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Abductive reasoning: Quiz


Question 1: In ________, explanation is done from a logical theory T representing a domain and a set of observations O.

Question 2: ________ is a computational framework that extends normal logic programming with abduction.
Aspect-oriented programmingConstraint programmingAbductive logic programmingConstraint logic programming

Question 3: ________ generalises probabilistic logic by including parameters for uncertainty in the input arguments.
Dempster–Shafer theoryBayesian networkSubjective logicBeta distribution

Question 4: ________ is the process of validating a given hypothesis through abductive reasoning.
Inductive reasoningAbductive reasoningAristotleWillard Van Orman Quine

Question 5: In intelligence analysis, ________ and Bayesian networks, probabilistic abductive reasoning is used extensively.
Open source intelligenceAnalysis of Competing HypothesesIntelligence cycle managementFinancial intelligence

Question 6: The term abduction is commonly presumed to mean the same thing as ________; however, an abduction is actually the process of inference that produces a hypothesis as its end result[1].
HypothesisScientific methodPlatoEmpiricism

Question 7: Abduction is a method of logical inference introduced by ________ which comes prior to induction and deduction for which the colloquial name is to have a "hunch".
Scientific methodAlfred North WhiteheadCharles Sanders PeirceAristotle

Question 8: Applications in ________ include fault diagnosis, belief revision, and automated planning.
René DescartesKarl PopperArtificial intelligenceDaniel Dennett

Question 9: A proof theoretical abduction method for first order classical logic based on the sequent calculus and a dual one, based on semantic tableaux (________) have been proposed (Cialdea Mayer & Pirri 1993).
Method of analytic tableauxPropositional calculusFirst-order logicLogical connective

Question 10: Criteria for picking out a member representing "the best" explanation include the simplicity, the ________, or the explanatory power of the explanation.
Bayesian probabilityLikelihood functionPrior probabilityKullback–Leibler divergence


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