

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Semantic Networks

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Production Rules

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Frames

Logical Representations 

The Greek Philosopher, Aristotle was one of the first to codify “Right thinking” i.e, a reputable reasoning process.

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This syllogism provided pattern for argument structures that yielded correct inferences(conclusions) when given correct premises(facts).

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For eg. Premises : Sachin is a man. All men are mortal.

Inference : Sachin is mortal.

Logic means drawing conclusions on the basis of conditions.  Logic can be classified into 2 categories: 

◦ Prepositional logic ◦Inputs Predicate logic Premises

Facts

Logic System

Output

Inference

Conclusio n

Propositional logic 

A Proposition is nothing more than a statement that is either true or false.

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Syntax ◦ Propositions such as P meaning “it is wet” ◦ Connectives: and, or, not, implies, equivalent

◦ Brackets, T (true) and F (false) 

Semantics ◦ How to work out the truth of a sentence  Need to know how connectives affect truth  E.g., “P and Q” is true if and only if P is true and Q is true  “P implies Q” is true if P and Q are true or if P is false

◦ Can draw up truth tables to work out the truth of statements

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Propositional logic is fundamental to all logic.

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Also called Propositional calculus, Sentential calculus, or Boolean algebra.

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Tells the ways of ing and/or modifying entire propositions, statements or sentences to form more complicated propositions, statements or sentences, as well as the logical relationships and properties that are derived from the methods of combining or altering

Predicate Logic 

Because propositional logic deals with complete sentences and whether they are true or false; its ability to represent real world knowledge is limited.

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The propositional logic, is not powerful enough for all types of assertions.

Example : The assertion "x > 2", where x is a variable, is not a proposition because it is neither true nor false unless value of x is defined. 

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Predicate logic permits us to break a sentence down into component parts, namely an object, a characteristic of the object or some assertion about the object.

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In addition, it lets us to use variables and functions of variables in a symbolic logic statement.

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Eg.  Ram likes Seeta. (Prepositional)  Likes(Ram, Seeta) (Predicate logic form)

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Example Sentence:

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Prepared By: Sweeti Sah Gayatri Vidya Parishad College of Engineering