Unit - 1

Introduction

Q1) Define Artificial Intelligence (AI).

A1) The study of how to make computers do things at which at the moment, people are better.

1. Systems that think like humans
2. Systems that act like humans
3. Systems that think rationally
4. Systems that act rationally

Q2) What is meant by Turing test?

A2) To conduct this test we need two people and one machine. One person will be an interrogator questioner, will be asking questions to one person and one machine. Three of them will be in a separate room.

Interrogator knows them just as A and B. So it has to identify which is the person and machine.

The goal of the machine is to make Interrogator believe that it is the person’s answer. If machine

Succeeds by fooling Interrogator, the machine acts like a human.

Programming a computer to pass Turing test is very difficult.

Q3) What are the capabilities, computer should possess to pass Turing test?

A3)

1. Natural Language Processing
2. Knowledge representation
3. Automated reasoning
4. Machine Learning

Q4) What are the factors that a rational agent should depend on at any given time?

A4)

1. The performance measure that defines degree of success.

2. Ever thing that the agent has perceived so far. We will call this complete perceptual history the

Percept sequence.

3. When the agent knows about the environment.

4. The action that the agent can perform.

Q5) List the various type of agent program.

A5)

1. Simple reflex agent program.
2. Agent that keeps track of the world.
3. Goal based agent program.
4. Utility based agent program

Q6) Give the structure of agent in an environment?

A6) Agent interacts with environment through sensors and actuators.

An Agent is anything that can be viewed as perceiving (i.e.) understanding its environment through sensors and acting upon that environment through actuators.

Q7) What is important for task environment?

A7)

PEAS → P- Performance measure

E - Environment

A- Actuators

S – Sensors

Example

Interactive English tutor performance measure maximize student’s score on test.

Environment

Set of students testing Agency

Actuators

Display exercises suggestions, corrections.

Sensors

Keyboard entry

Q8) List the properties of environments?

A8)

1. Fully Observable Vs Partially Observable
2. Deterministic Vs Stochastic
3. Episodic Vs Sequential
4. Static Vs Dynamic
5. Discrete Vs Continuous
6. Single Agent Vs Multi agent

1. Competitive Multi agent

7.     Co – operative Multi agent

Q9) Differentiate Blind Search and Heuristic Search

A9)

Q10) Describe briefly the various problem characteristics?

A10) Heuristic search is a very general method applicable to a large class of problem. It includes a variety of techniques. In order to choose an appropriate method, it is necessary to analyze the problem with respect to the following considerations.

A very large and composite problem can be easily solved if it can be broken into smaller problems and recursion could be used. Suppose we want to solve.

Ex: ∫ x2 + 3x+sin2x cos 2x dx

This can be done by breaking it into three smaller problems and solving each by applying specific rules. Adding the results, the complete solution is obtained.

1. Ignorable problems Ex: theorem proving

In which solution steps can be ignored.

2. Recoverable problems Ex: 8 puzzles

In which solution steps can be undone

3. Irrecoverable problems Ex: Chess

In which solution steps can’t be undone good solution absolute or relative

There are two categories of problems. In one, like the water jug and 8 puzzle problems, we are satisfied with the solution, unmindful of the solution path taken, whereas in the other category not just any solution is acceptable.

Problem Classification

Actual problems are examined from the point of view, the task here is examine an input and decide which of a set of known classes.

Ex: Problems such as medical diagnosis, engineering design.

Q11) Explain in detail about Uninformed Search and Informed Search Strategies?

A11) Uninformed Search Strategies have no additional information about states beyond that provided in the problem definition Strategies that know whether one non goal state is ―more promising than another is called informed search or heuristic search strategies.

There are five uninformed search strategies as given below.

1. Breadth-first search
2. Uniform-cost search
3. Depth-first search
4. Depth-limited search
5. Iterative deepening search

Q12) Explain in detail about Depth first search Algorithm?

