PAIML-UNIT 3dfgbgfdbdfgbdfbdfbdfbdfb.pptx

gfdfgdfgdfg

1. Reasoning in Artificial intelligence In previous topics, we have learned various ways of knowledge representation in artificial intelligence. Now we will learn the various ways to reason on this knowledge using different logical schemes .

2. Reasoning The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. Or we can say, " Reasoning is a way to infer facts from existing data ." It is a general process of thinking rationally, to find valid conclusions. In artificial intelligence, the reasoning is essential so that the machine can also think rationally as a human brain, and can perform like a human.

3. Types of Reasoning Deductive reasoning Inductive reasoning Abductive reasoning Common Sense Reasoning Monotonic Reasoning Non-monotonic Reasoning Note: Inductive and deductive reasoning are the forms of propositional logic

4. 1. Deductive reasoning: Deductive reasoning is deducing new information from logically related known information. It is the form of valid reasoning, which means the argument's conclusion must be true when the premises are true. Deductive reasoning is a type of propositional logic in AI, and it requires various rules and facts. It is sometimes referred to as top-down reasoning, and contradictory to inductive reasoning. In deductive reasoning, the truth of the premises guarantees the truth of the conclusion.

5. Deductive reasoning mostly starts from the general premises to the specific conclusion, which can be explained as below example. Example: Premise-1: All the human eats veggies Premise-2: Suresh is human. Conclusion: Suresh eats veggies.

6. 2. Inductive Reasoning : Inductive reasoning is a form of reasoning to arrive at a conclusion using limited sets of facts by the process of generalization. It starts with the series of specific facts or data and reaches to a general statement or conclusion. Inductive reasoning is a type of propositional logic, which is also known as cause-effect reasoning or bottom-up reasoning. In inductive reasoning, we use historical data or various premises to generate a generic rule, for which premises support the conclusion.

7. In inductive reasoning, premises provide probable supports to the conclusion, so the truth of premises does not guarantee the truth of the conclusion . Example: Premise: All of the pigeons we have seen in the zoo are white. Conclusion: Therefore, we can expect all the pigeons to be white

8. 3. Abductive reasoning : Abductive reasoning is a form of logical reasoning which starts with single or multiple observations then seeks to find the most likely explanation or conclusion for the observation. Abductive reasoning is an extension of deductive reasoning, but in abductive reasoning, the premises do not guarantee the conclusion.

9. Example: Implication:  Cricket ground is wet if it is raining Axiom:  Cricket ground is wet. Conclusion It is raining.

10. 4. Common Sense Reasoning Common sense reasoning is an informal form of reasoning, which can be gained through experiences. Common Sense reasoning simulates the human ability to make presumptions about events which occurs on every day. It relies on good judgment rather than exact logic and operates on  heuristic knowledge  and  heuristic rules .

11. Example : One person can be at one place at a time. If I put my hand in a fire, then it will burn. The above two statements are the examples of common sense reasoning which a human mind can easily understand and assume.

12. 5. Monotonic Reasoning In monotonic reasoning, once the conclusion is taken, then it will remain the same even if we add some other information to existing information in our knowledge base. In monotonic reasoning, adding knowledge does not decrease the set of prepositions that can be derived. To solve monotonic problems, we can derive the valid conclusion from the available facts only, and it will not be affected by new facts. Monotonic reasoning is not useful for the real-time systems, as in real time, facts get changed, so we cannot use monotonic reasoning.

13. Monotonic reasoning is used in conventional reasoning systems, and a logic-based system is monotonic. Any theorem proving is an example of monotonic reasoning. Example: Earth revolves around the Sun. It is a true fact, and it cannot be changed even if we add another sentence in knowledge base like, "The moon revolves around the earth" Or "Earth is not round," etc.

14. 6. Non-monotonic Reasoning In Non-monotonic reasoning, some conclusions may be invalidated if we add some more information to our knowledge base. Logic will be said as non-monotonic if some conclusions can be invalidated by adding more knowledge into our knowledge base. Non-monotonic reasoning deals with incomplete and uncertain models.

15. "Human perceptions for various things in daily life, "is a general example of non-monotonic reasoning. Example:  Let suppose the knowledge base contains the following knowledge: Birds can fly Penguins cannot fly Pitty is a bird So from the above sentences, we can conclude that  Pitty can fly . However, if we add one another sentence into knowledge base " Pitty is a penguin ", which concludes " Pitty cannot fly ", so it invalidates the above conclusion.

