1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

INTRODUCTION:

Artificial Intelligence is one of the booming technologies of computer science, which is ready to create a new revolution in the world by making intelligent machines. AI is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, painting etc. AI holds a tendency to cause a machine to work as a human.

What is Artificial Intelligence?

Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power."

So, we can define AI as, "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions."

Why Artificial Intelligence?

· With the help of AI, we can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc.

· With the help of AI, we can create your personal virtual Assistant, such as Cortana, Google Assistant etc.

· With the help of AI, we can build such Robots which can work in an environment where survival of humans can be at risk.

· AI opens a path for other new technologies, new devices, and new Opportunities.

Goals of Artificial Intelligence: Following are the main goals of Artificial Intelligence:

1. Replicate human intelligence

2. Solve Knowledge-intensive tasks

3. An intelligent connection of perception and action

4. Building a machine which can perform tasks that requires human intelligence such as:

· Proving a theorem

· Playing chess

· Plan some surgical operation

5. Driving a car in traffic Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.

Definition: “AI is the study of how to make computers do things at which, at the moment, people are better”.

Advantages of Artificial Intelligence:

1. High Accuracy with fewer errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.

2. High-Speed: AI systems can be of very high-speed and fast-decision making; because of that AI systems can beat a chess champion in the Chess game.

3. High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.

4. Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.

5. Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.

6. Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.

Disadvantages of Artificial Intelligence:

1. High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements.

2. Can't think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed.

3. No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken.

4. Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities.

5. No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative.

HISTORY OF AI:

Year 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons.

Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning.

Year 1950: The Alan Turing who was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test.

Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems.

Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field.

Year 1966: The researchers emphasized developing algorithms which can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA.

Year 1972: The first intelligent humanoid robot was built in Japan which was named as WABOT-1.

Year 1974-1980: The duration between years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientist dealt with a severe shortage of funding from government for AI researches.

Year 1980: After AI winter duration, AI came back with "Expert System". Expert systems were programmed that emulate the decision-making ability of a human expert.

Year 1987-1993: The duration between the years 1987 to 1993 was the second AI Winter duration. Again Investors and government stopped in funding for AI research as due to high cost but not efficient result.

Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion.

Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.

Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.

Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve the complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.

Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction.

Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test." Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well.

Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high intelligence.

INTELLIGENT SYSTEMS:

AI is the combination of computer science, physiology and philosophy. AI is a broad topic consisting of different fields from Machine vision to expert systems. John McCarthy was one of the founders of AI field who stated that “AI is the science and engineering of making intelligent machines, especially intelligent computer programs”.

Other Definitions of AI:

1. AI is the study of mental faculties through the use computational models.

2. The art of creating machines that performs functions which require intelligence when performed by people.

3. AI is a field of study that seeks to explain & emulate intelligent behavior in terms of computational processes.

4. AI is the study of how to make computers do things at which, at the moment, people are better.

5. AI is the study of the computations that make it possible to perceive reason & act.

6. AI is the branch of computer science that is concerned with the automation of intelligent behavior.

ELIZA: ELIZA was a program that conversed with user in English. The program was able to converse about any subject, because it stored subject information in data banks. Another feature of ELIZA was its ability to pick up speech patterns from user’s questions & provide responses using those patterns. The following is a passage that contains dialogue between Eliza & a teenage girl

ELIZA: Hello. I am Eliza. GIRL: Can you help me?

ELIZA: Don’t you believe that I can help you? GIRL: Yes.

ELIZA: I see.

GIRL: I feel depressed.

ELIZA: How long have you been feeling depressed? GIRL: Since last month.

ELIZA: That is quite interesting. GIRL: What is interesting in this? ELIZA: Have you asked anyone else? GIRL: NO.

ELIZA: Are you saying no just to be negative? GIRL: NO I did not ask anyone.

ELIZA: Why no?

GIRL: I do not believe other people.

ELIZA: Say, do you have any psychological problems? GIRL: I do not know.

ELIZA: Tell me more.

Since then a lot many versions of ELIZA have been developed & are available on the internet. The basic philosophy & characteristics in all these programs are same.

