Ai+Viva+Questions

ARTIFICIAL INTELLIGENCE VIVA QUESTIONS 1. Definitions • Agent • Constraint Satisfaction • Expert system • Game playing • Genetic Algorithm and genetic programming • Knowledge acquisition • Knowledge representation • Logic programming prolog • Machine learning • Natural language processing • Robotics • Simulated annealing • Uncertainty • Vision • Search and game playing • Logic • Planning • Probabilistic reasoning • Nonmonotonic Reasoning and truth maintenance systems (TMS) 2. A*: A search algorithm to find the shortest path through a search space to a goal state using a heuristic. See ‘search’, ‘problem space’, admissibility’ and ‘heuristic’. 3. Admissibility: An admissible search algorithm is one that is guaranteed to find the optimal path fron the start node to a goal node, if one exists. In A* search, an admissible heuristic is one that never overestimated the distance remaining from the current node to the goal. 4. Agent “Anything that can be viewed a perceiving its environment through sensors and acting upon that environment through effectors.” [Russel, Norvig 1995] 5. AI: A three-toed sloth of genus Bradypus. This forest-dwelling animal eats the leaves of the trumpet- tree and sounds a high-pitched squeal when disturbed.(Based on the Random House Dictionary definition.) 6. Alpha-Beta Prunning: A method of limiting search in the MiniMax algorithm. The coolest thing you learn in an undergraduate course. 7. Backward Chaining:

In a logic system, reasoning from a query to the data. See forward chaining 8. Belief Network (also Bayesian Network): It is a mechanism for representing probabilistic knowledge. Inference algorithms in belief networks use the structure of the network to generate inference efficiently (compared to joint probability distributions over all the variables). 9. Breadth-first Search: It is an uninformed search algorithm where the shallowest node in the search tree is expanded first. 10. Case- Based Reasoning: Technique whereby “cases” similar to the current problem are retrieved and their “solutions” modified to work on the current problem. 11. Closed World Assumption: The assumption that if a system has no knowledge about a query, it is false. 12. Open World Assumption: If there is no idea about a query it is assumed to be unknown. 13. Computational Linguistics: It is the branch of AI that deals with understanding human language. It is also called natural language processing. 14. Data Mining: Also known as Knowledge Discovery in Databases(KDD) was been defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data” in Frwaley and Piatetsky-Shapiro’s overview. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to humans. 15. Depth-first Search: It is an uninformed search algorithm where the deepest non-terminal node is expanded first. 16. Evaluation Function: A function applied to a game state to generate a guess as to who is winning. Used by Minimax when the game tree is too large to be searched exhaustively. 17. Forward Chaining: In logic system, reasoning from facts to conclusions is called forward chaining. See Backward Chaining. 18. Fuzzy Logic: In Fuzzy logic, truth values are real values in the closed interval [0...1]. The definitions of the Boolean operators are extended to fit this continuous domain. By avoiding discrete truth values, Fuzzy logic avoids some of the problems inherent in either-or judgments and

yields natural interpretations of utterances like “very hot”. Fuzzy Logic has applications in control theory. 19. Heuristic: The dictionary defines it as a method that serves as an aid to problem solving. It si sometimes defined as any ‘rule of thumb’. Technically, a heuristic is a function that takes a state as input and output a value for that state often as a guess of how far away that state is from the goal state. See also: Admissibility, Search 20. Information Extraction: Getting computer-understandable information from human-readable (ie natural language) documents is called Information Extraction. 21. Iterative Deepening: It is an uninformed search that combines good properties of depth-first and breadth-first search. The ideas of iterative deepening applied to A*. 22. Language Acquisition: A relatively new sub-branch of AI; tradionally computational linguists tried to make computers understand human language by giving the computer grammar rule. Language acquisitions are a technique for the computer to generate the grammar rules itself. 23. Machine Learning: A field of AI concerned with programs that learn. It includes reinforcement Learning and Neural Networks among many other fields. 24. MiniMax: It is an algorithm for game playing in games with perfect information. 25. Modus Ponens: An inference rules that says: if you know x and you know that ‘If x is true then y is true then you can conclude y’. 26. Nonlinear Planning: A planning paradigm which does not enforce a total (linear) ordering on the components of a plan is called nonlinear planning. 27. Natural language (NL): This is used to evolve languages that human use to communicate with one another. 28. Natural language Queries: Using human language to get information from a database is called Natural language queries. 29. Partial order Planner. A planner that only orders steps that need to be ordered, and leaves unordered any steps that can be done in any order.

