Artificial Intelligence Interview Questions

by Rupa.R, on Sep 11, 2022 6:39:08 PM

Interview Questions (41)

1. What is Artificial Intelligence?

Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.

2. What is an artificial intelligence Neural Networks?

Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.

3. What are the various areas where AI (Artificial Intelligence) can be used?

Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.

4. Which is not commonly used programming language for AI?

Perl language is not commonly used programming language for AI

5. What is Prolog in AI?

In AI, Prolog is a programming language based on logic.

Artificial Intelligence Training

6. Give an explanation on the difference between strong AI and weak AI?

Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.

7. Mention the difference between statistical AI and Classical AI ?

Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc.  While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.

8. What is alternate, artificial, compound and natural key?

Alternate Key:  Excluding primary keys all candidate keys are known as Alternate Keys.

Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence.  This is known as artificial key.

Compound Key:  When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.

Natural Key:  Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.

9. What does a production rule consist of?

The production rule comprises of a set of rule and a sequence of steps.

10. Which search method takes less memory?

The “depth first search” method takes less memory.

11. Which is the best way to go for Game playing problem?

Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.

12. A* algorithm is based on which search method?

A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.

13. What does a hybrid Bayesian network contain?

A hybrid Bayesian network contains both a discrete and continuous variable

14. What is agent in artificial intelligence?

Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. Agent includes Robots, Programs, and Humans

15.What does Partial order or planning involve?

In partial order planning , rather than searching over possible situation it involves searching over the space of possible plans.  The idea is to construct a plan piece by piece.

16. What are the two different kinds of steps that we can take in constructing a plan?

a)      Add an operator (action)

b)      Add an ordering constraint between operators

17. Which property is considered as not a desirable property of a logical rule-based system?

Attachment” is considered as not a desirable property of a logical rule based system. "Attachment” is considered as not a desirable property of a logical rule based system.

18. What is Neural Network in Artificial Intelligence?

In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, process the data and gives the output based on the algorithm and empirical data.

19. When an algorithm is considered completed?

An algorithm is said completed when it terminates with a solution when one exists.

20. What is a heuristic function?

A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.

21. What is the function of the third component of the planning system?

In a planning system, the function of the third component is to detect when a solution to problem has been found.

22. What is “Generality” in AI ?

Generality is the measure of ease with which the method can be adapted to different domains of application.

23. What is a top-down parser?

A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.

24. the difference between breadth first search and best first search in artificial intelligence?

These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.

25. What are frames and scripts in “Artificial Intelligence”?

Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered.  Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.

26. What is FOPL stands for and explain its role in Artificial Intelligence?

FOPL stands for First Order Predicate Logic, Predicate Logic provides

a)      A language to express assertions about certain “World”

b)      An inference system to deductive apparatus whereby we may draw conclusions from such assertion

c)       A semantic based on set theory

27. What does the language of FOPL consists of?

a)      A set of constant symbols

b)      A set of variables

c)       A set of predicate symbols

d)      A set of function symbols

e)      The logical connective

f)       The Universal Quantifier and Existential Qualifier

g)      A special binary relation of equality[/vc_column_text][subtitle subtitle_content=” For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?”][vc_column_text]In online search, it will first take action and then observes the environment.[/vc_column_text][subtitle subtitle_content=”Which search algorithm will use a limited amount of memory in online search?”][vc_column_text]RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.[/vc_column_text][subtitle subtitle_content=” In ‘Artificial Intelligence’ where you can use the Bayes rule?”][vc_column_text]In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.[/vc_column_text][subtitle subtitle_content=”For building a Bayes model how many terms are required?”][vc_column_text]For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.[/vc_column_text][subtitle subtitle_content=” While creating Bayesian Network what is the consequence between a node and its predecessors?”][vc_column_text]While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.[/vc_column_text][subtitle subtitle_content=” To answer any query how the Bayesian network can be used?”][vc_column_text]If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.[/vc_column_text][subtitle subtitle_content=” What combines inductive methods with the power of first order representations?”][vc_column_text]Inductive logic programming combines inductive methods with the power of first order representations.[/vc_column_text][subtitle subtitle_content=” In Inductive Logic Programming what needed to be satisfied?”][vc_column_text]The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied.[/vc_column_text][subtitle subtitle_content=” In top-down inductive learning methods how many literals are available? What are they?”][vc_column_text]There are three literals available in top-down inductive learning methods they are

