1. Habib Ullah qamar Mscs(se)
Reasoning and KR
Habib Ullah qamar
Mscs(se)

2. Artificial intelligence
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines.
In Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Colloquially, the term "artificial intelligence" is used to describe machines that copycat(mimic) "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

3. AI Cycle

4. AI Cycle
Learning : the study of computer algorithms that improve automatically through experience.
Reasoning : step-by-step reasoning that humans use when they solve puzzles or make logical deductions.
KR : representation of data, information and experiment in KB. objects, properties, categories and relations between objects;
Planning : able to make predictions about how their actions will change it—and be able to make choices

5. Knowledge and its types
A well-focused subject area is referred to as a knowledge domain, for example, medical domain, engineering domain, business domain etc.
Procedural knowledge: Describes how to do things, provides a set of directions of how to perform certain tasks, e.g., how to drive a car.
Declarative knowledge: It describes objects, rather than processes. What is known about a situation, e.g. it is sunny today, and cherries are red.

6. Knowledge and its types
Meta knowledge: Knowledge about knowledge, e.g., the knowledge that blood pressure is more important for diagnosing a medical condition than eye color.
Heuristic knowledge: Rule-of-thumb, e.g. if I start seeing shops, I am close to the market. o Heuristic knowledge is sometimes called shallow knowledge.
Structural knowledge: Describes structures and their relationships. e.g. various parts of the car fit together to make a car, or knowledge structures in terms of concepts, sub concepts, and objects.

7. Knowledge and its types

8. Representation
Approaches and schemes that come to mind when we begin to think about representation – Pictures and symbols. This is how the earliest humans represented knowledge when sophisticated linguistic ystems had not yet evolved – Graphs and Networks – Numbers

9. Representation – An example

10. Formal representation - FACTS
Facts are a basic block of knowledge (the atomic units of knowledge). They represent declarative knowledge (they declare knowledge about objects).
A proposition is the statement of a fact. Each proposition has an associated truth value. It may be either true or false. In AI, to represent a fact, we use a proposition and its associated truth value, e.g.

11. Formal representation
FACTS
In AI, to represent a fact, we use a proposition and its associated truth value, e.g.

12. Formal Representation - Facts
FACTS Types
Single values – Multi valued
an individual can only have one eye color, but may have many cars.
Uncertain facts :
it will probably be sunny today. 34% chances
Fuzzy facts : Fuzzy facts are ambiguous in nature, e.g. the book is heavy/light.
Object-Attribute-Value triplets
Ahmed’s son is Ali : Object: Ahmed , Attribute: son , Value: Al

13. Formal Representation - Facts
FACTS Types
Single values – Multi valued
an individual can only have one eye color, but may have many cars.
Uncertain facts :
it will probably be sunny today. 34% chances
Fuzzy facts : Fuzzy facts are ambiguous in nature, e.g. the book is heavy/light.
Object-Attribute-Value triplets
Ahmed’s son is Ali : Object: Ahmed , Attribute: son , Value: Al

14. Formal Representation -facts
Rules : are another form of KR. Durkin defines a rule as “A knowledge structure that relates some known information to other information that can be concluded or inferred to be true.”
Components of a rule : A Rule consists of two components
Antecedent or premise or the IF part
Consequent or conclusion or the THEN part
For example, we have a rule: IF it is raining THEN I will not go to school
Premise: It is raining Conclusion: I will not go to school.

15. Formal Representation - facts
Compound Rules: Multiple premises or antecedents may be joined using AND (conjunctions) and OR (disjunctions), e.g.
IF it is raining AND I have an umbrella THEN I will go to school.
IF it is raining OR it is snowing THEN I will not go to school.

16. Formal Representation-fatcs
Types of Rules
Relationship : are used to express a direct occurrence relationship between two events, e.g. IF you hear a loud sound THEN the silencer is not working.
Recommendation : offer a recommendation on the basis of some known information, e.g. IF it is raining THEN bring an umbrella.
Directive : like recommendations rule but they offer a specific line of action
IF it is raining AND you don’t have an umbrella THEN wait for the rain to stop

17. Formal Representation- logic
Just like algebra is a type of formal logic that deals with numbers, e.g. 2+4 = 6, propositional logic and predicate calculus are forms of formal logic for dealing with propositions.
We will consider two basic logic representation techniques: –Propositional Logic –Predicate Calculus

18. Propositional logic
Compound statements : Different propositions may be logically related and we can form compound statements of propositions using logical connectives. Common logical connectives are:
AND OR
NOT
IF Then
IF and only IF

19. Propositional logic

20. Practice of solving logic using truth table
¬(α ∨ β) ≡ (¬α ∧ ¬β) De Morgan (α ∧ (β ∨ γ)) ≡ ((α ∧ β) ∨ (α ∧ γ))

21. Predicate calculus
Predicate Calculus is an extension of propositional logic that allows the structure of facts and sentences to be defined. With predicate logic,
we can use expressions like Color( ball, blue)
This allows the relationship of sub-sentence units to be expressed, e.g. the relationship between color, ball and blue in the above example.
Due to its greater representational power, predicate calculus provides a mechanism for proving statements and can be used as a logic system for proving logical theorems.

22. Limitations of Propositional logic
Propositions can only represent knowledge as complete sentences, e.g. a = the ball’s color is blue.
Cannot analyze the internal structure of the sentence.
No quantifiers are available, e.g. for-all, there-exists
Propositional logic provides no framework for proving statements such as:
All humans are mortal
All women are humans
Therefore, all women are mortals This is a limitation in its representational power.

23. Quantifiers
Predicate calculus allows us to use quantifiers for statements. Quantifiers allow us to say things about some or all objects within some set.
The logical quantifiers used in basic predicate calculus are universal(∀) and existential (∃) quantifiers.
Universal is read as “for every” or “for all” and used in formulae to assign the same truth value to all variables in the domain, e.g. in the domain of numbers, we can say that (∀x) ( x + x = 2x). In words this is: for every x (where x is a number), x + x = 2x is true.

24. Quantifiers
Existential quantifier : The symbol for the existential quantifier is ∃. It is read as “there exists”, “ for some”, “for at least one”, “there is one”, and is used in formulae to say that something is true for at least one value in the domain, e.g. in the domain of persons, we can say that (∃ x) (Person (x) ∧ father (x, Ahmed) ).
In words this reads as: there exists some person, x who is Ahmed’s father.

25. Study material
Chapter 7
TheITeducation.com

26. Thanks ……
Habib ullah qamar