16. Cause and effect reasoning Comparative reasoning Conditional reasoning Critical reasoning De-Compositional reasoning Set Based reasoning

17. What is Fuzzy Logic? Fuzzy Logic  (FL) is a method of reasoning that resembles  human reasoning . This approach is similar to how humans perform decision making. And it involves all intermediate possibilities between  YES  and  NO .

18. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh , observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as

19. Why do we use Fuzzy Logic? Generally, we use the fuzzy logic system for both commercial and practical purposes such as: It  controls machines  and  consumer products If not accurate reasoning, it at least provides  acceptable reasoning This helps in dealing with the  uncertainty in engineering So, now that you know about Fuzzy logic in AI and why do we actually use it, let’s move on and understand the architecture of this logic.

20. Fuzzy Logic Systems Architecture

21. Fuzzy Logic Systems Architecture It has four main parts as shown Rules  – It contains all the rules and the if-then conditions offered by the experts to control the decision-making system. The recent update in the fuzzy theory provides different effective methods for the design and tuning of  fuzzy controllers . Usually, these developments reduce the number of fuzzy rules. Fuzzification  – This step converts inputs or the crisp numbers into fuzzy sets. You can measure the crisp inputs by sensors and pass them into the  control system  for further processing. It splits the input signal into five steps such as-

22. Inference Engine  – It determines the degree of match between fuzzy input and the rules. According to the input field, it will decide the rules that are to be fired. Combining the fired rules, form the control actions. Defuzzification  – The Defuzzification process converts the fuzzy sets into a crisp value. There are different types of techniques available, and you need to select the best-suited one with an expert system.

23. This was about the architecture of fuzzy logic in AI. Now, let’s understand the membership function Membership Function The membership function is a  graph  that defines how each point in the  input space  is mapped to membership value between 0 and 1. It allows you to  quantify linguistic terms  and represent a fuzzy set graphically. A membership function for a fuzzy set A on the universe of discourse X is defined as  μA:X → [0,1]

24. It quantifies the degree of membership of the element in X to the fuzzy set A. x-axis  represents the universe of discourse. y-axis  represents the degrees of membership in the [0, 1] interval There can be multiple membership functions applicable to fuzzify a numerical value. Simple membership functions are used as the complex functions do not add precision in the output. The membership functions for  LP, MP, S, MN, and LN  are:

25. Applications of Fuzzy Logic The Fuzzy logic is used in various fields such as automotive systems, domestic goods, environment control, etc. Some of the common applications are: It is used in the  aerospace field  for  altitude control  of spacecraft and satellite. This controls the  speed and traffic  in the  automotive systems. It is used for  decision making support systems  and personal evaluation in the large company business. It also controls the pH, drying, chemical distillation process in the  chemical industry . Fuzzy logic is used in  Natural language processing  and various intensive  applications in Artificial Intelligence . It is extensively used in  modern control systems  such as expert systems. Fuzzy Logic mimics how a person would make decisions, only much faster. Thus, you can use it with  Neural Networks .

26. Advantages & Disadvantages of Fuzzy Logic The structure of Fuzzy Logic Systems is  easy and understandable Fuzzy logic is widely used for  commercial  and  practical purposes It helps you to  control machines  and consumer products It helps you to deal with the  uncertainty in engineering

27. Example of a Fuzzy Logic System This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value.

28. The rule-based expert systems consist of three important elements:   Set of Facts: These are assertions or anything relevant to the beginning state of the system. Set of Rules: It contains all actions that should be taken within the scope of a problem and specify how to act on the assertion set. Here, facts are represented in an IF-THEN form. Termination Criteria or Interpreter: Determines whether a solution exists or not, as well as when to terminate the process.

29. Rule-Based System Example: A domain-specific expert system that uses rules to make deductions or narrow down choices is one of the most popular as well as the classic example of rule-based systems. Furthermore, recent advancement in technology has given way to the development of modern machines and systems like: IKEA Virtual Assistant. Diagnostics Oriented Rockwell Intelligence System (DORIS). Machine for Intelligent Diagnosis (MIND).