Characteristics of ELIZA:

1. Simulation of Intelligence

2. Quality of Response

3. Coherence

4. Semantics

Simulation of Intelligence: Eliza programs are not intelligent at all in real sense. The do not understand the meaning of utterance. Instead these programs simulate intelligent behavior quite effectively by recognizing keywords & phrases. By using a table lookup, one of a few ways of responding question is chosen.

Quality of Response: It is limited by the sophistication of the ways in which they can process the input text at a syntactic level. For example, the number of templates available is a serious limitation. However, the success depends heavily on the fact that the user has a fairly restricted notion of the expected response from the system. Coherence: The earlier version of the system imposed no structure on the conversation. Each statement was based entirely on the current input & no context information was used. More complex versions of Eliza can do a little better. Any sense of intelligence depends strongly on the coherence of the conversation as judged by the user.

Semantics: Such systems have no semantic representation of the content of either the user’s input or the reply. That is why we say that it does not have intelligence of understanding of what we are saying. But it looks that it imitates the human conversation style. Because of this, it passed Turing test.

Categorization of Intelligent Systems:

· System that thinks like humans

· System that acts like humans

· System that thinks rationally

· Systems that acts rationally

1. System that thinks like humans: This requires cognitive modeling approaches. Most of the time, it is a black box where we are not clear about our thought process. One has to know the functioning of the brain & its mechanism for processing information.

2. System that acts like humans: This requires that the overall behavior of the system should be human like which could be achieved by observation. Turing test is an example.

Turing Test: Turing Test was introduced by Alan Turing in 1950. A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human.

To conduct this test, we need two people and a machine to be evaluated. One person plays the role of an interrogator, who is in a separate room from the computer and the other person. The interrogator can ask questions of either the person or the computer by typing the questions and receiving typed responses. The interrogator knows them only as A and B and aims to determine which is the person and is a machine. The goal of the machine is to fool the interrogator into believing that the machine can think. If a large multiplication, for example, given the computer can give a wrong answer by taking a long time for calculation as a man can do, to fool the interrogator.

Turing proposed that if the human interrogator in Room C is not able to identify who is in Room A or in Room B, then the machine possesses intelligence. Turing considered this is a sufficient test for

attributing thinking capacity to a machine. As of today, Turing test is the ultimate test a machine must pass in order to be called as intelligent test.

3. System that thinks rationally: This relies on logic rather than human to measure correctness. For example, given John is a human and all humans are mortal than one can conclude logically that John is mortal.

4. System that acts rationally: This is the final category of intelligent system whereby rational behavior we mean doing the right thing. Even if the method is illogical, the observed behavior must be rational.

Components of AI:

· Knowledge base

· Control strategy

· Inference mechanism

Knowledge base: Ai programs should be learning in nature & update its knowledge accordingly. Knowledge base generally consists of facts & rules & has the following characteristics:

· It is voluminous in nature & requires proper structuring

· It may be incomplete & imprecise

· It may be dynamic & keep on changing

Control Strategy: It determines which rule to be applied. To know this rule, some heuristics or thumb rules based on problem domain may be used.

Inference mechanism: It requires search through knowledge base & derives new knowledge using the existing knowledge with the help of inference rules.

FOUNDATIONS OF AI:

The foundations of AI in various fields are as follows

· Mathematics

· Neuroscience

· Control theory

· Linguistics

Mathematics: AI systems use formal logical methods & Boolean logic, Analysis of limits to what to be computed, probability theory & uncertainty that forms the basis for most approaches to AI, fuzzy logic etc.

Neuroscience: This science of medicine helps in studying the function of brain. Recent studies use accurate sensors to correlate brain activity to human thought. By monitoring individual neurons, monkeys can now control a computer mouse using thought alone. Moore’s law states that the computers will have as many gates as humans have neurons. Researchers are working to know as to how to have a mechanical brain. Such systems will require parallel computation, remapping and interconnections to a large extent.

Control Theory: Machines can modify their behaviour in response to the environment. Steam engine governor, thermostat & water flow regulator are few examples of Control Theory. This theory of stable feedback systems helps in building systems that transition from initial state to goal state with minimum energy.

Linguistics: Speech demonstrates so much of human intelligence. Analysis of human language reveals thought taking place in ways not understood in other settings. Children can create sentences they have never heard before. Languages & thoughts are believed to be tightly intertwined.

