CHAPTER THREE of Knowledge management framework for the project management system

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1. KNOWLEDGE REPERSENATION AND REASONING IN ARTFICIAL INTELLEGENCE By: Inst. Bifa H. (MSc) CHAPTER THREE 17-Jun-23 1 By:Bifa H.

2. Outline: 17-Jun-23 By:Bifa H. 2 Logic and Inference Logical Agents Propositional Logic Predicate (First-Order)Logic Inference in First-Order Logic Knowledge Representation Knowledge Reasoning Bayesian reasoning Probabilistic reasoning Temporal reasoning Knowledge-based Systems

3. INTRODUCTION What is Knowledge? 17-Jun-23 3 By:Bifa H.

4. Cont.. 17-Jun-23 By:Bifa H. 4

5. What is KNOWLEDGE REPRESENATION? Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence which concerned with: AI agents thinking and How thinking contributes to intelligent behavior of agents. It is responsible for representing information about the real world so that a computer can understand and can utilize this knowledge to solve the complex real world problems . 17-Jun-23 5 By:Bifa H.

6. Cont … 17-Jun-23 By:Bifa H. 6 Knowledge Representation  in AI describes the representation of knowledge. Basically, it is a study of how the  beliefs, intentions , and  judgments  of an  intelligent agent  can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent . Knowledge representation is not just storing data into some database, but it also enables an intelligent machine to learn from that knowledge and experiences so that it can behave intelligently like a human.

7. Cont .. 17-Jun-23 By:Bifa H. 7

8. What to Represent:? Following are the kind of knowledge which needs to be represented in AI systems: Object:  All the facts about objects in our world domain Events:  Events are the actions which occur in our world. Performance:  It describes behavior which involves knowledge about how to do things. Meta-knowledge:  It is knowledge about what we know. Facts:  Facts are the truths about the real world and what we represent. Knowledge-Base:  The central component of the knowledge-based agents is the knowledge base. 17-Jun-23 8 By:Bifa H.

9. How we represent Knowledge? 17-Jun-23 By:Bifa H. 9

10. Picture and symbols 17-Jun-23 By:Bifa H. 10

11. Graphic representation 17-Jun-23 By:Bifa H. 11

12. Numbers 17-Jun-23 By:Bifa H. 12

13. Types of knowledge Knowledge:   Knowledge is awareness or familiarity gained by experiences of facts, data, and situations. Following are the types of knowledge in artificial intelligence: 17-Jun-23 13 By:Bifa H.

14. Types of knowledge 17-Jun-23 By:Bifa H. 14 Mainly there are two types of knowledge (Tacit and explicit ) Tacit knowledge – usually gets embedded in human mind through experience Includes awareness, perceptions, and feelings   Explicit knowledge- is codified and digitized in documents, books, reports, spreadsheets, memos etc. We can convert explicit knowledge to tacit knowledge

15. Tacit knowledge Explicit Knowledge 17-Jun-23 By:Bifa H. Un-codified Subjective Personal Context specific Difficult to share Codified Objective Impersonal Context independent Easy to share 15 Tacit vs Explicit

16. Other types of knowledge 17-Jun-23 By:Bifa H. 16 Declarative Knowledge  – It includes concepts, facts, and objects and expressed in a declarative sentence. Structural Knowledge  – It is a basic problem-solving knowledge that describes the relationship between concepts and objects. Procedural Knowledge  – This is responsible for knowing how to do something and includes rules, strategies, procedures, etc. Meta Knowledge  – Meta Knowledge defines knowledge about other types of Knowledge. Heuristic Knowledge  – This represents some expert knowledge in the field or subject.

17. 1. Declarative Knowledge: Declarative knowledge is to know about something. It includes concepts, facts, and objects. It is also called descriptive knowledge and expressed in declarative sentences . It is simpler than procedural language. 17-Jun-23 17 By:Bifa H.

18. Cont… 17-Jun-23 By:Bifa H. 18

19. 2. Procedural Knowledge It is also known as imperative knowledge . Procedural knowledge is a type of knowledge which is responsible for knowing how to do something. It can be directly applied to any task. It includes rules, strategies, procedures, agendas , etc. Procedural knowledge depends on the task on which it can be applied. 17-Jun-23 19 By:Bifa H.