30. Planning: A field of AI concerned with systems that construct sequences of actions to achieve goal in real-world-like environments. 32. Problem Space (also state space): It is the formulation of an AI problem into states and operators. There is usually a start state and a goal state. 33. Search: The finding of a path from a start to a goal state. See ‘Admissibility’, ‘Problem space’ and ‘Heuristic’. 34. Strong AI: Claim that computer can be made to actually think, just like human beings do. More precisely, the claim that there exists a class of computer program, such that any implementation of such a program is really thinking. 35. Unification: The process of finding a substitution (an assignment of constants and variables to variables) that makes two logical statements look the same. Validation: The process of confirming that one’s model uses measurable inputs and produces output that can be used to make decisions about the real world. Verification: The process of confirming that an implemented model works as intended. Weak AI: Claim that computer is important tools in the modeling and simulation of human activity. 36. Turing test: A. Alan Turing’s 1950 article Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligent. He argues that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it’s human and the machine would try to fool the observer. The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human. 37. Problem solving: Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situations in which the problem is embedded. In contrast, a general-

purpose technique used in AI is means-end analysis- a step-by-by, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means in the case of a simple robot this might consists of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT and MOVERIGHT – until the goal is reached. 38. Perception: In perception the environment is scanned by means of various sense organs, real or artificial, and processes internal to the perceiver analyze the scene into objects and their features and relationships. Analysis is complicated by the fact that one and the same object may present many different appearance on different occasions, depending on the angle from which it is viewed, whether or not parts of it are projecting shadows, and so forth. 39. Language-understanding: A language is a system of signs having meaning by convention. Traffic signs, for example, form a mini-language, it being a matter of convention that, for example, the hazard-ahead sign means hazard ahead. This meaning-by-convention that is distinctive of language is very different from what is called natural meaning, exemplified in statements like ‘Those clouds mean rain’ and the fall in pressure means the valve in malfunctioning’. 40. Learning: Learning is distinguished into a number of different forms. The simplest is learning b trialand-error. For example, a simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate. The program remembers the successful move and next time the computer is given the same problem it is able to produce the answer immediately. The simple memorizing of individual items- solutions to problems, words of vocabulary, etc. is known as rote learning. Role learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Learning that involves generalization leaves the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of e.g. “jump” until presented at least once with “jumped”, whereas a program that is able to generalize from examples can learn the “added” rule, and so form the past tense of “jump” in the absence of any previous to generalize complex rules from data. 41. Reasoning: To reason is to draw inferences appropriate to the situation in hand. Inferences are classified as either deductive or inductive. An example of the former is “Fred is either in the museum or the cafZ; he isn’t in the cafZ; so he’s the museum’, and of the latter “Previous accidents just like this one have been caused by instrument failure; so probably this one was caused by instrument failure”. The difference between the two is that in the deductive case, the truth of the premises guarantees the truth of the conclusion, whereas in the inductive cases, the truth of the premises lends support to the conclusion that the accident was caused by instrument failure, but nevertheless further investigation might reveal that, despite the truth of the premises, the conclusion is in fact false. There has been considerable success in programming computers to draw inferences, especially deductive

inferences. However, a program cannot be said to reason simply in virtue of being able to draw inferences. Reasoning involves drawing inferences that are relevant to the task or situation in hand. One of the hardest problems confronting AI is that of giving computers the ability to distinguish the relevant from the irrelevant. 42. Heuristic Search (Heuristics- Heuristic Search- Best-First Search – hill climbing – Minimizing Cost – A* Search [A-Start Search]- Other Search Techniques) 43. Generate and test: It is a search technique which proposes possible solutions and then tests them for their feasibility. 44. Heuristic Search: It is an informed method of searching a state space with the purpose of reducing its size and finding one or more suitable goal states. 45. means-end analysis: It is an AI Technique which tries to reduce the “difference” between a current state and a goal state.

ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS VIVA QUESTIONS 1. What is artificial intelligence? • It is the science and engineering of making intelligent machines, especially intelligent computer programs. • It is related to the similar task of using computers to understand human intelligence. • AI doesn’t have to confine itself to methods that are biologically observable. 2. What is intelligence? • Intelligence is the computational part of the ability to achieve goals in the world. • Varying kinds and degrees of intelligence occur in people, many animals and some machines. 3. Isn’t AI about simulating human intelligence? • Sometimes but not always or even usually. • On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods • AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do. 4. What about IQ? • IQ is based on the rates at which intelligence develops. 5. When did AI research start? • The English mathematician Alan Turing 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. • By the late 1950s, there were many researches on AI, and most of them were basing their work on programming computers. 6. Does AI aim to put the human mind into the computer? • Some researchers say they have that objective, but may be they are using the phrase metaphorically. • The human mind has a lot of peculiarities, and I’m not sure anyone is serious about imitating all of them. 7. What is the Turing Test? • The Turing test is a one-sided test. • A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human. 8. Does AI aims at human-level intelligence? • Yes. The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans.

9. What are the applications of AI? • Game playing. • Heuristic classification. • Expert systems • Computer vision • Understanding natural language • Speech recognition 10. What is game playing? Game playing • • •

You can buy machines that can play master level chess for a few hundred dollars. They play well against people mainly through brute force computation looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

11. What is speech recognition? • In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus united airlines have replaced its keyboard tree for flight information by system using speech recognition of flight numbers and city names. • It is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient. 12. Explain about understanding natural language applications in AI? • Just getting a sequence of words into a computer is not enough. Parsing sentences is nto enough either. • The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. 13. What do you mean by an Expert system? • An expert system, also know as a knowledge based system • It is a computer program that contains some of the subject-specific knowledge, and contains the knowledge and analytical skills of one or more human experts. • The class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. 14. What is an agent? • The term “agent” describes a software subtraction, an idea, or a concept, similar to OOP terms such as methods, functions and objects. • The concept of an agent provides a convenient and powerful way to describe a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on behalf of its user. • But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior

15. What do you mean by a Heuristic? • It can be either be any algorithm that gives up finding the optimal solution for an improvement in run time • It can be a function that estimates the cost of the cheapest path from one node to another. 16. What is uncertainty? • The lack of certainty, A state of having limited knowledge where it is impossible to exactly describe existing state or future outcome, more than one possible outcome. 17. What do you mean by Neural Networks? Neural network had been used to refer to a network or circuitry of biological neurons. The modern usage of the term often refers to artificial neurons or nodes. Thus the term ‘Neural Network’ has two • Biological Neural Networks. • Artificial Neural Networks. 18. What is Artificial Neural Networks? • Artificial Neural Networks are made up of interconnecting artificial neurons. • Artificial Neural Networks may either be used to gain an understanding of biological neural networks. 19. What is Cognitive Modeling? • A cognitive model is a model of cognitive processes. • It is the use of computers to model cognitive behaviour and sometimes the study of cognitive behavior to improve the usage of computers. • Cognitive models are used to study e.g. intelligent or social behaviour, and emergent properties of connectionist architecture. 20. What is DFS? Depth-first search (DFS) is an algorithm for traversing or searching a tree, tree structure or graph. 21. What is A* algorithm? • A* (pronounced “A star” is a graph/ tree search algorithm that finds a path from a given initial node to a given goal node (or one passing a given goal test). • It employs a “heuristic estimate” h(x) that ranks each node x by an estimate of the best route that goes through that node. • The A* algorithm is therefore an example of best-first search. 23. What do you mean by BFS? • Breadth-first search (BFS) is a graph search algorithm that begins at the root node and explores all the neighboring nodes. • Then for each of those nearest nodes, it explores their unexplored neighbour nodes, and so on, until it finds the goal