APPLICATIONS OF AI:

1. Gaming: AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

2. Natural Language Processing: It is possible to interact with the computer that understands natural language spoken by humans.

3. Expert Systems: There are some applications which integrate machine, software and special information to impart reasoning and advising. They provide explanation and advice to the users.

4. Vision Systems: These systems understand, interpret and comprehend visual input on the computer. For example,

a. A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

b. Doctors use clinical expert system to diagnose the patient.

c. Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

5. Speech Recognition: Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

6. Handwriting Recognition: The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and converts it into editable text.

7. Intelligent Robots: Robots are able to perform the tasks given by humans. They have special sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump,

and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

TIC-TAC-TOE GAME PLAYING:

TIC-TAC-TOE is a two-player game with one player marking O & other marking X, at their turn in the spaces in a 3X3 grid. The player who succeeds in playing 3 respective marks in any horizontal, vertical or diagonal row wins the game.

Here we are considering one human player & the other player to be a computer program. The objective to play this game using computer is to write a program which never loses. Below are 3 approaches to play this game which increase in

· Complexity

· Use of generalization

· Clarity of their knowledge

· Extensibility of their approach

Approach 1:

Let us represent 3X3 board as nine elements vector. Each element in a vector can contain any of the following 3 digits:

· 0 - representing blank position

· 1 - indicating X player move

· 2 - indicating O player move

It is assumed that this program makes use of a move table that consists of vector of 39 (19683) elements.

Index	Current Board Position	New Board Position
0	000000000	000010000
1	000000001	020000001
2	000000002	000100002
3	000000010	002000010
	. . .

All the possible board positions are stored in Current Board Position column along with its corresponding next best possible board position in New Board Position column.

Algorithm:

· View the vector (board) as a ternary number.

· Get an index by converting this vector to its corresponding decimal number.

· Get the vector from New Board Position stored at the index. The vector thus selected represents the way the board will look after the move that should be made.

· So set board position equal to that vector. Advantage:

· Very efficient in terms of time. Disadvantages:

· Requires lot of memory space to store move table.

· Lot of work is required to specify entries in move table manually.

· This approach cannot be extended to 3D TIC-TAC-TOE as 3²⁷ board positions are to be stored.

Approach 2:

The board B[1..9] is represented by a 9-element vector.

· 2 - Representing blank position

· 3 - Indicating X player move

· 5 - Indicating O player move

In his approach we use the following 3 sub procedures.

· Go(n) – Using this function computer can make a move in square n.

· Make_2 – This function helps the computer to make valid 2 moves.

· PossWin(P) – If player P can win in the next move then it returns the index (from 1 to 9) of the square that constitutes a winning move, otherwise it returns 0.

The strategy applied by human for this game is that if human is winning in the next move the human plays in the desired square, else if human is not winning in the next move then one checks if the opponent is winning. If so then block that square, otherwise try making valid 2 in any row, column (or) diagonal.

The function PossWin operates by checking, one at a time, for each of rows/columns & diagonals.

· If PossWin(P)=0, then P cannot win. Find whether opponent can win. If so then block it. This can be achieved as follows:

· If (3*3*2=18) then X player can win as there is one blank square in row, column (or) diagonal.

· If (5*5*2=50) then player O can win. Advantages:

· Memory Efficient

· Easy to understand Disadvantages:

· Not as efficient as first one with respect to time

· This approach cannot be extended to 3D TIC-TAC-TOE

Approach 3:

In this approach, we choose board position to be a magic square of order 3; blocks numbered by magic number. The magic square of order n consists of n² distinct numbers (from 1 to n²), such that the numbers in all rows, all columns & both diagonals sum to be 15.

In this approach, we maintain a list of the blocks played by each player. For the sake of convenience, each block is identified by its number. The following strategy for possible win for a player is used.

· Each pair of blocks a player owns is considered.

· Difference D between 15 & the sum of the two blocks is computed.

o If D<0 or D>9, then these two blocks are not collinear & so can be ignored. Otherwise if the block representing difference is blank (not in either list) then player can move in that block.

· This strategy will produce a possible win for a player.