20. Cont… 17-Jun-23 By:Bifa H. 20

21. 3. Meta-knowledge: Knowledge about the other types of knowledge is called Meta-knowledge. A study of planning, tagging and learning are some of the examples of meta knowledge 17-Jun-23 21 By:Bifa H.

22. 4. Heuristic knowledge: Heuristic knowledge is representing knowledge of some experts in a filed or subject. This knowledge is also known as Shallow knowledge and it follows the principle of thumb rule. It is very efficient in reasoning process as it solves the problems based on the records of past problems or the problems which are compiled by experts. It provides knowledge based on the experiences it gathered during the past problems 17-Jun-23 22 By:Bifa H.

23. Cont… 17-Jun-23 By:Bifa H. 23

24. 5. Structural knowledge: Structural knowledge is basic knowledge to problem-solving. It describes relationships between various concepts such as kind of, part of, and grouping of something. It describes the relationship that exists between concepts or objects. 17-Jun-23 24 By:Bifa H.

25. Cont… 17-Jun-23 By:Bifa H. 25

26. AI knowledge cycle: An Artificial intelligence system has the following components for displaying intelligent behavior: Perception Learning Knowledge Representation and Reasoning Planning Execution 17-Jun-23 26 By:Bifa H.

27. What is the Relation between Knowledge & Intelligence 17-Jun-23 By:Bifa H. 27 In the real world, knowledge plays a vital role in intelligence as well as creating  artificial intelligence . It demonstrates the intelligent behavior in  AI agents or systems . It is possible for an agent or system to act accurately on some input only when it has the knowledge or experience about the input.

28. Approaches to knowledge representation: There are mainly four approaches to knowledge representation, which are given below: 1. Simple relational knowledge 2. Inheritable knowledge 3. Inferential knowledge 4. Procedural knowledge 17-Jun-23 28 By:Bifa H.

29. 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. 17-Jun-23 29 By:Bifa H.

30. 2. Inheritable knowledge: In the inheritable knowledge approach, all data must be stored into a hierarchy of classes . All classes should be arranged in a generalized form or a hierarchal manner. In this approach, we apply inheritance property.  Every individual frame can represent the collection of attributes and its value. In this approach, objects and values are represented in Boxed nodes. We use Arrows which point from objects to their values 17-Jun-23 30 By:Bifa H.

31. Inheritable knowledge…. 17-Jun-23 31 By:Bifa H.

32. 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 . 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)s 17-Jun-23 32 By:Bifa H.

33. 4. Procedural knowledge: Procedural knowledge approach uses small programs and codes which describes how to do specific things, and how to proceed. In this approach, one important rule is used which is  If-Then rule . In this knowledge, we can use various coding languages such as  LISP language  and  Prolog language . We can easily represent heuristic or domain-specific knowledge using this approach. But it is not necessary that we can represent all cases in this approach. 17-Jun-23 33 By:Bifa H.

34. Techniques of knowledge representation 17-Jun-23 By:Bifa H. 34

35. Techniques of knowledge representation… There are mainly four ways of knowledge representation which are given as follows: Logical Representation Semantic Network Representation Frame Representation Production Rules 17-Jun-23 35 By:Bifa H.

36. Requirements for knowledge Representation system: 17-Jun-23 By:Bifa H. 36 A good knowledge representation system must have properties such as: Representational Accuracy :  It should represent all kinds of required knowledge. Inferential Adequacy : It should be able to manipulate the representational structures to produce new knowledge corresponding to the existing structure. Inferential Efficiency : The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides. Acquisition efficiency : The ability to acquire new knowledge easily using automatic methods.

37. 1. Logical Representation Logical representation is a language with some concrete rules which deals with propositions and has no ambiguity in representation. Logical representation means drawing a conclusion based on various conditions. This representation lays down some important communication rules . It consists of precisely defined syntax and semantics which supports the sound inference. Each sentence can be translated into logics using syntax and semantics. 17-Jun-23 37 By:Bifa H.