a)      Predicates

b)      Equality and Inequality

c)       Arithmetic Literals[/vc_column_text][subtitle subtitle_content=” Which algorithm inverts a complete resolution strategy?”][vc_column_text]‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.[/vc_column_text][subtitle subtitle_content=” In speech recognition what kind of signal is used?”][vc_column_text]In speech recognition, Acoustic signal is used to identify a sequence of words.[/vc_column_text][subtitle subtitle_content=” In speech recognition which model gives the probability of each word following each word?”][vc_column_text]Biagram model gives the probability of each word following each other word in speech recognition.[/vc_column_text][subtitle subtitle_content=” Which algorithm is used for solving temporal probabilistic reasoning?”][vc_column_text]To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.[/vc_column_text][subtitle subtitle_content=” What is Hidden Markov Model (HMMs) is used?”][vc_column_text]Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour.  They are used in almost all current speech recognition systems.[/vc_column_text][subtitle subtitle_content=” In Hidden Markov Model, how does the state of the process is described?”][vc_column_text]The state of the process in HMM’s model is described by a ‘Single Discrete Random Variable’.[/vc_column_text][subtitle subtitle_content=” In HMM’s, what are the possible values of the variable?”][vc_column_text]‘Possible States of the World’ is the possible values of the variable in HMM’s.[/vc_column_text][subtitle subtitle_content=” In HMM, where does the additional variable is added?”][vc_column_text]While staying within the HMM network, the additional state variables can be added to a temporal model.[/vc_column_text][subtitle subtitle_content=” In Artificial Intelligence, what do semantic analyses used for?”][vc_column_text]In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.[/vc_column_text][subtitle subtitle_content=” What is meant by compositional semantics? In Artificial Intelligence, what do semantic analyses used for?”][vc_column_text]The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.[/vc_column_text][subtitle subtitle_content=” How logical inference can be solved in Propositional Logic?”][vc_column_text]In Propositional Logic, Logical Inference algorithm can be solved by using

a)      Logical Equivalence

b)      Validity

c)       Satisfying ability[/vc_column_text][subtitle subtitle_content=” Which process makes different logical expression looks identical?”][vc_column_text]’Unification’ process makes different logical expressions identical.  Lifted inferences require finding substitute which can make a different expression looks identical.  This process is called unification.[/vc_column_text][subtitle subtitle_content=” Which algorithm in ‘Unification and Lifting’ takes two sentences and returns a unifier?”][vc_column_text]In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.[/vc_column_text][subtitle subtitle_content=” Which is the most straight forward approach for planning algorithm?”][vc_column_text]State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.[/vc_column_text][subtitle subtitle_content=”What Is The Difference Between Strong Ai And Weak Ai?”][vc_column_text]Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans. Weak AI simply states that some “thinking-like” features can be added to computers to make them more useful tools… and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition software). What does ‘think’ and ‘thinking-like’ mean? That’s a matter of much debate.[/vc_column_text][subtitle subtitle_content=”I Am A Programmer Interested In Ai. I Am Writing A Game That Needs Ai. Where Do I Start?”][vc_column_text]It depends what the game does. If it’s a two-player board game,look into the “Mini-max” search algorithm for games. In most commercial games, the AI is is a combination of high-level scripts and low-level efficiently-coded, real-time, rule-based systems. Often, commercial games tend to use finite state machines for computer players. Recently, discrete Markov models have been used to simulate unpredictible human players (the buzzword compliant name being “fuzzy” finite state machines).

A recent popular game, “Black and White”, used machine learning techniques for the non-human controlled characters. Basic reinforcement learning, perceptrons and decision trees were all parts of the learning system.[/vc_column_text][subtitle subtitle_content=”What Is An Agent?”][vc_column_text]A very misused term. Today, an agent seems to mean a stand-alone piece of AI-ish software that scours across the internet doing something “intelligent.” Russell and Norvig define it as “anything that can can be viewed a perceiving its environment through sensors and acting upon that environment through effectors.” Several papers I’ve read treat it as ‘any program that operates on behalf of a human,’ similar to its use in the phrase ‘travel agent’. Marvin Minsky has yet another definition in the book “Society of Mind.” Minsky’s hypothesis is that a large number of seemingly-mindless agents can work together in a society to create an intelligent society of mind. Minsky theorizes that not only will this be the basis of computer intelligence, but it is also an explanation of how human intelligence works. Andrew Moore at Carnegie Mellon University once remarked that “The only proper use of the word ‘agent’ is when preceded by the words ‘travel’, ‘secret’, or ‘double’.”[/vc_column_text][subtitle subtitle_content=”What Has Ai Accomplished?”][vc_column_text]Quite a bit, actually. In ‘Computing machinery and intelligence.’, Alan Turing, one of the founders of computer science, made the claim that by the year 2000, computers would be able to pass the Turing test at a reasonably sophisticated level, in particular, that the average interrogator would not be able to identify the computer correctly more than 70 per cent of the time after a five minute conversation. AI hasn’t quite lived upto Turing’s claims, but quite a bit of progress has been made, including:

  • Deployed speech dialog systems by firms like IBM, Dragon and Lernout & Hauspie
  • Financial software, which is used by banks to scan credit card transactions for unusual patterns that might signal fraud. One piece of software is estimated to save banks $500 million annually.
  • Applications of expert systems/case-based reasoning: a computerized Leukemia diagnosis system did a better job checking for blood disorders than human experts.
  • Machine translation for Environment Canada: software developed in the 1970s translated natural language weather forecasts between English and French. Purportedly stil in use.

[/vc_column_text][subtitle subtitle_content=”What Are The Branches Of Ai?”][vc_column_text]There are many, some are ‘problems’ and some are ‘techniques’.

Automatic Programming: The task of describing what a program should do and having the AI system ‘write’ the program.

Bayesian Networks: A technique of structuring and conferencing with probabilistic information. (Part of the “machine learning” problem).

Constraint Satisfaction: solving NP-complete problems, using a variety of techniques.

Knowledge Engineering/Representation: turning what we know about particular domain into a form in which a computer can understand it.

Machine Learning: Programs that learn from experience or data.

Natural Language Processing(NLP): Processing and (perhaps) understanding human (“natural”) language. Also known as computational linguistics.

Neural Networks(NN): The study of programs that function in a manner similar to how animal brains do.

Planning: given a set of actions, a goal state, and a present state, decide which actions must be taken so that the present state is turned into the goal state

Robotics: The intersection of AI and robotics, this field tries to get (usually mobile) robots to act intelligently.

Speech Recognition: Conversion of speech into text.[/vc_column_text][subtitle subtitle_content=”What Are Good Programming Languages For Ai?”][vc_column_text]This topic can be somewhat sensitive, so I’ll probably tread on a few toes, please forgive me. There is no authoritative answer for this question, as it really depends on what languages you like programming in. AI programs have been written in just about every language ever created. The most common seem to be Lisp, Prolog, C/C++, recently Java, and even more recently, Python.

LISP: For many years, AI was done as research in universities and laboratories, thus fast prototyping was favored over fast execution. This is one reason why AI has favored high-level languages such as Lisp. This tradition means that current AI Lisp programmers can draw on many resources from the community. Features of the language that are good for AI programming include: garbage collection, dynamic typing, functions as data, uniform syntax, interactive environment, and extensibility. Read Paul Graham’s essay, “Beating the Averages” for a discussion of some serious advantages:

PROLOG: This language wins ‘cool idea’ competition. It wasn’t until the 70s that people began to realize that a set of logical statements plus a general theorem prover could make up a program. Prolog combines the high-level and traditional advantages of Lisp with a built-in unifier, which is particularly useful in AI. Prolog seems to be good for problems in which logic is intimately involved, or whose solutions have a succinct logical characterization. Its major drawback (IMHO)[/vc_column_text][subtitle subtitle_content=”What Is The Difference Between Classical Ai And Statistical Ai?”][vc_column_text]Statistical AI, arising from machine learning, tends to be more concerned with “inductive” thought: given a set of patterns, induce the trend. Classical AI, on the other hand, is more concerned with “deductive” thought: given a set of constraints, deduce a conclusion. Another difference, as mentioned in the previous question, is that C++ tends to be a favorite language for statistical AI while LISP dominates in classical AI.

A system can’t be truly intelligent without displaying properties of both inductive and deductive thought. This lends many to believe that in the end, there will be some kind of synthesis of statistical and classical AI.[/vc_column_text][subtitle subtitle_content=”What Are Best Graduate Schools For Ai?”][vc_column_text]

  • The short answer is: MIT, CMU, and Stanford are historically the powerhouses of AI and still are the top 3 today.

