Q1. In 1956 —– regarded as the father of AI, organized a conference to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming.
Answer: John McCarthy
Q2. In 1957, the first version of a new program —- was tested.
Answer: The General Problem Solver (GPS)
Q3. Generally in AI empiric rules exist, called —-, to shorten the search as the number of possible solutions for complex problems can be very high.
Answer: heuristics
Q4. —- is usually defined as the science of making computers do things that require intelligence when done by humans.
Answer: Artificial Intelligence (AI)
Q5. —- in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and language-understanding.
Answer: Research
Q6. A generic term commonly used to indicate the inclusion in software of some type of automated application of rules, the results of which give the appearance of b —-b on the part of the computer.
Answer: intelligence
Q7. When learning the usual range of tasks that we might look forward to an b intelligent entityb to perform, we require to take into account both b commonplaceb tasks in addition to —- tasks.
Answer: expert
Q8. —- tasks are performed by matter of routinely by people and some other animals.
Answer: Common-place
Q9. —- are the ways that can be used to generate and produce computer programs generally observed as forms of artificial intelligence.
Answer: Artificial intelligence techniques
Q10. —- are computer programs intended about the cognitive processes utilized by the human brain.
Answer: Neural networks
Q11. —- are artificial intelligence techniques constructed around logic and b if/thenb statements.
Answer: Expert systems
Q12. —- systems are those that most completely look for to emulate human thought and cognitive potentials through a broad range of functions.
Answer: Strong AI
Q13. Artificial intelligence techniques that build up —- systems are narrower in focus, and look for to imitate only a single function or facet of human intelligence.
Answer: weak AI
Q14. The long-term criteria include —- Test performed for Weak AI.
Answer: Turing
Q15. In short-term criteria, performance is equal or better than —- .
Answer: humans
Q16. The thought of —- Search is that a problem can be solved by probing the steps which might be taken for the solution.
Answer: State Space
Q17. —- are the States whose links have all been discovered.
Answer: Closed State
Q18. A —- is defined by the measurement of the values of all attributes of interest in the world.
Answer: state
Q19. A system that utilizes this form of knowledge representation is known as a —- .
Answer: production system
Q20. Partially commutative, monotonic production systems are functional for solving —- problems.
Answer: ignorable
Q21. In graph theory, —- is a graph search algorithm that starts at the root node and discovers all the adjacent nodes.
Answer: breadth-first search (BFS)
Q22. From the position of the algorithm, all child nodes attained by increasing a node are added to a —- queue.
Answer: FIFO (i.e., First In, First Out)
Q23. BFS can be utilized to test —-, by beginning the hunt at any vertex and providing alternating labels to the vertices visited all through the search.
Answer: bipartiteness
Q24. —- is an algorithm for navigating or looking for a tree, tree structure, or graph.
Answer: Depth-first search (DFS)
Q25. A —- is a list of the vertices so that they were first visited by the depthfirst search algorithm.
Answer: preordering
Q26. A reverse postordering is the reverse of a postordering, which is defined a list of the vertices in the —- order of their previous visit.
Answer: conflicting
Q27. A —- search enhances competently the search process, but sacrifices the claims of wholeness.
Answer: heuristic
Q28. A very great and compound problem can be simply solved if it can be wrecked into smaller problems and —- could be used.
Answer: recursion
Q29. —- is the application of a rule by no means averts the later application of another rule that could also have been functional at the time the first rule was chosen.
Answer: Monotonic Production System
Q30. The problem is said to be —- when the computer will be specified a problem explanation and will create an answer, with no in-between communication and with he demand for a clarification of the reasoning process.
Answer: Solitary
Q31. In —- problem, neither jug contains any gauging markers on it.
Answer: water jug
Q32. There is a —- that can be accessed to fill the jugs with water.
Answer: pump
Q33. The —- comprises eight numbered, changeable tiles set in a 3 C 3 frame.
Answer: 8 puzzle
Q34. The program is to modify the initial configuration into the —- configuration.
