Description
AI True/False Questions:
(1) Under the AIMA structure, a sensible agent relies on a deductive strategy for making choices.
FALSE; the model describes it as an entity that behaves logically by selecting moves to enhance its overall benefit.
(2) The setting around an agent qualifies as probabilistic if the following condition is fully dictated by the existing situation and the agent’s decision.
FALSE; in probabilistic settings, outcomes from decisions carry elements of chance.
(3) Exploration starting from the deepest levels is a perfect, blindfolded search strategy.
FALSE; it lacks perfection in results.
(4) The opportunistic exploration technique functions by continually assessing the descendants of the untouched node in the exploration zone that an evaluative guess considers nearest to an objective point.
TRUE
(5) An exploration method is viewed as dependable only when it consistently uncovers an answer to an exploration issue provided one is available.
FALSE; a dependable method produces accurate outcomes, though it might miss some entirely.
(6) When h1(n) and h2(n) serve as two allowable A* assessment tools, their total h(n) = h1(n) + h2(n) would similarly qualify as allowable.
FALSE; it could inflate estimates, rendering it invalid for allowance.
(7) Tackling various Limitation Fulfillment Issues (LFIs) through proactive verification or connection reliability often necessitates some form of retrospective exploration.
TRUE
(8) A limitation issue encompassing three elements (for instance, A + B < C) is unable to be depicted in a limitation diagram since such diagrams solely handle single or dual connections.
FALSE
(9) Employing the max-min process with or without branch-cutting optimization will invariably pinpoint the superior choice for the participant about to act.
TRUE
(10) Max-min combined with branch-cutting optimization enhances the quantity of eliminated paths when options beneath a point are sequenced from superior to inferior.
TRUE
(11) Basic upward progression is a thorough method for resolving limitation fulfillment issues.
FALSE
(12) Within a balanced-opposition duo contest, there must inevitably be a victor and a defeated party.
FALSE
(13) Max-min with and without branch-cutting optimization may occasionally yield varying outcomes.
FALSE
(14) The contest-theory model is capable of representing contests that lack balanced opposition.
TRUE
(15) A balanced outcome in a contest represents a set of participant tactics where no individual can enhance their result by altering their tactic alone.
TRUE
(16) A “sensible entity” consistently executes the optimal decision in any scenario.
FALSE; a sensible entity might identify the decision yielding the highest benefit even without full data, but the query’s intent is unclear, leading to potential true interpretations.
(17) Wide-level exploration where every connection costs one unit will unfailingly discover the briefest route to an endpoint if one is present.
TRUE
(18) Progressive deepening exploration demands additional storage than wide-level exploration.
FALSE; progressive deepening conducts multiple depth-oriented explorations until an endpoint appears, requiring storage linked to the graph’s peak depth. Wide-level exploration, in extreme cases, needs space tied to branching raised to solution depth.
(19) If h1(n) and h2(n) represent distinct allowable assessment tools, then the midpoint (h1(n) + h2(n))/2 is inevitably allowable as well.
TRUE
(20) A benefit of upward progression exploration lies in its need for merely a fixed storage amount during issue resolution.
TRUE
(21) Depth-oriented exploration invariably assesses no fewer points than A* exploration using an allowable assessment tool.
FALSE
(22) Upward progression exploration methods solely function for exploration areas that are flat or possess answer-maintaining mappings to flat spaces.
FALSE
(23) Resolving limitation fulfillment issues using proactive verification and connection reliability techniques eliminates the need for retrospective exploration.
FALSE
(24) A captive’s quandary contest serves as an instance of a duo, incomplete-data, balanced-opposition contest.
FALSE; it does not qualify as balanced-opposition.
(25) An impeccably sensible backgammon entity applying max-min with boundless foresight would never suffer defeat.
FALSE; in backgammon, possible actions depend on chance from dice, so unfortunate sequences could cause loss.
(26) A basic reaction entity can exhibit sensibility.
FALSE; a reaction entity reacts identically to scenarios without weighing anticipated results.
(27) An entity cannot behave sensibly in a setting with limited visibility.