A12) The h function can be extended to be applicable to (non-empty) paths. The heuristic value of a path is the heuristic value of the node at the end of the path. That is:

h (⟨no....nk⟩) =h(nk)

A simple use of a heuristic function is to order the neighbors that are added to the stack representing the frontier in depth-first search. The neighbors can be added to the frontier so that the best neighbor is selected first. This is known as heuristic depth first search. This search chooses the locally best path, but it explores all paths from the selected path before it selects another path. Although it is often used, it suffers from the problems of depth-fist search.

Another way to use a heuristic function is to always select a path on the frontier with the lowest heuristic value. This is called best-first search. It usually does not work very well; it can

Follow paths that look promising because they are close to the goal, but the costs of the paths may keep increasing.

Q13) Discuss in detail about Constraints Satisfaction Problem?

A13) Constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a set of values for the variables

That satisfies all constraints—that is, a point in the feasible region.

The techniques used in constraint satisfaction depend on the kind of constraints being considered. Often used are constraints on a finite domain, to the point that constraint satisfaction

Problems are typically identified with problems based on constraints on a finite domain. Such problems are usually solved via search, in particular a form of backtracking or local search.

Constraint propagation are other methods used on such problems; most of them are incomplete in general, that is, they

May solve the problem or prove it unsatisfiable, but not always. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via variable elimination or the simplex algorithm.

Complexity

Solving a constraint satisfaction problem on a finite domain is an NP complete problem with respect to the domain size. Research has shown a number of tractable subcases, some limiting the allowed constraint relations, some requiring the scopes of constraints to form a tree, possibly in a reformulated version of the problem. Research has also established relationship of the constraint satisfaction problem with problems in other areas such as finite model theory

Q14) Explain in details about Production System?

A14) Types of Production Systems.

A Knowledge representation formalism consists of collections of condition-action rules (Production Rules or Operators), a database which is modified in accordance with the rules,

And a Production System Interpreter which controls the operation of the rules i.e.

The 'control mechanism' of a Production System, determining the order in which Production Rules are fired. A system that uses this form of knowledge representation is called a production system.

A production system consists of rules and factors. Knowledge is encoded in a declarative from which comprises of a set of rules of the form

Situation ------------ Action

SITUATION that implies ACTION.

Example:

IF the initial state is a goal state, THEN quit.

The major components of an AI production system are

i. A global database

Ii. A set of production rules and

Iii. A control system

The goal database is the central data structure used by an AI production system. The production system. The production rules operate on the global database. Each rule has a precondition that is either satisfied or not by the database. If the precondition is satisfied, the rule can be applied.

Application of the rule changes the database. The control system chooses which applicable rule should be applied and ceases computation when a termination condition on the database is satisfied. If several rules are to fire at the same time, the control system resolves the conflicts.

Four classes of production systems:

1. A monotonic production system

2. A non-monotonic production system

3. A partially commutative production system

4. A commutative production system.

Q15) Give the drawback of DFS

A15) The drawback of DFS is that it can get stuck going down the wrong path. Many problems have very deep or even infinite search tree. So dfs will never be able to recover from an unlucky choice at one of the nodes near the top of the tree. So DFS should be avoided for search trees with large or infinite maximum depths

Q16) Explain history of Artificial Intelligence?

A16) Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development.

Maturation of Artificial Intelligence (1943-1952)

• 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.

The birth of Artificial Intelligence (1952-1956)

• 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.

At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time.

The golden years-Early enthusiasm (1956-1974)

• 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.

The first AI winter (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.
• During AI winters, an interest of publicity on artificial intelligence was decreased.

A boom of AI (1980-1987)

• 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.
• In the Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University.

The second AI winter (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. The expert system such as XCON was very cost effective.

The emergence of intelligent agents (1993-2011)

• 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.

Deep learning, big data and artificial general intelligence (2011-present)

• 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.
• Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointment on call, and lady on other side didn't notice that she was talking with the machine.

Q17) What are the applications of Artificial Intelligence?

A17) Ten applications of artificial intelligence

1. Chatbots

E-commerce websites are using chatbots to improve the customer support service. Chatbots are providing 24/7 customer support to buyers. Visit any recognized E-commerce website. You will be prompted with a chat box asking what do you want or how can I help. You can tell your requirements in the chat box and you will be served with highly filtered results. Chatbots are built using artificial intelligence and are able to communicate with humans. They can also collect your past data and provide you with a very personalized user experience.