Answer: objective
Q35. To solve a problem by means of a —- system, we must state the global database the rules, and the control strategy.
Answer: production
Q36. The problem goal condition generates the basis for the —- condition of the production system.
Answer: termination
Q37. The problem of forcing a robot to acclimatize to these changes is the foundation of the —- problem in artificial intelligence.
Answer: frame
Q38. —- level interprets what type of information is being inspected.
Answer: Semantic
Q39. —- level just decides in which format the information should be examined.
Answer: Syntactic
Q40. The —- problem recommends that one is never totally positive if a particular rule will work.
Answer: Qualification
Q41. The General Purpose of —- problem is to check the complete world of things that are changeable.
Answer: inferential
Q42. The —- problem illustrates how an action can cause deviations inside its environment.
Answer: Ramification
Q43. The —- portion of AI studies what types of facts regarding the world are accessible to a viewer with specified opportunities to scrutinize.
Answer: epistemological
Q44. Epistemological problems leave away the —- problems of how to search spaces of probabilities and how to match patterns.
Answer: heuristic
Q45. The problem of showing information regarding what remains unchanged by an —- was known as the frame problem.
Answer: event
Q46. Heuristic techniques are known as —- methods, as they are susceptible to combinatorial explosion.
Answer: weak
Q47. —- techniques give the frame work into which domain specific knowledge can be positioned.
Answer: Heuristic
Q48. The —- algorithm is a depth first search practice since complete possible solutions are produced before test.
Answer: generate and Test
Q49. Producing a possible solution for some problems; this means generating a particular point in the —- .
Answer: problem space
Q50. In —- the test function is offered with a heuristic function which offers an estimate of how close a known state is to goal state.
Answer: hill climbing
Q51. A b —-b b which is a flat area of the search space, in which adjacent states have the similar value.
Answer: plateau
Q52. A b —- b which is an area in the search that is superior than the surrounding areas, but can not be looked in a simple move.
Answer: ridge
Q53. —- is a amalgamation of depth first and breadth first searches.
Answer: Best First Search
Q54. —- is a precedence queue of nodes that have been evaluated by the heuristic function but which have not yet been extended into successors.
Answer: OPEN
Q55. —- are nodes that have already been produced and these nodes must be amassed since a graph is being used in partiality to a tree.
Answer: CLOSED
Q56. The Best First algorithm is an easy form of the —- algorithm.
Answer: A*
Q57. Many troubles in AI can be regarded as problems of —- where the goal state pleases a specified set of constraint.
Answer: constraint satisfaction
Q58. Constraint satisfaction problems can be solved by means of any of the —- approaches.
Answer: search
Q59. The —- process centers about locating the difference among current state and goal state.
Answer: means-ends analysis
Q60. Means- ends analysis is functional for many —- activities.
Answer: human planning
Q61. —- knowledge is rule-based knowledge that is used to match actions to situations by invoking appropriate rules.
Answer: Explicit
Q62. —- knowledge can be attained through dialogue, job shadowing, storytelling, and sharing of best practices and lessons learned.
Answer: Tacit
Q63. Knowledge acquisition can be considered as one of the technology contributing to the development of —- .
Answer: knowledge-based Systems (KBS)
Q64. —- is an area in artificial intelligence that is concerned with how to formally b thinkb , that is, how to use a symbol system to represent b a domain of discourseb
Answer: Knowledge Representation
Q65. A —- is an artificial intelligence data structure used to divide knowledge into substructures by representing b stereotyped situations.b
Answer: Frame
Q66. The frame contains —- on how to use the frame, what to expect next, and what to do when these expectations are not met.
Answer: Information
Q67. —- rules, which are arguably the most common form of knowledge representation in Artificial Intelligence, are ambiguous.
Answer: If-then
Q68. A —- network is a network which represents semantic relations between the concepts. This is often used as a form of knowledge representation.
Answer: Semantic
Q69. Predicate logic contains various standard logic symbols to represent —- .
Answer: Knowledge
Q70. Isa is used to demonstrate class —- .