FALSE; sensibility means selecting a decision that boosts expected results based on available, possibly incomplete, knowledge.
(28) Progressive deepening exploration entails repeatedly executing wide-level exploration with growing depth boundaries.
FALSE; it performs this using depth-oriented exploration, not wide-level.
(29) Modeled thermal softening is a twist on upward progression exploration that avoids trapping in nearby peaks.
TRUE; modeled thermal softening resembles upward progression but includes fluctuating chances of picking less-than-ideal steps.
(30) A perfect answer trail for an exploration issue with solely affirmative expenses will avoid duplicate conditions.
TRUE; in trails with duplicates, eliminating loops creates a lower-expense trail.
(31) The branch-cutting max-min method is favored over plain max-min as it delivers identical results typically while skipping more of the contest structure.
TRUE; branch-cutting and plain max-min yield matching figures, but branch-cutting skips sections of the structure.
(32) Certain limitation fulfillment issues expressible via triple-element limitations cannot use dual limitations.
FALSE; such issues can convert to equivalents adding extra elements, restricting to dual limitations.
(33) Contest theory exclusively applies to unbalanced-opposition contests.
FALSE; a key aspect of contest theory is its applicability to both balanced and unbalanced opposition contests.
(34) Connection reliability offers stronger limitation spreading than proactive verification.
TRUE; proactive verification targets only elements with one viable option, removing options from connected elements. Connection reliability addresses all elements, discarding options unable to meet linked limitations.
(35) If h1 and h2 qualify as allowable exploration assessment tools, then h3 = 2h1 – h2 inevitably qualifies too.
FALSE; consider when h1 equals the actual gap and h2 equals zero.
(36) Alan Turing suggested his renowned evaluation as a way to determine if an issue was solvable by his machine model.
FALSE; the evaluation gauges “smartness” without direct ties to solvability.
(37) An opportunistic, priority-driven exploration method is consistently thorough.
FALSE; endless cycles might prevent solution discovery.
(38) A straightforward wide-level exploration unfailingly locates the shortest answer if one exists with bounded span.
TRUE
(39) For an exploration issue, the trail from even-expense exploration could shift by appending a fixed positive value to each step expense.
TRUE; with paths S to A to G costing 1+1=2, and S to G costing 3, the best is via A. Adding 2 per step makes S to G cost 5, via A costs 5, but example shows shift in optimality.
(40) The branch-cutting method is chosen over max-min for offering superior guess of optimal choice at set foresight span.
FALSE; branch-cutting and max-min deliver identical figures.
(41) In a duo, balanced-opposition contest, a victor and defeated always emerge.
FALSE; numerous such contests allow ties.
(42) In a captive’s quandary, participants select overriding tactics, yet both might fare better with alternate choices.
TRUE
(43) Contest theory forecasts that participants invariably possess an overriding tactic.
FALSE; various contests lack overriding tactics.
(44) Reciprocal action is a tactic unusable in recurring contests.
FALSE; reciprocal action is favored in contests with ongoing interactions.
(45) If h1(s) and h2(s) act as allowable A assessment tools, their midpoint (h1(s) + h2(s))/2 inevitably acts allowably.
TRUE; with h(s) as real span, h1(s) ≤ h(s) and h2(s) ≤ h(s), so average ≤ h(s).
(46) In a probabilistic setting, the following condition is wholly set by the entity’s decision.
FALSE; probabilistic settings include randomness.
(47) Upward progression exploration methods function only for flat exploration areas or those with answer-keeping flat mappings.
FALSE
(48) If assessment tool H1(s) is allowable and H2(s) is not, then H3(s) = min(H1(s), H2(s)) remains allowable.
TRUE; as H3(s) ≤ H1(s) ≤ true span.
(49) In A* exploration, the initial trail to the endpoint added to the boundary is invariably perfect.
FALSE; the initial trail extracted from the boundary is perfect with allowable tool.
(50) If a Limitation Fulfillment Issue (LFI) achieves connection reliability, resolution avoids retrospection.
FALSE; connection reliability might not fully resolve.
(51) Determining LFI consistency generally falls into hard computational complexity.