38. Cont… 17-Jun-23 By:Bifa H. 38

39. Cont… 17-Jun-23 By:Bifa H. 39 Logical representation is of two types: Propositional Logic:   Propositional logic is also known as statement logic or propositional calculus that works in a Boolean, which means a method of True or False .  First-order Logic :  First-order logic is a type of logical knowledge representation that you can also term First Order Predicate Calculus Logic (FOPL). This representation of logical knowledge represents the predicates and objects in quantifiers. It is an advanced model of propositional logic

40. 2. Semantic Network Representation Semantic networks are alternative of predicate logic for knowledge representation. In Semantic networks, we can represent our knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Semantic networks are easy to understand and can be easily extended Storing information in the form of graph 17-Jun-23 40 By:Bifa H.

41. Cont… This representation consists of mainly two types of relations : IS-A relation (Inheritance) Kind-of-relation Example:  Following are some statements which we need to represent in the form of nodes and arcs. Statements: Jerry is a cat. Jerry is a mammal Jerry is owned by Priya . Jerry is brown colored. All Mammals are animal 17-Jun-23 41 By:Bifa H.

42. Cont… 17-Jun-23 By:Bifa H. 42

43. Cont… 17-Jun-23 By:Bifa H. 43 Advantages : Semantic networks are a natural representation of knowledge. Also, it conveys meaning in a transparent manner. These networks are simple and easy to understand . Disadvantages: Semantic networks take more computational time at runtime. Also, these are inadequate as they do not have any equivalent quantifiers. These networks are not intelligent and depend on the creator of the system.

44. 3. Frame Representation A frame is a record like structure which consists of a collection of attributes and its values to describe an entity in the world. Frames are the AI data structure which divides knowledge into substructures by representing stereotypes situations. It consists of a collection of slots and slot values. These slots may be of any type and sizes. Slots have names and values which are called facets. Facets:  The various aspects of a slot is known as  Facets . Facets are features of frames which enable us to put constraints on the frames. 17-Jun-23 44 By:Bifa H.

45. Lets take an example of a frame for a book   17-Jun-23 45 By:Bifa H.

46. Cont… 17-Jun-23 By:Bifa H. 46   Advantages: It makes the programming easier by grouping the related data. Frame representation is easy to understand and visualize. It is very easy to add slots for new attributes and relations. Also, it is easy to include default data and search for missing values. Disadvantages: In frame system inference, the mechanism cannot be easily processed. The inference mechanism cannot be smoothly proceeded by frame representation. It has a very generalized approach

47. 4. Production Rules Production rules system consist of ( condition, action ) pairs which mean, "If condition then action". It has mainly three parts: The set of production rules Working Memory The recognize-act-cycle In production rules agent checks for the condition and if the condition exists then production rule fires and corresponding action is carried out. Knowledge is in the form of if then statement and machine take decision based on rules And the action part carries out the associated problem-solving steps. This complete process is called a recognize-act cycle. 17-Jun-23 47 By:Bifa H.

48. Cont… Example: IF (at bus stop AND bus arrives) THEN action (get into the bus) IF (on the bus AND paid AND empty seat) THEN action (sit down). IF (on bus AND unpaid) THEN action (pay charges). IF (bus arrives at destination) THEN action (get down from the bus). . 17-Jun-23 48 By:Bifa H.

49. Cont… 17-Jun-23 By:Bifa H. 49 Advantages: The production rules are expressed in natural language. The production rules are highly modular and can be easily removed or modified. Disadvantages: It does not exhibit any learning capabilities and does not store the result of the problem for future uses. During the execution of the program, many rules may be active. Thus, rule-based production systems are inefficient.

50. REASONING IN ARTIFTIAIL INTELLEGENCE 17-Jun-23 By:Bifa H. 50 Logic is a language of reasoning . It is a collection of rules called logic arguments, we use when doing logical reasoning. Reasoning is an act of deriving a conclusion from certain premises u sing a given methodology . Reasoning is a process of thinking, logically arguing, drawing inference.

51. REASONING 17-Jun-23 51 By:Bifa H.

52. 1. Deductive reasoning 17-Jun-23 By:Bifa H. 52

53. Cont… 17-Jun-23 By:Bifa H. 53 Deductive Reasoning starts with the general and draws specific conclusions. This would look like driving past a forest of trees, noticing all of the leaves on the trees are green, then making a hypothesis that any given tree in that forest would also have green leaves.