Answer: Inclusion
Q71. Instance is used to demonstrate class —- .
Answer: Membership
Q72. A —- is a propositional letter or the negation of a propositional letter.
Answer: Literal
Q73. Resolution is considered as a basis of —- method.
Answer: Prologs Inference
Q74. When applied to problems outside the specified set, the —- begins to break down and a great deal of additional machinery is needed, defeating the original simplification.
Answer: Unification
Q75. These days, large programs are written in many different languages, and linked using b —- languagesb .
Answer: Interface Definition
Q76. In —- representation, knowledge is signified as static collection of details which are influenced by common procedures.
Answer: Declarative Knowledge
Q77. Details can be easily added to —- systems without altering the common procedures.
Answer: Declarative
Q78. In Procedural representation, knowledge is signified as —- .
Answer: Procedures
Q79. Most of the knowledge representation structures have been generated to manage programs that control —- .
Answer: Natural Language Input
Q80. —- are used to illustrate general sequence of events.
Answer: Scripts
Q81. —- models are used to illustrate general features shared between a set of rules in a production system.
Answer: Rule
Q82. —- contains three major classes of application: as a general-purpose programming language, a database language, and a knowledge representation language in AI.
Answer: Logic Programming
Q83. As a database language, logic programming simplifies —- databases, to comprise general clauses as well as facts.
Answer: Relational
Q84. Conditionals in logic programs are also known as —- .
Answer: Clauses
Q85. —- are the particular case where all of the conditions are atomic formulae.
Answer: Horn clauses
Q86. A search procedure must locate a path among initial and —- states.
Answer: Goal
Q87. The method of chaining backward from the preferred final state is known as —- .
Answer: Back tracing
Q88. The —- component identifies the meaning of the algorithm while the control component only affects its competence.
Answer: Logic
Q89. The —- of an algorithm can frequently be improved by enhancing the control component without altering the logic of the algorithm and thus without altering the meaning of the algorithm.
Answer: Competence
Q90. The approach in —- is towards the division of logic and control.
Answer: Databases
Q91. —- reasoning implies to basic rules of inference along with logic knowledge representations.
Answer: Formal
Q92. —- reasoning uses procedures that state how to possibly solve (sub) problems.
Answer: Procedural
Q93. —- lead to compensate for deficiency of knowledge.
Answer: Incompleteness
Q94. In —- reasoning if we expand at set of axioms we cannot withdraw any present declarations or axioms.
Answer: monotonic
Q95. In —- reasoning, wewant to sketch conclusion based on what is most probable to be correct.
Answer: Default
Q96. Non-Monotonic reasoning is common depiction of a class of —- .
Answer: reasoning
Q97. —- is fundamentally an extension of first-order predicate logic to comprise a modal operator, M.
Answer: Non-Monotonic Logic
Q98. Default logic brings in a new inference rule: —- which specifies if A is deducible and it is consistent to presume B then conclude C.
Answer: (A.B)/C
Q99. —- and the conclusions we execute on it is an example of monotonic reasoning.
Answer: Predicate logic
Q100. —- is a rule of speculation that permits you to come to the conclusion that the objects you can demonstrate that posses some property, p, are actually all the objects that posses that property.
Answer: Circumscription
Q101. In —-, assertions are connected through a network of dependency.
Answer: Truth Maintenance Systems (TMSs)
Q102. —- is a simple TMS such that it does not recognize anything regarding the structure of the assertions themselves.
Answer: Justification-Based Truth Maintenance Systems (JTMS)
Q103. —- is similar to JTMS except nodes (assertions) presume no relationships between them except ones overtly specified in justifications.
Answer: Logic-Based Truth Maintenance Systems (LTMS)
Q104. JTMS and LTMS follow a —- line of reasoning at a time and backtrack (dependency-directed) when required b depth first search.
Answer: Single
Q105. —- sustain alternative paths in parallel b breadth-first search.
Answer: Assumption-Based Truth Maintenance Systems (ATMS)
Q106. —- methods give a method for showing principles that are not certain (or uncertain) but for which there may be some assisting (or contradictory) confirmation.