TRUE
(52) Connection reliability provides mightier limitation spreading than proactive verification.
TRUE
(53) Branch-cutting can modify the figured max-min worth at a contest exploration root.
FALSE; branch-cutting accelerates figuring without altering results.
(54) Max-min with branch-cutting on a left-to-right traversed contest structure avoids eliminating the farthest left path.
TRUE; no options exist to justify elimination on the extreme left path.
(55) Board contests like chess, checkers, and go feature settings with limited visibility.
FALSE; full condition appears on the board.
(56) The Turing evaluation judges a setup’s capacity for sensible behavior.
FALSE
(57) Iterative deepening will never expand more nodes than breadth-first search.
TRUE
(58) If a bounded answer exists, depth-oriented exploration ensures its discovery.
FALSE
(59) A bounded issue diagram can produce an endless exploration tree via depth-oriented exploration.
TRUE
(60) Depth-oriented progressive deepening yields identical answer as wide-level exploration with finite branching and set successor sequence.
TRUE
(61) In a bounded exploration area without endpoint, A* explores every condition.
TRUE
(62) If f1(s) and f2(s) are allowable A* tools, their midpoint f(s) = 0.5*(f1+f2) inevitably allowable.
TRUE
(63) A challenge with upward progression exploration is its storage demand.
FALSE
(64) The connection-reliability method proves useful solely post each element assignment in LFI exploration.
FALSE
(65) Merging retrospective exploration and connection-reliability invariably uncovers an LFI answer if available.
TRUE
(66) Merging retrospective exploration and proactive verification might miss an LFI answer despite availability.
FALSE
(67) The max-choice principle in contest theory relies on planning to exploit opponent’s missteps.
FALSE
(68) In a balanced-opposition duo contest, a victor and defeated must always occur.
FALSE
(69) Storage needed for max-min with branch-cutting is branching to power of depth.
FALSE
(70) The captive’s quandary exemplifies a contest where both sides hold overriding tactics.
TRUE
(71) In a balanced outcome, no participant can singly boost their benefit by tactic shift.
TRUE
(72) All properly structured propositional expressions can convert to joined normal structure.
TRUE
(73) All legitimate propositional expressions are fulfillable.
TRUE
(74) All fulfill-able propositional expressions are legitimate.
FALSE
(75) A dependable and thorough deduction setup for propositional expressions is possible using just the combination rule.
TRUE
(76) A probabilistic setting has the next condition fully set by the entity’s decision.
FALSE
(77) A supposedly complete visibility setting can turn limited due to sensor inaccuracies and interference.
TRUE
(78) Depth-oriented exploration always assesses at least as many points as A* with allowable tool.
FALSE
(79) The primary flaw in upward progression is its assurance only for flat exploration areas.
FALSE
(80) A* method might assess a suboptimal answer point with allowable tool.
FALSE
(81) Method A conducts wide-level exploration if evaluation equals path expense.
TRUE
(82) The key benefit of progressive deepening A* over regular A* is linear storage based on exploration depth.
FALSE
(83) Basic wide-level exploration always locates perfect answer if existent, finite, with bounded successors per point.
TRUE
(84) Progressive deepening exploration practically incorporates assessment tools into method A.
FALSE
(85) Method A performs depth-oriented exploration if evaluation = negative path expense.
TRUE
(86) A plus of upward progression exploration is low storage need.
TRUE
(87) Dual-direction exploration always outperforms single-direction.
FALSE
(88) Connection reliability is mightier in limitation spreading than proactive verification.
TRUE
(89) Judging LFI consistency is typically hard computationally.
TRUE
(90) Branch-cutting method is favored over max-min for superior guess of best choice at fixed foresight.
FALSE
(91) A limitation in contest-playing approaches that utilize the max-min and branch-trimming techniques lies in their inability to execute trades allowing a short-term setback for a future gain.
FALSE
(92) The max-min and branch-trimming methods will consistently propagate matching assessments to the primary node of a contest diagram regardless of the chosen static appraisal mechanism.
TRUE
(93) An flawlessly logical entity in backgammon contests avoids defeat entirely.