Execution: Here, Human uses mind & Machine uses calculations. Assuming that Machine is the first player Move 1: Machine: Takes the element 5.

Move 2: Human: Takes the element 8.

Move 3: Machine: Takes the element 4. So, the machine elements consists of 5, 4. Move 4: Human: Takes the element 6. So, the human elements consists of 8, 6.

Move 5: Machine: From the above moves machine has the elements 5, 4. First machine checks whether it can win. 5+4=9, 15-9=6. Since the element 6 is already taken by the human, there is no possibility of machine winning in this move. Now machine checks whether human can win (or) not. The elements taken by human 8,

6. So, 8+6=14, 15-14=1. Since the element 1 is available the machine takes the element 1. Finally, the elements taken by the Machine are 5, 4, 1.

Move 6: Human: Takes the element 3. So, the human elements consist of 8, 6, 3.

Move 7: Machine: Considering all the elements taken by the 5, 4, 1 now 5+1=6, 15-6=9. Since the element 9 is available machine takes the element 9.

Therefore, 1+5+9=15, which leads to Machine winning state. Advantage:

· This approach could extend to handle three-dimensional TIC-TAC-TOE. Disadvantage:

· Requires more time than other 2 approaches as it must search a tree representing all possible move sequences before making each move.

DEVELOPMENT OF AI LANGUAGES:

AI Languages have traditionally been those which stress on knowledge representation schemes, pattern matching, flexible search & programs as data. Examples of such languages are

· PYTHON

· LISP

· PROLOG

· R-Language

· JAVA

PYTHON:

Python is viewed as in any case in the rundown of all Artificial Intelligence (AI) development programming languages because of the simplicity.

The programming syntax and data structures of the python very simple and easily learned. Accordingly, numerous Artificial Intelligence (AI) algorithms can be effectively executed in it.

Python takes short advancement time in comparison to other programming languages like Java, C#, C++, and Ruby. It supports functional, object-oriented as well as procedure-oriented styles of programming.

There are a lot of libraries in python, which make our tasks simpler. Python has a lot of libraries that solve many scientific computations. For instance: Numpy is a library for python that causes us to settle numerous logical calculations. Additionally, we have Pybrain, which is for utilizing Artificial Intelligence (AI) in Python

LISP:

LISP is a functional Language based on lambda Calculus.

Lisp is one of the oldest and the most popular suited programming languages for Artificial Intelligence (AI) development. It was developed by John McCarthy, the father of Artificial Intelligence (AI) in 1958. It can process symbolic data effectively.

Its great prototyping abilities and simple dynamic creation of new objects, with automatic garbage collection feature. Its development life cycle allows interactive evaluation of expressions and recompilation of functions or documents while the program is as yet running. Throughout the years, due to advancement, many of these features have migrated into many other programming languages in this manner influencing the uniqueness of Lisp.

Lisp is a group of programming languages, of which the most famous languages are Clojure and Common Lisp. Compared to other programming languages on this list, Lisp has the longest history. Accordingly, it had a lot of influence on the development of R, Python, and JavaScript languages.

Lisp is a general-purpose and dynamically typed programming language but has found its mostly used in the area of traditional, symbolic AI

PROLOG: It is based on first-order predicate logic. Both the languages are declarative languages where one is concerned about ‘what to compute’ & not ‘how to compute’.

Prolog is a declarative programming language where the programs expressed in terms of relations, and execution happens by running inquiries over these relations. Prolog is especially useful for database, symbolic reasoning, and language parsing applications. Prolog is broadly used in Artificial Intelligence (AI) today.

Prolog is a logic programming language and computational phonetics that are related to artificial intelligence (AI). Prolog has its first-order logic, a formal logic, and unlike as many other programming languages, Prolog is planned basically as a definitive programming language, the prolog program logic is expressed as far as relations, represented as facts, rules, and standards. A calculation is started by running a question over these relations.

Prolog was one of the main logic programming languages and remains the most well known among such logic programming languages today. The language has been utilized for hypothesis demonstrating, master frameworks, term rewriting, type systems, and automated planning, just as its unique proposed field of use, natural language processing.