54. 2. Inductive Reasoning: 17-Jun-23 By:Bifa H. 54

55. Cont…. 17-Jun-23 By:Bifa H. 55 Inductive reasoning follows a specific pathway. It begins making a specific observation (the leaves on the observed tree is green), notices a pattern (this group of trees in front of me all have green leaves), and draws a general conclusion all trees have green leaves.

56. 3. Abductive reasoning 17-Jun-23 By:Bifa H. 56

57. Cont… 17-Jun-23 By:Bifa H. 57 Abductive Reasoning happens when an algorithm draws a conclusion with incomplete data after noticing a pattern. Say you want to conclude the temperature outside using only the clothes people are wearing. When people are cold, they typically wear coats. When looking outside, no one is wearing a coat so you conclude it must be warm.

58. 4. Common Sense Reasoning 17-Jun-23 By:Bifa H. 58

59. 5. Monotonic Reasoning 17-Jun-23 By:Bifa H. 59

60. 6. Non-monotonic Reasoning 17-Jun-23 By:Bifa H. 60

61. Propositional logic in Artificial intelligence 17-Jun-23 By:Bifa H. 61 The simplest kind of logic is propositional logic (PL ), in which all statements are made up of propositions. The term  "Proposition "refers to a declarative statement that can be true or false . It's a method of expressing knowledge in logical and mathematical terms. Propositional logic is a simple form of logic which is also known as Boolean logic

62. Cont… 17-Jun-23 By:Bifa H. 62 proposition has TRUTH values (0 and 1) which means it can have one of the two values i.e. True or False. It is the most basic and widely used logic. Example: It is Sunday. The Sun rises from West (False proposition) 3 + 3 = 7 (False proposition) 5 is a prime number.

63. Its Properties: 17-Jun-23 By:Bifa H. 63 Satisfiable:  A atomic propositional formula is satisfiable if there is an interpretation for which it is true. Tautology:  A propositional formula is valid or a tautology it is true for all possible interpretations.   Contradiction:   A propositional formula is contradictory (unsatisfiable) if there is no interpretation for which it is true. Contingent:  A propositional logic can be contingent which means it can be neither a tautology nor a contradiction.

64. Syntax of propositional logic: 17-Jun-23 By:Bifa H. 64 The syntax of propositional logic defines the allowable sentences for the knowledge representation. There are two types of Propositions: Atomic Propositions Compound propositions Atomic Proposition:  Atomic propositions are the simple propositions. It consists of a single proposition symbol. These are the sentences which must be either true or false .

65. Cont… 17-Jun-23 By:Bifa H. 65 a) 2+2 is 4, it is an atomic proposition as it is a true fact.   b) "The Sun is cold" is also a proposition as it is a false fact. Compound proposition:  Compound propositions are constructed by combining simpler or atomic propositions, using parenthesis and logical connectives. a) "It is raining today, and street is wet."   b) " Ankit  is a doctor, and his clinic is in Mumbai."  

66. Logical Connectives and truth table 17-Jun-23 By:Bifa H. 66 Logical connectives are used to connect two simpler propositions or representing a sentence logically. We can create compound propositions with the help of logical connectives. There are mainly five connectives, which are given as follows:

67. Rules of Inference in Artificial intelligence 17-Jun-23 By:Bifa H. 67 In artificial intelligence, we need intelligent computers which can create new logic from old logic or by evidence,  so generating the conclusions from evidence and facts is termed as Inference . Inference rules: Inference rules are the templates for generating valid arguments. Inference rules are applied to derive proofs in artificial intelligence, and the proof is a sequence of the conclusion that leads to the desired goal.

68. Cont…. 17-Jun-23 By:Bifa H. 68 In inference rules, the implication among all the connectives plays an important role. Following are some terminologies related to inference rules: Implication:  It is one of the logical connectives which can be represented as P → Q. It is a Boolean expression. Converse:  The converse of implication, which means the right-hand side proposition goes to the left-hand side and vice-versa. It can be written as Q → P. Contra positive:  The negation of converse is termed as contra positive, and it can be represented as ¬ Q → ¬ P. Inverse:  The negation of implication is called inverse. It can be represented as ¬ P → ¬ Q.