There are however, hundreds of schools all over the world with at least one or two active researchers doing interesting work in AI. What is most important in graduate school is finding an advisor who is doing something YOU are interested in. Read about what’s going on in the field and then identify the the people in the field that are doing that research you find most interesting. If a professor and his students are publishing frequently, then that should be a place to consider.

[/vc_column_text][subtitle subtitle_content=”What Are Partial, Alternate, Artificial, Compound And Natural Key?”][vc_column_text]It is a set of attributes that can uniquely identify weak entities and that are related to same owner entity. It is sometime called as Discriminator.

Alternate Key:

All Candidate Keys excluding the Primary Key are known as Alternate Keys.

Artificial Key:

If no obvious key, either stand alone or compound is available, then the last resort is to simply create a key, by assigning a unique number to each record or occurrence. Then this is known as developing an artificial key.

Compound Key:

If no single data element uniquely identifies occurrences within a construct, then combining multiple elements to create a unique identifier for the construct is known as creating a compound key.

Natural Key:

When one of the data elements stored within a construct is utilized as the primary key, then it is called the natural key.[/vc_column_text][subtitle subtitle_content=”Where Can I Find Conference Information?”][vc_column_text]Georg Thimm maintains a webpage that lets you search for upcoming or past conferences in a variety of AI disciplines.[/vc_column_text][subtitle subtitle_content=”What Is A Chatterbot?”][vc_column_text]chatterbot is a game[/vc_column_text][subtitle subtitle_content=”Where To Find Specific Information On Search Bots?”][vc_column_text]Check out ALICE and ELIZA bots are very good …and we can get more info on how to build in respective websites[/vc_column_text][subtitle subtitle_content=”Suppose I Have Gmail Account, I Want To Delete All The Mails In My Inbox Having The Same Name(for Eg., Orkut). I Have Thousands Of Mails Like That. So, How Can I Delete All The Mails Having Single Name. Is There Any Option Provided In Gmail?”][vc_column_text]Yes, its very easy …just do one thing the top of the Inbox page there is a search box just search whatever you want to delete then click .. after few sec all the mail with concerned name get displayed .. just select them and delete them .. as you delete your spam or other mails..[/vc_column_text][subtitle subtitle_content=”Do Bots And Intelligent Agents Have Personalities And Emotions?”][vc_column_text]IA is used to develop bots… and moreover how u program it is very important.It uses NL and ML also.If a person uses proper ontology then it can answer out.[/vc_column_text][subtitle subtitle_content=”Do Bots And Intelligent Agents Have Personalities And Emotions?”][vc_column_text]IA is used to develop bots… and moreover how u program it is very important.It uses NL and ML also.If a person uses proper ontology then it can answer out.[/vc_column_text][subtitle subtitle_content=”2 Batsman Are On 94 Notout,need To Win 7 Runs Off 2 Balls,both Hit A Century? How It Is Possible?”][vc_column_text]First batsman hit 4 on no ball and then took a single on next ball. Thus completed his century. Second batsman hit 6 on last ball and completed his century too.[/vc_column_text][subtitle subtitle_content=”Suppose 2 Batsmen Each On 94. 7 Runs To Win In 3 Balls. Both Make Unbeaten 100. How?”][vc_column_text]Case 1: A batsman can be given out 1st batsman hits a six….gets caught on d nxt ball…crease is changed….next batsman hits a six again…

Case 2: No batsman is out

1st batsman hits d ball n hits d keepers helmet kept behind…he also takes a single…6 runs are added to his total making it 100…on d next ball, 2nd batsman hits a six,making his score 100….as simple as dat….

28. What Are The Undesirable Properties Of Knowledge?

Following are the undesirable properties of knowledge:

  1. Voluminous: Knowledge may become voluminous
  2. Difficult to characterize: It is difficult to characterize the knowledge accurately
  3. Variability: Knowledge has a property that it may change over the time
  4. Variation in usage: Knowledge may be used in some other way than the way in which data is organized

29. How Should Knowledge Be Represented To Be Used For An Ai Technique?

Following are the requirements for knowledge to be used for an AI technique:

  1. When two individual situations are represented, knowledge should provide generalization such that only common properties of both situations are represented rather than representing both situations individually
  2. Knowledge should be represented such that it should be understood by the people who have provided it
  3. Knowledge should be represented in a way that it can be easily modified
  4. Knowledge should be represented such that it should still be applicable to one or more situations even if it is inaccurate or incomplete