Answer: Statistical
Q107. —- are (real) numbers in the range 0 to 1.
Answer: Probabilities
Q108. —- probability, P(A|B), signifies the probability of event A specified that we know event B has appeared.
Answer: Conditional
Q109. The set of all —- must be mutually exclusive and comprehensive.
Answer: hypotheses
Q110. P(—-) specifies the probability of A specified only Bb s evidence.
Answer: A|B
Q111. Probability = (number of desired outcomes) / (—-)
Answer: total number of outcomes
Q112. MYCIN signifies knowledge as a set of —- .
Answer: rules
Q113. MYCIN accesses rules to reason backward to clinical data evidence from its aim of forecasting a —- causing organism.
Answer: disease
Q114. The load of assuring —- is positioned on the rule writer.
Answer: independence
Q115. —- networks are also known as Belief Networks or Probabilistic Inference Networks primarily generated by Pearl (1988).
Answer: Bayesian
Q116.A Bayesian Network is define as a —- acyclic graph.
Answer: directed
Q117. Conditional probabilities measure the power of —- .
Answer: dependencies
Q118. A —- is an attribute value pair in its simplest outline.
Answer: Slot
Q119. A —- is a value that a slot can take b could be a numeric, string (or any data type) value or a pointer to a different slot.
Answer: Filler
Q120. A weak slot and filler structure does not consider the —- of the representation.
Answer: Content
Q121. In —- method, the view that spreading activation out of two nodes and locating their intersection locates relationships between objects.
Answer: Intersection Search
Q122. —- Semantic Networks permit for propositions to be made without assurance to truth.
Answer: Partitioned
Q123. —- Structures facilitates attribute values to be improved speedily where declarations are indexed by the entities and binary predicates are indexed by first argument.
Answer: Weak Slot and Filler
Q124. Frames can also be considered as an expansion to —- .
Answer: Semantic Nets
Q125.A —- is a compilation of attributes or slots and related values that portray some actual world entity.
Answer: Frame
Q126. Frames on their own are not mainly useful but frame systems are a powerful manner of encoding —- to sustain reasoning.
Answer: Information
Q127. The more —- the system it turns out to be more useful to use frames.
Answer: Structured
Q128. A —- is a set and its elements contain properties.
Answer: Class
Q129. A —- is a particular class whose elements are themselves classes.
Answer: Metaclass
Q130. A —- is a set of ordered pairs so one relation is a subset of another.
Answer: Relation
Q131. —- of default values appears when one element or class is an instance of a class.
Answer: Inheritance
Q132. Every slot has a domain and —- .
Answer: Range
Q133. —- initially generated to symbolize knowledge attained from natural language input.
Answer: Conceptual Dependency
Q134. CD representations of a sentence is constructed out of —-, which are not words belonging to the language but are abstract , these primitives are united to form the meanings of the words.
Answer: Primitives
Q135. For any 2 (or more) sentences that are equal in meaning there should be only —- depiction of that meaning.
Answer: One
Q136. Sentences are displayed as a series of —- portraying actions by means of both abstract and real physical situations.
Answer: Diagrams
Q137. Primitive Act —- defines movement of a body part by owner.
Answer: MOVE
Q138. Primitive Act —- defines Application of a physical force to an object.
Answer: PROPEL
Q139. MARGIE (Meaning Analysis, Response Generation and Inference on English) is used to model —- .
Answer: Natural Language Understanding
Q140. A —- is a structure that recommends a set of conditions which could be projected to follow on from one another.
Answer: Script
Q141. Script is alike to a thought sequence or a sequence of —- which could be anticipated.
Answer: Situations
Q142. Script could be measured to comprise of a number of —- but with more specialized roles.
Answer: Slots or Frames
Q143. —- component define the slots displaying objects included in events.
Answer: Props
Q144. If a specific script is to be applied it must be triggered and the —- is based on its significance.
Answer: Activating
Q145. Different trails may be permitted for dissimilar —- of Scripts.