2. Image search

Ever come across a situation where you liked any product or item but don’t what it is called or what it is? Artificial intelligence service eases this type of task for you. The concept of that of the image search is implemented in the E-commerce websites with the application of the artificial intelligence. Artificial intelligence has made it possible to understand images. Buyers can make a search on the basis of images. Mobile apps of E-commerce websites can find the product by just pointing the camera towards the product. This eliminates the need for keyword searches.

3. Handling Customer Data

E-commerce platforms have two things in abundance. On is an endless list of products and other is data. E-commerce has to deal with a lot of data every day. This data can be anything like daily sales, the total number of items sold, the number of orders received in an area or as a whole and what not. It has to take care of customer data also. Handling that amount of data is not possible for a human. Artificial intelligence cannot only collect this data in a more structured form but also generate proper insights out of this data.

This helps in understanding the customer behaviour of the whole populations as well as of the individual buyer. Understanding the customer buying pattern can make E-commerce to make changes wherever needed and predicting the next buy of the user also.

4. Recommendation Systems

Have you ever experienced that how E-commerce websites like that of the Amazon is constantly showing that of the products similar you just checked? Well, this is the application of that of the artificial intelligence in E-commerce. AI and machine learning algorithms can predict the behaviour of the buyer from its past searches, likings, frequently bought products. By predicting the behaviour of the user, E-commerce websites are able to recommend the products that user is highly interested in. This improves the user experience as the user no longer has to spend hours searching the product. It also helps the E-commerce websites to improve that of their sales.

5. Inventory management

The inventory management is one of the most important areas in any business. You have to keep yourself updated on how much inventory you are holding and how much more is needed. There are thousands of product categories over the E-commerce websites. Keeping an eye on the inventory of all the products daily is not possible for a human. This is where artificial intelligence comes into that of the picture. Artificial intelligence applications have helped E-commerce in managing the inventory. Moreover, the inventory management system will get better over the time. AI systems build a correlation between the current demand and the future demand.

6. Cybersecurity

Artificial intelligence has also improved that of the cybersecurity of the E-commerce websites. It can prevent or detect any kind of fraudulent activities. E-Commerce has to deal with a lot of transactions on that of the daily basis. Cybercriminals and hackers can hack the user account to gain unauthenticated access. This can lead to that of the exposure of the private data and the online fraud. The reputation of that of the business also gets a very big blow. To prevent this, Artificial intelligence and the machine learning algorithms are developed that can mitigate that of the chances of fraud activities over that of the website.

7. Better Decision Making

E-commerce can make better decisions with that of the application of Artificial intelligence. Data analysts have to handle a lot of data every day. This data is very big for them to handle. Moreover, analysing the data also becomes a difficult task. Artificial intelligence has fastened that of the decision-making process of the E-Commerce. AI algorithms can easily identify the complex patterns in the data by predicting user behaviour and their purchasing pattern.

8. After Sales Service

Selling the product is not enough. Businesses have to aid that of the customer in the complete buying cycle. After sales service is an integral part of after-sales service. Artificial intelligence applications can automate the feedback form, replacements and handling any other ambiguity in the product. By solving the buyer’s issues, the brand value of the website gets improved.

9. CRM

In the past, Customer Relationship Management (CRM) relied on the people to collect a huge amount of data in order to collect the data and serve the clients. But today, artificial intelligence can predict which customers are most likely to make a purchase and how can we better engage with them. Artificial intelligence applications can help in identifying the trends and plan the actions according to the latest trends. With the help of machine learning algorithms, advanced CRM can learn and improve over time.

10. Sales Improvement

Artificial intelligence applications can generate and predict the accurate forecast of the E-commerce business. The study of historical data, data analytics, and latest trends can help in optimizing the resource allocation, build a healthy pipeline and analyse the team performance. The managers can get a better insight into the latest trends in sales. They can analyse the trends and can improve the sales by making strategies well before time.