Research areas of AI

Fuzzy Logic

Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.

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:

CERTAINLY	YES
POSSIBLY	YES
CANN0T	SAY
POSSIBLY	NO
CERTAINLY	NO

The fuzzy logic works on the levels of possibilities of input to achieve the definite output.

Why fuzzy logic is been used?

Fuzzy logic is used for both commercial and practical purpose.

 It can control machines and consumer products.

 It may not give accurate reasoning, but acceptable reasoning.

 Fuzzy logic helps to deal with the uncertainty in engineering.

Fuzzy logic system architecture

It has four main parts as shown:

1. Fuzzification Module: transforms the system inputs, which are crisp numbers, into

fuzzy sets.

It splits the input signal into five steps such as:

LP	x is Large Positive
MP	x is Medium Positive
S	x is Small
MN	x is Medium Negative
LN	x is Large Negative

2. Knowledge Base: It stores IF-THEN rules provided by experts.

3. Inference Engine: It simulates the human reasoning process by making fuzzy

inference on the inputs and IF-THEN rules.

4. Defuzzification Module: It transforms the fuzzy set obtained by the inference engine

into a crisp value.

Membership Functions

Membership functions allow you to quantify linguistic term 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].

Here, each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership. 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 use of complex functions does not add more precision in the output. All membership functions for LP, MP, S, MN, and LN are shown as below:

Membership Functions

Membership functions allow you to quantify linguistic term 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].

Here, each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership. 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 use of complex functions does not add more precision in the output. 

Application area of fuzzy system

The key application areas of fuzzy logic are as given:

Automotive Systems

 Automatic Gearboxes

 Four-Wheel Steering

 Vehicle environment control

Consumer Electronics

 Hi-Fi Systems

 Photocopiers

 Still and Video Cameras

 Television

Domestic Goods

 Microwave Ovens

 Refrigerators

 Toasters

 Vacuum Cleaners

 Washing Machines

Environment Control

 Air Conditioners/Dryers/Heaters

 Humidifiers

Natural Language Processing (NLP)

Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.

The field of NLP involves making computers to perform useful tasks with the natural languages

humans use. The input and output of an NLP system can be:

 Speech

 Written Text

Components of NLP

There are two components of NLP as given:

Natural Language Understanding (NLU)

Understanding involves the following tasks:

 Mapping the given input in natural language into useful representations.

 Analyzing different aspects of the language.

Natural Language Generation (NLG)

It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.

It involves:

 Text planning: It includes retrieving the relevant content from knowledge base.

 Sentence planning: It includes choosing required words, forming meaningful phrases, setting tone of the sentence.

 Text Realization: It is mapping sentence plan into sentence structure. The NLU is harder than NLG.

NLP terminology

 Phonology: It is study of organizing sound systematically.

 Morphology: It is a study of construction of words from primitive meaningful units.

 Morpheme: It is primitive unit of meaning in a language.

 Syntax: It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases.

 Semantics: It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences.

 Pragmatics: It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected.

 Discourse: It deals with how the immediately preceding sentence can affect the interpretation of the next sentence.

 World Knowledge: It includes the general knowledge about the world.

Expert Systems

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Characteristics of Expert Systems

 High performance

 Understandable

 Reliable

 Highly responsive

Capabilities of expert system

The expert systems are capable of:

 Advising

 Instructing and assisting human in decision making

 Demonstrating

 Deriving a solution

 Diagnosing

 Explaining

 Interpreting input

 Predicting results

 Justifying the conclusion

 Suggesting alternative options to a problem

They are incapable of:

 Substituting human decision makers

 Possessing human capabilities

 Producing accurate output for inadequate knowledge base

 Refining their own knowledge

Knowledge base

Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon

the collection of highly accurate and precise knowledge.

What is Knowledge?

The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.

Components of Knowledge Base

The knowledge base of an ES is a store of both, factual and heuristic knowledge.

 Factual Knowledge – It is the information widely accepted by the Knowledge Engineers and scholars in the task domain.

 Heuristic Knowledge – It is about practice, accurate judgment, one’s ability of evaluation, and guessing.

Inference engine

In case of rule based ES, it:

 Applies rules repeatedly to the facts, which are obtained from earlier rule application.

 Adds new knowledge into the knowledge base if required.

 Resolves rules conflict when multiple rules are applicable to a particular case

To recommend a solution, the inference engine uses the following strategies:

 Forward Chaining

 Backward Chaining

Forward Chaining

It is a strategy of an expert system to answer the question, “What can happen next?”

Here, the inferance engine follows the chain of conditions and derivations and finally deduces the outcome. It considers all the facts and rules, and sorts them before concluding to a solution.

This strategy is followed for working on conclusion, result, or effect. For example, prediction of share market status as an effect of changes in interest rates.

  

Backward Chaining

With this strategy, an expert system finds out the answer to the question, “Why this happened?”

On the basis of what has already happened, the inference engine tries to find out which

conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason. For example, diagnosis of blood cancer in humans.

  

ES technologies

There are several levels of ES technologies available. Expert systems technologies include:

1. Expert System Development Environment: The ES development environment includes hardware and tools. They are:

o Workstations, minicomputers, mainframes

o High level Symbolic Programming Languages such as LISt Programming (LISP)

and PROgrammationen LOGique (PROLOG).

o Large databases

2. Tools: They reduce the effort and cost involved in developing an expert system to large extent.

o Powerful editors and debugging tools with multi-windows.

o They provide rapid prototyping

o Have Inbuilt definitions of model, knowledge representation, and inference design.

3. Shells: A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface, and explanation facility.

For example, few shells are given below:

o Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.

o Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables knowledge encoding in the form of IF-THEN rules.

Benefits of Expert Systems

 Availability: They are easily available due to mass production of software.

 Less Production Cost: Production cost is reasonable. This makes them affordable.

 Speed: They offer great speed. They reduce the amount of work an individual puts in.

 Less Error Rate: Error rate is low as compared to human errors.

 Reducing Risk: They can work in the environment dangerous to humans.

 Steady response: They work steadily without getting motional, tensed or fatigued