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1µ¥Ñ¡(1·Ö)ÓÐÁ½ÖÖ8»ÊºóÎÊÌâµÄÐÎʽ»¯·½Ê½¡£¡°³õʼʱ8¸ö»Êºó¶¼·ÅÔÚÆåÅÌÉÏ£¬È»ºóÔÙ½øÐÐÒÆ¶¯¡±ÊÇÄÄÒ»ÖÖÐÎʽ»¯·½Ê½£¿µÃ·Ö/×Ü·Ö

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A. A*ËÑË÷

B.Éî¶ÈÓÅÏÈËÑË÷0.33/1.00 C. Ò»Ö´ú¼ÛËÑË÷0.33/1.00 D.Éî¶ÈÊÜÏÞËÑË÷

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? A.f(n) = g(n)

? B.f(n) = h(n)1.00/1.00 ? C.f(n) = g(n) - h(n) ?

D.f(n) = g(n) + h(n)

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3µ¥Ñ¡(1·Ö)Éèh(n)±íʾÆô·¢Ê½º¯ÊýÇÒg(n)±íʾ´ú¼Û£¬ÔòA*ËÑË÷ËùʹÓÃµÄÆÀ¼Ûº¯ÊýÊÇ:µÃ·Ö/×Ü·Ö

? A.f(n) = h(n) ? B.f(n) = g(n)

? C.f(n) = g(n) + h(n)1.00/1.00 ?

D.f(n) = g(n) - h(n)

ÕýÈ·´ð°¸£ºCÄãÑ¡¶ÔÁË

4¶àÑ¡(1·Ö)ÏÂÁÐÄÄЩÏîÓÃÓÚ¶ÔÎÊÌâ½øÐÐÐÎʽ»¯µÃ·Ö/×Ü·Ö

? A.³õʼ״̬0.33/1.00 ? B.·¾¶¼ì²â

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A.ÓÐÐÅÏ¢ËÑË÷0.50/1.00 ? B.¶þÔªËÑË÷ ? C.ÎÞÐÅÏ¢ËÑË÷

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D. Æô·¢Ê½ËÑË÷0.50/1.00

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7Ìî¿Õ(1·Ö)ijЩ_______»òNPÄÑÎÊÌâÖ»ÄÜͨ¹ýËÑË÷À´Çó½â¡£µÃ·Ö/×Ü·Ö

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8Ìî¿Õ(1·Ö)ÎÊÌâÐÎʽ»¯ÊǸø¶¨Ò»¸öÄ¿±ê£¬¾ö¶¨Òª¿¼ÂǵÄ_______Óë״̬µÄ´¦Àí¡£µÃ·Ö/×Ü·Ö ÕýÈ·´ð°¸£º¶¯×÷ Quizzes for Chapter 4

1µ¥Ñ¡(1·Ö)Keeping just one node in memory might seem to be an extreme reaction to the problem of memory limitations. Local beam search keeps track of:ÔÚÄÚ´æÖнö±£´æÒ»¸ö½ÚµãËÆºõÊǶÔ

ÄÚ´æÏÞÖÆÎÊÌâµÄ¼«¶Ë·´Ó¦¡£¾Ö²¿ÊøËÑË÷±£³Ö£ºµÃ·Ö/×Ü·Ö

?

A.m states rather than n states.m¸ö״̬¶ø²»ÊÇn

¸ö

?

B.just m states rather than n states½öÓÐn״̬¶ø

²»ÊÇm

? C.k states rather than just one.k¸ö״̬¶ø²»½ö½ö

Ϊһ1.00/1.00

?