69. Types of Inference rules: 17-Jun-23 By:Bifa H. 69 1. Modus Ponens: The Modus Ponens rule is one of the most important rules of inference, and it states that if P and P → Q is true, then we can infer that Q will be true. It can be represented as: Example: Statement-1: "If I am sleepy then I go to bed" ==> P→ Q Statement-2: "I am sleepy" ==> P Conclusion: "I go to bed." ==> Q. Hence, we can say that, if P→ Q is true and P is true then Q will be true .

70. 2. Modus Tollens: 17-Jun-23 By:Bifa H. 70 the Modus Tollens rule state that if P→ Q is true and  ¬ Q is true, then ¬ P  will also true. It can be represented as:

71. 3. Hypothetical Syllogism: 17-Jun-23 By:Bifa H. 71 The Hypothetical Syllogism rule state that if P→R is true whenever P→Q is true, and Q→R is true. It can be represented as the following notation: Example: Statement-1:  If you have my home key then you can unlock my home.  P→Q Statement-2:  If you can unlock my home then you can take my money.  Q→R Conclusion:  If you have my home key then you can take my money.  P→R

72. Cont…. 17-Jun-23 By:Bifa H. 72

73. 4. Disjunctive Syllogism: 17-Jun-23 By:Bifa H. 73 The Disjunctive syllogism rule state that if P∨Q is true, and ¬P is true, then Q will be true. the Disjunctive syllogism rule state that if P∨Q is true, and ¬P is true, then Q will be true. It can be represented as: Example: Statement-1:   Today is Sunday or Monday. ==>P∨Q Statement-2:   Today is not Sunday. ==> ¬P Conclusion:   Today is Monday. ==> Q

74. 5. Addition: 17-Jun-23 By:Bifa H. 74 the Addition rule is one the common inference rule, and it states that If P is true, then P∨Q will be true.

75. 6. Simplification: 17-Jun-23 By:Bifa H. 75

76. 7. Resolution: 17-Jun-23 By:Bifa H. 76

77. First-Order Logic in Artificial intelligence 17-Jun-23 By:Bifa H. 77 First-order logic is another way of knowledge representation in artificial intelligence. It is an extension to propositional logic. FOL is sufficiently expressive to represent the natural language statements in a concise way. First-order logic is also known as  Predicate logic or First-order predicate logic . First-order logic is a powerful language that develops information about the objects in a more easy way and can also express the relationship between those objects.

78. Cont…. 17-Jun-23 By:Bifa H. 78 First-order logic (like natural language) does not only assume that the world contains facts like propositional logic but also assumes the following things in the world: Objects :  A, B, people, numbers, colors, wars, theories, squares, pits, wumpus Relations:   It can be unary relation such as:  red, round, is adjacent,  or n-any relation such as:  the sister of, brother of, has color, comes between Function:  Father of, best friend, third inning of, end of, As a natural language, first-order logic also has two main parts: Syntax Semantics

79. Cont… 17-Jun-23 By:Bifa H. 79 First-order logic statements can be divided into two parts: Subject :  Subject is the main part of the statement. Predicate:  A predicate can be defined as a relation, which binds two atoms together in a statement. Consider the statement: "x is an integer.“ it consists of two parts, the first part x is the subject of the statement and second part "is an integer," is known as a predicate.

80. Quantifiers in First-order logic: 17-Jun-23 By:Bifa H. 80 A quantifier is a language element which generates quantification, and quantification specifies the quantity of specimen in the universe of discourse. These are the symbols that permit to determine or identify the range and scope of the variable in the logical expression. There are two types of quantifier: Universal Quantifier : (for all, everyone, everything) Existential quantifier :(for some, at least one).

81. Universal Quantifier : 17-Jun-23 By:Bifa H. 81 Universal quantifier is a symbol of logical representation, which specifies that the statement within its range is true for everything or every instance of a particular thing. The Universal quantifier is represented by a symbol ∀ , which resembles an inverted A . If x is a variable, then ∀x is read as: For all x For each x For every x .

82. Existential Quantifier: 17-Jun-23 By:Bifa H. 82 Existential quantifiers are the type of quantifiers, which express that the statement within its scope is true for at least one instance of something. It is denoted by the logical operator ∃, which resembles as inverted E. When it is used with a predicate variable then it is called as an existential quantifier. If x is a variable, then existential quantifier will be ∃x or ∃(x). And it will be read as: There exists a 'x.' For some 'x.' For at least one 'x.'