FALSE
(94) The captive’s conundrum demonstrates that contest theory lacks reliability.
FALSE
(95) Contest theory exclusively pertains to balanced-opposition scenarios.
FALSE
(96) All thorough deduction methods are likewise dependable.
TRUE
(97) All dependable deduction methods are likewise thorough.
FALSE
(98) A partial deduction method might generate a wrong conclusion.
FALSE
(99) An unreliable deduction method might generate a wrong conclusion.
TRUE
(100) In Prolog, a variable’s range is limited to its containing expression.
TRUE
(101) Prolog scripts are dependable yet partial.
FALSE
(102) Upward progression exploration techniques function solely for flat exploration realms or those with resolution-maintaining flat projections.
FALSE
(103) Progressive deepening essentially merges the superior traits of wide-level and depth-oriented exploration.
TRUE
(104) Excessive fitting may arise in automated learning when the dataset includes numerous unrelated characteristics.
TRUE
(105) The choice structure learning method discussed in sessions identifies a perfect choice structure, meaning one that reduces the quantity of inquiries required to categorize an instance.
FALSE
(106) The principle of simplicity favors the most basic aligned interpretation.
TRUE
(107) Choice structure learning is inapplicable if the dataset contains interference.
FALSE
(108) Within an appropriate probabilistic graph, an element remains conditionally unrelated to its non-offspring provided its predecessors.
TRUE
(109) Data yield serves to establish the graph layout in a probabilistic network.
FALSE
(110) Stochastic elements X and Y are unrelated if the joint likelihood equals the conditional likelihood times the marginal.
FALSE
(111) Should two elements be unrelated, they remain conditionally unrelated given any additional element.
FALSE
(112) When seeking a strategy, a flexible sequence strategist navigates a realm composed of potential circumstances.
FALSE
(113) A flexible sequence strategist may progress ahead from a starting point to an objective, reverse from an objective to the start, or bidirectionally toward the center.
FALSE
(114) The circumstance formalism addresses alterations in stochastic elements via primary-order reasoning.
FALSE
(115) Combination is contradiction-thorough, implying that for inconsistent expressions, a conflict emerges in bounded duration.
TRUE
(116) To employ combination, transform your expression into joined normal structure.
FALSE
(117) A contradiction demonstration verifies that an expression follows from a data repository by incorporating its opposite and deriving a conflict.
TRUE
(118) Inference via assumption is dependable but potentially partial.
FALSE
(119) In primary-order reasoning, variables solely span items, excluding connections, assertions, or operations.
TRUE
(120) The circumstance formalism represents action impacts through reasoning.
TRUE
(121) A properly structured probabilistic conviction graph excludes loops.
TRUE
(122) The reversal rule links the chance of an origin given manifestations to the chance of manifestations given an origin.
TRUE
(123) In a probabilistic conviction graph, two elements are unrelated solely without a non-directed route connecting them.
FALSE
(124) Excessive fitting happens when an automated learning setup models arbitrary flaws or disturbances rather than core connections.
TRUE
(125) Margin-based classifiers demand that every trait possesses a bounded set of options.
FALSE
(126) Although margin-based classifiers identify a straight mix of traits to divide affirmative and adverse cases of a group, the “transformation technique” may add non-straight elements.
TRUE
(127) All choice structures can convert to a collection of guidelines.
TRUE
(128) The choice3 choice structure acquisition method locates a perfect choice structure.
FALSE
(129) Removing elements from a choice structure might leave the final categorizer unchanged in application.
TRUE
(130) The combination deduction guideline is broad and applicable for assumption-based deduction.
FALSE
(131) The simplicity heuristic favors the most basic aligned interpretation.
TRUE
(132) An unreliable deduction method might yield a wrong conclusion.
TRUE
(133) All dependable thinkers are thorough, and all thorough thinkers are dependable.
FALSE
(134) A challenge with the linear strategist is that it might reverse the fulfillment of one sub-objective while pursuing another.
TRUE
(135) Each strategy discovered by a flexible sequence strategist converts to at least one straight strategy.