30. Features of Rule-Based Systems: Widely used in Artificial Intelligence, Rule-Based Expert System is not just only responsible for modeling intelligent behavior in machines and building expert system that outperform human expert (s) but also helps:   Composed of combined knowledge of human experts in the problem domain. Represent knowledge in a highly declarative way. Enables the use of several different knowledge representations paradigms. Supports implementation of non-deterministic search and control strategies. It helps describe fragmentary, ill-structured, heuristic, judgemental knowledge. It is robust and can operate with uncertain or incomplete knowledge. Helps with rule-based decision making examples monitoring, control, diagnostics, service, etc.

31. Advantages of Rule-Based Systems: Being one of the core technologies responsible for making machines capable of rule-based learning , rule-based systems offer a range of advantages like:   Rule-based programming is easy to understand. It can be built to represent expert judgment in simple or complicated subjects. The cause-and-effect in Rule-Based Systems is transparent. It offers flexibility and an adequate mechanism to model several basic mental processes into machines. Mechanizes the reasoning process.

32. Disadvantages of Rule-Based Systems: Though exceptionally beneficial, rule-based systems have certain drawbacks associated with them, such as:   They require deep domain knowledge and manual work. Generating rules for a complex system is quite challenging and time-consuming. It has less learning capacity, as it generates results based on the rules.

33. Algorithm Define linguistic Variables and terms (start) Construct membership functions for them. (start) Construct knowledge base of rules (start) Convert crisp data into fuzzy data sets using membership functions. ( fuzzification ) Evaluate rules in the rule base. (Inference Engine) Combine results from each rule. (Inference Engine) Convert output data into non-fuzzy values. ( defuzzification )

34. Development Step 1 − Define linguistic variables and terms Linguistic variables are input and output variables in the form of simple words or sentences. For room temperature, cold, warm, hot, etc., are linguistic terms. Temperature (t) = {very-cold, cold, warm, very-warm, hot} Every member of this set is a linguistic term and it can cover some portion of overall temperature values.

35. Step 2 − Construct membership functions for them The membership functions of temperature variable are

36. Step3 − Construct knowledge base rules Create a matrix of room temperature values versus target temperature values that an air conditioning system is expected to provide .

37. Build a set of rules into the knowledge base in the form of IF-THEN-ELSE structures.

38. Step 4 − Obtain fuzzy value Fuzzy set operations perform evaluation of rules. The operations used for OR and AND are Max and Min respectively. Combine all results of evaluation to form a final result. This result is a fuzzy value.

39. Step 5 − Perform defuzzification Defuzzification is then performed according to membership function for output variable

40. Fuzzy Set Theory To learn about classical and Fuzzy set theory, firstly you have to know about what is set. Set A set is a term, which is a collection of unordered or ordered elements. Following are the various examples of a set: A set of all-natural numbers A set of students in a class. A set of all cities in a state. A set of upper-case letters of the alphabet.

41. Types of Set : Finite Empty Infinite Proper Universal Subset Singleton Equivalent Set Disjoint Set

42. Classical Set It is a type of set which collects the distinct objects in a group. The sets with the crisp boundaries are classical sets. In any set, each single entity is called an element or member of that set.

43. Mathematical Representation of Sets Any set can be easily denoted in the following two different ways 1. Roaster Form: This is also called as a tabular form. In this form, the set is represented in the following way: Set_name = { element1, element2, element3, ......, element N} The elements in the set are enclosed within the brackets and separated by the commas. Following are the two examples which describes the set in Roaster or Tabular form: Example 1: Set of Natural Numbers: N={1, 2, 3, 4, 5, 6, 7, ......,n).

44. 2. Set Builder Form: Set Builder form defines a set with the common properties of an element in a set. In this form, the set is represented in the following way : A = { x:p (x)} The following example describes the set in the builder form: The set {2, 4, 6, 8, 10, 12, 14, 16, 18} is written as: B = {x:2 ≤ x < 20 and (x%2) = 0}

45. Operations on Classical Set Following are the various operations which are performed on the classical sets: Union Operation Intersection Operation Difference Operation Complement Operation

46. Properties of Classical Set 1. Commutative Property 2. Associative Property 3. Idempotency Property 4. Absorption Property 5. Distributive Property 6. Identity Property 7. Transitive property 8. Ivolution property 9. De Morgan's Law