Answer: Outcomes
Q146. Scenes define the sequence of —- that take place.
Answer: Events
Q147. Different tracks may share components of the —- script.
Answer: Same
Q148. —- is a field of computer science concerned with the interactions between computers and human (natural) languages.
Answer: Natural Language Processing
Q149. b —-b language means, among other things, knowing what concepts a word or phrase stands for and knowing how to link those concepts together in a meaningful way.
Answer: Understanding
Q150. Specific problem components of —- ambiguity include sentence boundary disambiguation.
Answer: Syntactic
Q151. A —- can often be considered an action by the speaker.
Answer: Sentence
Q152. The goal of the Natural Language Processing (NLP) group is to design and build —- that will analyze, understand, and generate languages that humans use naturally.
Answer: Software
Q153. In —- Analysis, Individual worlds are scrutinized into their components and non word tokens, like punctuation are alienated from the words.
Answer: Morphological
Q154. In —-, Linear sequences of words are malformed into structures that illustrate how the words associate to each other.
Answer: Syntactic Analysis
Q155. —- concentrates on scrutinizing the words in a sentence so as to reveal the grammatical arrangement of the sentence.
Answer: Syntactic Processing
Q156. Design patterns intend to increase the flexibility of a model by —- some aspects of a class.
Answer: Decoupling
Q157. The term —- includes both spoken and written forms, as well as both monologue and dialogue.
Answer: Discourse
Q158. —- is what makes a collections of sentences/utterances a discourse.
Answer: Coherence
Q159. The goal of —- is the detection and rectification of typographic and orthographic faults in the text at the level of word incidence measured out of its perspective.
Answer: Spell Checking
Q160. Some people do not recognize the correct spelling of some words, particularly in a foreign language. Such errors are known as —- .
Answer: Spelling Errors
Q161. The amount of linguistic information required for spell checkers is much superior than for —- .
Answer: Hyphenation
Q162. Initially, a spell checker simply detects the strings that are not accurate words in a specified —- language.
Answer:
Q163. —- is defined as modifications in the system that are adaptive in the sense that they facilitate the system to do the similar task or tasks taken from the similar population more competently and more successfully the next time.
Answer: Learning
Q164. Learning that involves —- leaves the learner able to perform better in situations not previously encountered.
Answer: Generalization
Q165. —- is also known as memorization since the knowledge, without any alteration is, just copied into the knowledge base.
Answer: Rote Learning
Q166. —- is a key requirement for learning.
Answer: Memorization
Q167. The knowledge must be transformed into an —- form before stored in the knowledge base.
Answer: Operational
Q168. The thought of generating a classification program that can develop its own class definitions is known as —- .
Answer: Concept Learning or Induction
Q169. The objective of —- is to generate a description that is reliable with all positive examples but no negative examples in the training set.
Answer: Version Spaces
Q170. An —- system accepts an example (i.e. a training example) and illustrates what it learns from the example.
Answer: Explanation-based Learning (EBL)
Q171. —- is a system that incorporates problem solving, planning, and learning methods in a single design.
Answer: PRODIGY
Q172. In case of learning by —-, learning system depends on evaluation procedure that merges information from numerous sources into a single summary static.
Answer: Parameter Adjustment
Q173. Series of actions that can be considered as a whole are known as —- .
Answer: Macro-operators
Q174. Generalization is performed by substituting all the —- in the macro-operators with variables.
Answer: Constants
Q175. A —- system comprise of a set of rules that are in if-then outline.
Answer: Production
Q176. A —- is nothing but a great production that does the work of the whole sequence of smaller ones.
Answer: Chunk
Q177. In —- learning, the learner utilizes his own experience and prior knowledge to learn the truths that are to be studied.
Answer: Discovery
Q178. —- starts by doing trait withdrawal on data items and compute the values of Notes the selected feature set.
Answer: Clustering
Q179. When learning new things, there is a likelihood that the learning system may make —- .