TRUE
(136) Two chance binary elements are unrelated solely if the likelihood both hold equals the product of individual likelihoods.
TRUE
(137) A properly structured probabilistic conviction graph may include up to one loop.
FALSE
(138) A basic reversal categorizer presumes all traits are unrelated.
TRUE
(139) The reversal rule links the chance of an origin given manifestations to the chance of manifestations given an origin.
TRUE
(140) An element in a probabilistic conviction graph may possess dual predecessors but solely one successor.
FALSE
(141) Awareness of an element’s value in a probabilistic graph informs the probability of every non-unrelated element’s value.
TRUE
(142) The choice3 choice structure induction method employs data yield and ensures locating the perfect choice structure aligned with provided training instances.
FALSE
(143) Guided learning requires solely training instances with known correct responses.
TRUE
(144) Choice structure learning solely produces dual categorizers.
FALSE
(145) Grouping methods exemplify unguided learning.
TRUE
(146) Excessive fitting arises when an automated learning setup trains on dual instances with varying tags.
FALSE
(147) A benefit of a choice structure setup is deriving a guideline-based categorizer from it.
TRUE
(148) Prediction modeling is an automated learning approach that predicts elements with real-valued outcomes.
TRUE
(149) Closest-group grouping operates on data points as sequences of size closest.
FALSE
(150) Dependable deduction methods avoid deriving false claims from true ones.
TRUE
(151) Legitimate expressions hold in every interpretation.
FALSE
(152) Reverse spread is a method for modifying connections in a basic level neural unit.
FALSE
(153) A limitation of a basic reversal categorizer is needing numerous combined chance charts.
FALSE
(154) A margin vector setup categorizer applies directly solely for dual categorization.
TRUE
(155) A basic level neural unit learns solely straight-divisible configurations.
TRUE
(156) The choice3 choice structure induction method ensures locating the perfect choice structure aligned with provided training instances.
FALSE
(157) Stochastic elements X and Y are unrelated if joint likelihood equals conditional times marginal.
FALSE
(158) Should the mystery strategist locate a strategy, it ensures optimality, meaning no shorter strategy exists.
TRUE
(159) Circumstance formalism adjusts connections in a neural setup during acquisition.
FALSE
(160) All thorough deduction methods are dependable.
TRUE
(161) All dependable deduction methods are thorough.
FALSE
(162) A dependable yet partial deduction method might yield a wrong conclusion.
FALSE
(163) An unreliable deduction method might yield a wrong conclusion.
TRUE
(164) Each interpretation of (x and (y or z)) serves as an interpretation of (x or (y or z)).
TRUE
(165) Should a claim-based expression be legitimate, it fulfills in all interpretations.
TRUE
(166) The linear planning method aims to locate the briefest viable strategy for an issue.
FALSE
(167) Reverse strategists seek strategies by beginning at an objective and progressing toward the start.
TRUE
(168) Choice structure learning solely produces dual categorizers.
FALSE
(169) In a properly structured probabilistic conviction graph, an element remains conditionally unrelated to non-offspring given predecessors.
TRUE
(170) Data yield establishes the graph layout in a probabilistic network.
FALSE
(171) The reversal rule links the chance of an origin given manifestations to the chance of manifestations given an origin.
TRUE
(172) A key benefit of margin vector setups is direct implementation of categorizers with numerous groups.
FALSE
(173) The closest-neighbors method is an unguided automated learning approach for grouping points into closest clusters where closest is a positive integer.
FALSE
(174) A limitation of the closest-means method is requiring specification of exact cluster count.
TRUE
(175) Ensemble merging aids in constructing probabilistic conviction graphs by spotting statistically related elements.
FALSE
(176) Excessive fitting arises when an automated learning setup trains on dual instances with varying tags.
FALSE
(177) The accuracy measure in automated learning is the proportion of correct affirmatives to the total of correct and incorrect affirmatives.
TRUE
(178) The accuracy measure in automated learning is the proportion of correct affirmatives to the total of correct and incorrect affirmatives.
TRUE
(179) An acquisition graph in automated learning illustrates the balance between a setup’s accuracy and recovery.
FALSE
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