D.just one rather than k states.½öÓÐÒ»¸ö״̬¶ø²»

ÊÇk¸ö

2µ¥Ñ¡(1·Ö)A genetic algorithm is a variant of stochastic beam search, in which successor states are generated:ÒÅ´«Ëã·¨ÊÇËæ»úÊøËÑË÷µÄÒ»¸ö±äÌ壬ÆäÖкó¼Ì½ÚµãµÄÉú³ÉÊÇÓÉ£ºµÃ·Ö/×Ü·Ö

? C.The inner loop of the simulated annealing

algorithm is quite similar to hill climbing.Ä£ÄâÍË»ðËã·¨µÄÄÚÑ­»·ÓëÅÀɽ·¨·Ç³£ÏàËÆ¡£0.50/1.00

? A.by combining two parent nodes rather than by

modifying a single node.×éºÏµ¥Ò»×´Ì¬¶ø²»ÊÇ×éºÏÁ½¸öË«Ç×״̬¡£

? D.Instead of picking the best move, simulated

annealing algorithm picks a random move.Ä£ÄâÍË»ðËã·¨²»ÊÇÑ¡Ôñ×î¼ÑÐж¯£¬¶øÊÇÑ¡ÔñËæ»úÐж¯¡£0.50/1.00

5Ìî¿Õ(1·Ö)Ant Colony Optimization (ACO) algorithm was inspired by the behavior of ants seeking a path between

? B.by combining two parent states rather than by

modifying a single state.×éºÏÁ½¸öË«Ç×״̬¶ø²»ÊÇÐ޸ĵ¥Ò»×´Ì¬¡£1.00/1.00

? C.by modifying a single node rather than by

_______ and a source of food.ÒÏȺÓÅ»¯Ëã·¨ÊÇÊÜÂìÒÏÔÚcombining two parent nodes.×éºÏµ¥Ò»½Úµã¶ø²»ÊÇ×éºÏÁ½¸öË«Ç׽ڵ㡣

? D.by modifying a single state rather than by

combining two parent states.×éºÏÁ½¸öË«Ç×½Úµã¶ø²»ÊÇÐ޸ĵ¥Ò»½Úµã¡£

3¶àÑ¡(1·Ö)Hill climbing search is sometimes called greedy local search because it grabs a good neighbor state without thinking ahead about where to go next. Unfortunately, it often gets stuck for the three reasons:ÅÀɽËÑË÷ÓÐʱҲ±»³ÆÎªÌ°À·¾Ö²¿ËÑË÷£¬ÒòΪËüÖ»¹Ëץסһ¸öºÃµÄÁÚ½ÓµãµÄ״̬£¬¶ø²»Ìáǰ˼¿¼ÏÂÒ»²½¸ÃÈ¥ÄĶù¡£ËüÔÚÈýÖÖÇé¿öϾ­³£±»À§£ºµÃ·Ö/×Ü·Ö

? A. RidgesɽÁë0.33/1.00 ? B.Mountain¸ßɽ

? C.Plateaux¸ßÔ­0.33/1.00

?

D.Local maxima¾Ö²¿×î´óÖµ0.33/1.00

4¶àÑ¡(1·Ö)Which of the following statements are true about Simulated annealing algorithm£¿ÒÔϹØÓÚÄ£ÄâÍË»ðËã·¨µÄ³ÂÊöÄÄЩÊÇÕýÈ·µÄ£¿µÃ·Ö/×Ü·Ö

? A.Instead of picking the random move, simulated

annealing algorithm picks a best move.Ä£ÄâÍË»ðËã·¨²»ÊÇÑ¡ÔñËæ»úÐж¯£¬¶øÊÇÑ¡Ôñ×î¼ÑÐж¯¡£

? B.The inner loop of the simulated annealing

algorithm is very different from hill climbing.Ä£ÄâÍË»ðËã·¨µÄÄÚÑ­»·ÓëÅÀɽ·¨ÍêÈ«²»Í¬¡£