Answer: Errors
Q180. Learning by recoding cases method is chiefly used in —- learning tasks
Answer: Natural Language
Q181. That is, —- examples simplify specific models and negative examples specialize common models.
Answer: Positive
Q182. A version space portrayal includes two —- which harmonize to each other: one symbolize general model and the other symbolize specific model.
Answer: Trees
Q183. An —- is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence.
Answer: Expert System
Q184. Knowledge base contains essential information about the problem —- .
Answer: Domain
Q185. —- chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached.
Answer: Forward
Q186. —- chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals.
Answer: Backward
Q187. The —- usually sees an expert system through an interactive dialog.
Answer: End-user
Q188. An —- is a statement that has two parts, an if-clause and a then-clause.
Answer: Inference Rule
Q189. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the —- .
Answer: Operating Systems
Q190. Expert system encourages organizations to clarify the logic of their —- .
Answer: Decision-making
Q191. Expert System lacks —- needed in some decision making.
Answer: Common Sense
Q192. Typically, the —- to be solved are of the sort that would normally be tackled by a medical or other professional.
Answer: Problems
Q193. Generally, expert systems are used for problems for which there is no single b correctb solution which can be encoded in a —- algorithm.
Answer: Conventional
Q194. A —- is a complete development environment for building and maintaining knowledge-based applications.
Answer: Shell
Q195. The —- is a command line interpreter developed by Byron Rakitzis and Paul Haahr, that uses a scripting language similar to the rc shell of the Plan 9 operating system.
Answer: Es Shell
Q196. The description of knowledge is quite vague and imprecise, and includes a great deal of —- .
Answer: Uncertainty
Q197. —- can be defined as identifying qualitative structures, identifying the states of those qualitative structures, and the pattern of changes (transformations) in those states.
Answer: Qualitative Analysis
Q198. Automatic —- perceptive is the process by which a computer plots an auditory speech signal to some form of conceptual meaning of the speech.
Answer: Speech
Q199. —- is the job of converting written input to spoken output.
Answer: Speech Synthesis
Q200. Prolog is a logical and a declarative —- language.
Answer: Programming
Q201. Prolog is based upon —- principle (1965) together with mechanisms for extracting answers proposed by Green (1968)
Answer: Robinsonb s Resolution
Q202. In ProLog, —- describe the relationships between different objects and are independent of each other.
Answer: Facts
Q203. In ProLog, —- are used in the process of decision-making and can deduce new facts from existing ones.
Answer: Rules
Q204. Proposing a —- means demanding Prolog to attempt to prove that the statement(s) implied by the query can be prepared true given the right variable instantiations are prepared.
Answer: Query
Q205. —- is composed of a sequence of characters that is parsed by the Prolog reader as a single unit.
Answer: Atom
Q206. A —- is composed of an atom called a functor and a number of b argumentsb , which are again terms.
Answer: Compound Term
Q207. The predicate —- functions in the traditional manner, i.e., whenever backtracking reaches it, execution continues forward again via the same clauses as if another substitute had been found.
Answer: Repeat
Q208. The query —- locates exactly one solution to Goal. It is comparable to call((Goal,!)) and is opaque to cuts.
Answer: Once
Q209. Prolog must be able to manage arithmetic in order to be a —- general purpose programming language.
Answer: Functional
Q210. Two terms are defined to —- if they are either indistinguishable or if they can be made indistinguishable by variable instantiation.
Answer: Match
Q211. The similar variable has to be instantiated with the similar —- all through an expression.
Answer: Value
Q212. The —- predicate provides an alternative path for a program when the current path fails.
Answer: Backtracking
Q213. Backtracking allows Prolog to nd all —- solutions to a given query.
Answer: Alternative
Q214. A —- is an ordered sequence of zero or more terms.
Answer: List
Q215. Each element in the list is accompanied by a —-, which indicates the location of the next element in the list.
Answer: Pointer
Q216. Input/output predicates in which no stream is mentioned address the —- streams.
Answer: Current
Q217. You should —- a current stream only if you desire to consequently use it frequently Notes for input or output.
Answer: Redefine
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