29. How Many Types Of Entities Are There In Knowledge Representation?

There are two types of entities in knowledge representation:

  1. Facts: These are truths that need to be represented
  2. Symbols: It is a form of representation of facts and it is manipulated by the programs to derive new facts

30. What Are The Properties Of A Good Knowledge Representation System?

A good knowledge representation system must have following properties:

Representation Adequacy: It must be able to represent all knowledge required in a particular domain
Inferential Adequacy: It must be able to derive knowledge representation structures such as symbols when new knowledge is inferred from old knowledge
Inferential Efficiency: It must be able to incorporate additional information into knowledge structures which may help inference process to move in promising direction
Acquisition Efficiency: It must be able to incorporate new information.

31. What Are The Techniques To Represent Knowledge?

There are four techniques to represent knowledge:

  1. Relational knowledge: In this representation, knowledge is represented as a set of relations, similar to relations that are used in the database
  2. Inheritable knowledge: In this representation, knowledge is represented using objects, their attributes and the values of the attributes
  3. Inferential knowledge: In this representation, knowledge is represented in the form of first-order predicate logic
  4. Procedural knowledge: In this representation, knowledge is represented as a set of rules and a rule describes an action to be performed when a condition is met

32. What Is Relational Knowledge?

It is a knowledge representation scheme in which facts are represented as a set of relations. For example knowledge about players can be represented using a relation called “player” having three fields: player name, height and weight. This form of knowledge representation provides weak inferential capabilities when used as standalone but are useful as an input for sophisticated inferential procedures.

33. What Is Inheritable Knowledge?

It is a knowledge representation scheme in which knowledge is represented using objects, their attributes and corresponding value of the attributes. The relation between different objects is defined using a “isa” property. For example if two entities “Adult male” and “Person” are represented as objects then the relation between the two is that Adult male “isa” person.

34. In Artificial Intelligence, What Do Semantic Analysis Used For?

In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.

35. What Is Meant By Compositional Semantics?

The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.

36. What are the components of a Physical Symbol System?

A physical symbol system is composed of following components:

a) A collection of symbol structures

b) A collection of processes which are responsible for creating, modifying, reproducing and destroying symbol structures

A symbol structure is known as expression and is composed of a set of symbols which are physically related to each other.

37. What are the steps followed in a system which attempts to solve a given problem?

A system must do the following in order to solve a particular problem:

a) It should define the problem accurately by specifying the initial situation and final situation (solution of the problem)

b) It should analyze the problem thoroughly because if an important feature is skipped, the technique employed to solve the problem may become ineffective

38. What is a state space of a problem?

It is a set of states where each state represents a legal position within the problem. A state space must contain an initial state and a set of final states representing set of acceptable states. Using a set of rules, one of final states can be reached from the initial state. For example, in chess there exists a set of states where each state represents a legal board position and rules are used to reach one of the final states from the start state.

39. What is the benefit of representing a problem using a state space?

There are two benefits of representing a problem using a state space:

a) As most AI problems require transition from a given situation (initial state) to a desired situation (final state), a state space defines these problems formally

b) It helps in solving the problem using a set of rules and search. Search is considered as an important technique because it helps in solving difficult problems in the absence of direct technique

 40. What do you mean by the term “operationalization ?

It is the process of converting an informal description of a problem into a formal description. This process is easy for problems which are not natural and are highly structured such as water-jug and chess problems. But this process is difficult for natural occurring problems such as understanding an English sentence where this process may be automated using programs.

41. What are the steps followed in order to provide a formal description of a problem?

In order to provide a formal description of a problem the full steps should be followed:

a) Define a state space. A state space may be defined without explicitly specifying all of its states

b) Define one or more states that may represent possible situations from which the process of problem-solving may start. These states are known as initial states

c) Define one or more states that may represent situations which are considered as acceptable solutions to the problem. These states are known as final or goal states

d) Define various actions to be performed. These are known as set of rules

42. What are the components of a production system?

A production system is composed of following:

a) A set of rules: A rule consists of two parts: a left side and a right side. Left side of the rule denotes condition of the rule and the right side denotes the action to be taken when the condition is met

b) One or more knowledge database: A knowledge database contains all the information required for problem solving process. It is important to note that all parts of knowledge database may not be relevant to the solution of the problem

c) A control strategy: A control strategy specifies the sequence in which the rules will be compared against the knowledge database in order to be applied. A control strategy also resolves the conflict when more than one rule matches

d) A rule applier: It is a system which applies the rule by using a control strateg

43. What are the different areas where AI (Artificial Intelligence) can be utilized?
Here are some.

game playing

speech recognition

understanding natural language

computer vision

expert systems

heuristic classification etc

44. Which Language is Best for Artificial Intelligence?

Python. Python is one of the most widely used programming languages in the AI field because of its simplicity. …

Java. Java is also the best choice.