83. Knowledge-Based Agent in Artificial intelligence 17-Jun-23 By:Bifa H. 83 An intelligent agent needs  knowledge  about the real world for taking decisions and  reasoning  to act efficiently. Knowledge-based agents are those agents who have the capability of maintaining an internal state of knowledge, reason over that knowledge, update their knowledge after observations and take actions . These agents can represent the world with some formal representation and act intelligently. Knowledge-based agents are composed of two main parts: Knowledge-base and Inference system .

84. Cont… 17-Jun-23 By:Bifa H. 84 A knowledge-based agent must able to do the following: An agent should be able to represent states, actions, etc. An agent Should be able to incorporate new percepts An agent can update the internal representation of the world An agent can deduce the internal representation of the world An agent can deduce appropriate actions.

85. The architecture of knowledge-based agent: 17-Jun-23 By:Bifa H. 85

86. Cont… 17-Jun-23 By:Bifa H. 86 The knowledge-based agent (KBA) take input from the environment by perceiving the environment. The input is taken by the inference engine of the agent and which also communicate with KB to decide as per the knowledge store in KB. The learning element of KBA regularly updates the KB by learning new knowledge.

87. Knowledge base: 17-Jun-23 By:Bifa H. 87  Knowledge-base is a central component of a knowledge-based agent, it is also known as KB. It is a collection of sentences (here 'sentence' is a technical term and it is not identical to sentence in English). These sentences are expressed in a language which is called a knowledge representation language . The Knowledge-base of KBA stores fact about the world .

88. Inference system 17-Jun-23 By:Bifa H. 88 Inference means deriving new sentences from old. Inference system allows us to add a new sentence to the knowledge base. A sentence is a proposition about the world. Inference system applies logical rules to the KB to deduce new information. Inference system generates new facts so that an agent can update the KB. An inference system works mainly in two rules which are given as: Forward chaining Backward chaining

89. Cont…. 17-Jun-23 By:Bifa H. 89

90. Operations Performed by KBA 17-Jun-23 By:Bifa H. 90 Following are three operations which are performed by KBA in order to show the intelligent behavior: TELL :   This operation tells the knowledge base what it perceives from the environment . ASK:   This operation asks the knowledge base what action it should perform . Perform:   It performs the selected action.

91. Various levels of knowledge-based agent: 17-Jun-23 By:Bifa H. 91 A knowledge-based agent can be viewed at different levels which are given below: 1. Knowledge level Knowledge level is the first level of knowledge-based agent, and in this level, we need to specify what the agent knows, and what the agent goals are. 2. Logical level: At this level, we understand that how the knowledge representation of knowledge is stored. 3. Implementation level: This is the physical representation of logic and knowledge. At the implementation level agent perform actions as per logical and knowledge level.

92. Probabilistic reasoning: 17-Jun-23 By:Bifa H. 92 Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty. We use probability in probabilistic reasoning because it provides a way to handle the uncertainty that is the result of someone's laziness and ignorance. Probability:  Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal uncertainties.

93. Bayes ' theorem in Artificial intelligence 17-Jun-23 By:Bifa H. 93 Bayes ' theorem is also known as  Bayes ' rule, Bayes ' law , or   Bayesian reasoning , which determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events. The  Bayesian inference  is an application of Bayes' theorem, which is fundamental to Bayesian statistics .

94. Need of probabilistic reasoning in AI: 17-Jun-23 By:Bifa H. 94 When there are unpredictable outcomes. When specifications or possibilities of predicates becomes too large to handle. When an unknown error occurs during an experiment. In probabilistic reasoning, there are two ways to solve problems with uncertain knowledge : Bayes ' rule Bayesian Statistics

95. Application of Bayes ' theorem in Artificial intelligence: 17-Jun-23 By:Bifa H. 95 Following are some applications of Bayes ' theorem: It is used to calculate the next step of the robot when the already executed step is given. Bayes ' theorem is helpful in weather forecasting. It can solve the Monty Hall problem.

96. Reading Assignments 17-Jun-23 By:Bifa H. 96 Bayesian reasoning Probabilistic reasoning Temporal reasoning