Present-day Prolog environments support the creation of graphical user interfaces (GUI), just as authoritative and organized applications. Prolog is well-designed for specific tasks that fit by standard-based logical queries, like, voice control systems, searching databases, etc

R-Language:

R for a long time is an equivalent word for data science & technology. It is interpreted and dynamically typed programming language.

R is one of the best programming language and environment for analyzing and controlling the data for statistical purposes. Using R, we can easily produce a well-structured production quality plot, including mathematical symbols and formulae where required.

It is the very useful general-purpose programming language for AI, R has various packages like RODBC, Gmodels, Class and Tm which are utilized in the field of Artificial Intelligence (AI), Machine learning (ML). These packages help in the implementation of machine learning algorithms easily, It is used to splitting the business-related issues.

Many organizations use R for data analysis, big-data modeling, and visualization. Some of them are Google, Uber, the New York Times. R has a wide utilization in banking, particularly in the fields for predicting different risks. In this area, I would specify Bank of America and ANZ Bank.

JAVA:

Java can also be considered as a good choice for Artificial intelligence (AI) and Machine Learning (ML) development. Artificial intelligence has a lot to do with search algorithms, artificial neural networks, and genetic programming.

Java provides many benefits: easy use, debugging ease, package services, simplified work with large-scale projects, graphical representation of data and better user interaction.

It also has the abstract of Swing and SWT (the Standard Widget Toolkit). These tools make graphical and user-interfaces look appealing & sophisticated.

Java can likewise be considered as a good choice for Artificial intelligence (AI) development. Java gives numerous advantages: simple use, investigating ease, bundle administrations, improved work with enormous scale ventures, the graphical portrayal of information and better client association.

It likewise has the fuse of Swing and SWT (the Standard Widget Toolkit). These devices make illustrations and interfaces look engaging and complex.

Java is compiled and strongly typed in the general-purpose programming language. In programming, it’s a standard language, and it is not falling for its popularity for many years. The execution of the program is greatly improved compared to other programming languages. But learning and coding are more complex than other programming languages.

It is used as a wide range of applications development like games, web, mobile and desktop applications. java can be a good choice for Machine Learning (ML), especially all of the businesses are based on java. It can make challenges in this field, even senior designers. Along these lines, Python and R are more dominant than in Machine Learning (ML).

CURRENT TRENDS IN AI:

· Hard Computing Techniques

· Soft Computing Techniques

Conventional Computing (Hard Computing) is based on the concept of precise modeling & analyzing to yield accurate results. Hard Computing Techniques works well for simple problems, but is bound by NP-complete set which include problems often occurring in biology, medicine, humanities, management sciences & similar fields.

Soft Computing is a formal computer Science area of study which refers to a collection of computational techniques in computer science, machine learning & some engineering disciplines, which study, model & analyze very complex phenomena. Components of Soft Computing include Neural Networks, Fuzzy Systems, Evolutionary Algorithms, Swarm Intelligence etc.

· Neural Networks have been developed based on functioning of human brains. Attempts to model the biological neuron have led to development of the field called Artificial Neuron Network.

· 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.

· Evolutionary techniques mostly involve meta-heuristic optimization algorithms such as evolutionary algorithms & Swarm Intelligence.

· Genetic algorithms were developed mainly by emulating nature & behavior of biological chromosome.

· Ant colony algorithm was developed to emulate the behavior of real ants. An ant algorithm is one in which a set of artificial ants (agents) cooperate to find the solution of a problem by exchanging information on graph edges.

· Swarm Intelligence (SI) is a type of AI based on the collective behavior of decentralized, self organized systems. Social insects like ants, bees, wasps & termites perform their tasks independent of other members of the colony. However, they are able to solve complex problems emerging in their daily lives by mutual cooperation. This emergent behavior of self organization by a group of social insects is known as Swarm Intelligence.

· Expert system continues to remain an attractive field for its practical utility in all walk of real life.

· Emergence of Agent technology as a subfield of AI is a significant paradigm shift for software development & break through as a new revolution. Agents are generally suited in some environment & are capable of taking autonomous decisions while solving a problem. Multi Agent Systems are designed using several independent & interacting agents to solve the problems of distributed nature.

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CRR CSE JNTUK R19 ARTIFICIAL INTELLIGENCE

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