Final thoughts.

45. What is a LISP Programming?

LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. Developed in 1959 by John McCarthy, it is a commonly used language for artificial intelligence (AI)programming. It is one of the oldest programming languages still in relatively wide use.

46. What kind of Language is ProLo?

Unlike traditional programming languages that are based on performing sequences of commands, Prolog is based on defining and then solving logical formulas. Prologis sometimes called a declarative language or a rule-based language because its programs consist of a list of facts and rules.

47. Can Java be used for AI?

Pretty much any language can be used to code pretty much anything – given the effort and will. But Python has more functional programming constructs which may be more useful when you are coding AI. … Never use PHP for AI. Java or C/C++ is the best, but Python for fast development.

48. Which Programming Language is used in AI?

Prolog and Lisp are ‘old’ languages used in A.I. But nowadays almost all ‘A.I’ is written in standard programming languages like C, C++, Python, etc.

49. What is meant by Learning in AI?

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

50. What is AI in computer?

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans.

51. What is AI in computer science?

Artificial Intelligence (AI) is the science of mimicking human intelligence using computers. The Computer Science and Artificial Intelligence degree course is a computing degree that allows students to specialize in AI through their project work and a number of specialist AI modules.

52. What is AIML?

AIML (Artificial Intelligence Markup Language) is an XML-compliant language that’s easy to learn and makes it possible for you to begin customizing an Alicebot or creating one from scratch within minutes. … <aiml>: the tag that begins and ends an AIML document.

53. What is the Best Programming Language for Robotics?

There has been a huge resurgence of Python in recent years, especially in robotics. One of the reasons for this is probably that Python (and C++) are the two main programming languages found in ROS. Like Java, it is an interpretive language. Unlike Java, the prime focus of the language is ease of use.

54. What is inference in AI?

Inferences are steps in reasoning, moving from premises to conclusions. … Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference.

55. Is AI Science Or is it Engineering?

Artificial Intelligence (AI) combines science and engineering in order to build machines capable of intelligent behavior. … Intelligent robotics is a related discipline in which the machines can manipulate objects in the physical world, (see ROBOT).

56. How does an Artificial Intelligence work?

There is no single way in which artificial intelligence works. One definition of AI is when a computer can solve a problem that normally requires a level of intelligence. Over the past 60 or so years, many different approaches to solving a wide variety of problems have been discovered by AI researchers.

57. When did the idea of AI start?

After 2nd World War, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines.

58. When was the first AI Program written?

The first working AI programs were written in 1951 to run on the Ferranti Mark 1 machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz. 1952–1962.

59. What is meant by Learning Algorithm?

A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task.

60. What is the meaning of Supervised Learning?

.In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

61. What is meant by Intelligent Systems?

An intelligent system is a machine with an embedded, Internet-connected computer that has the capacity to gather and analyze data and communicate with other systems. … In IT, a system is defined as a collection of connected elements or components that are organized for a common purpose.

62. What is Classification problem?

The classification problem is the problem that for many real-world objects and systems; coming up with an iron-clad classification system (to determine if an object is a member of a set or not, or which of several sets) is a difficult problem.

63. What is the main difference between Classification and Clustering?

Clustering and Classification are the absolute basics of machine learning. Let’s look at the difference between them. … Because of this difference in learning, Clustering is called an unsupervised learning method and Classification is called a supervised learning method.

64. What is the difference between Supervised Learning and Unsupervised Learning?

Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal)

65. What is an Intelligent Agent?

In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is “rational”, as defined in economics).

66. What is an Expert system in computing?

In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than through conventional procedural code.

67. What is the difference between Classification and Regression?

Regression is used to predict continuous values. Classification is used to predict which class a data point is a part of (discrete value).

68. What is a rational agent in AI?

In economics, game theory, decision theory, and artificial intelligence, a rational agent is an agent that has clear preferences, models uncertainty via expected values of variables or functions of variables, and always chooses to perform the action with the optimal expected outcome for itself from among all feasible.

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