_______ºÍʳÎïÔ´Ö®¼äѰÕÒ·¾¶ÐÐΪµÄÆô·¢¶øÐγɵġ£ Òϳ² 1.00/1.00

6Ìî¿Õ(1·Ö)Inspired by social behavior of birds and fishes,

Particle Swarm Optimization (PSO) algorithm uses a number of ________ that constitute a swarm moving around in the search space looking for the best solution.ÊÜÄñÀàºÍÓãÀàµÄÉç»áÐÐΪµÄÆô·¢£¬Á£×ÓȺÓÅ»¯Ëã·¨²ÉÓÃÈô¸É_______¹¹³ÉÒ»¸öÎ§ÈÆËÑË÷¿Õ¼äÒÆ¶¯µÄȺÌåÀ´Ñ°ÕÒ×îÓŽ⡣ Á£×Ó 1.00/1.00

7Ìî¿Õ(1·Ö)Local search algorithms operate using a single ______ (rather than multiple paths) and generally move only to neighbors of that node.¾Ö²¿ËÑË÷Ë㷨ʹÓÃÒ»¸ö______£¨¶ø²»ÊǶàÌõ·¾¶£©£¬²¢ÇÒͨ³£½öÒÆ¶¯µ½¸Ã½ÚµãÏàÁڵĽڵ㡣

µ±Ç°½Úµã 1.00/1.00

8Ìî¿Õ(1·Ö)In addition to finding goals, local search algorithms are useful for solving pure _________, in which the aim is to find the best state according to an objective function.³ýÁËѰÕÒÄ¿±êÖ®Í⣬¾Ö²¿ËÑË÷Ëã·¨¶Ô½â¾ö´¿_________Ò²ºÜÓÐЧ¡£ÆäÄ¿µÄÊǸù¾ÝÒ»¸öÄ¿±êº¯ÊýÕÒµ½Æä×îºÃµÄ״̬¡£ ÓÅ»¯ÎÊÌâ 1.00/1.00 Quizzes for Chapter 5

1µ¥Ñ¡(1·Ö)Which of the following is a true statement about games?ÒÔϹØÓÚ²©ÞĵijÂÊöÄĸöÊÇÕýÈ·µÄ£¿µÃ·Ö/×Ü·Ö

?

A.Local search problems are often known as

games.¾Ö²¿ËÑË÷ÎÊÌâͨ³£³ÆÎª²©ÞÄ

? B.Heuristic search problems are often known as

games.Æô·¢Ê½ËÑË÷ÎÊÌâͨ³£³ÆÎª²©ÞÄ

? C.Classical search problems are often known as

games.¾­µäËÑË÷ÎÊÌâͨ³£³ÆÎª²©ÞÄ

? D.Adversarial search problems are often known

as games.¶Ô¿¹ËÑË÷ͨ³£³ÆÎª²©ÞÄ1.00/1.00

2µ¥Ñ¡(1·Ö)___________ describes a situation in which the interacting agents' aggregate gains and losses can be less than or more than zero.___________ÖÐÖÇÄÜÌå½»»¥¶¯×÷µÄ×ÜÊÕÒæºÍËðʧ¿ÉÒÔСÓÚ»ò´óÓÚÁãµÃ·Ö/×Ü·Ö

? C.Alpha¨Cbeta pruning is to increase the number

of nodes that are evaluated by the minimax algorithm in its search tree.Alpha¨Cbeta¼ôÖ¦Ö¼ÔÚÔö¼ÓÆäËÑË÷Ê÷ÖÐÓÉminimaxËã·¨ÆÀ¼ÛµÄ½ÚµãÊýÁ¿¡£

? D.Alpha¨Cbeta pruning is to decrease the number

of nodes that are evaluated by the minimax algorithm in its search tree.Alpha¨Cbeta¼ôÖ¦Ö¼ÔÚ¼õÉÙÆäËÑË÷Ê÷ÖÐÓÉminimaxËã·¨ÆÀ¼ÛµÄ½ÚµãÊýÁ¿¡£0.50/1.00

? ? A.Zero sum gameÁãºÍ²©ÞÄ B. Computer game¼ÆËã»ú²©ÞÄ ? C.Two-player gameË«È˲©ÞÄ

?

D.Non-zero sum game·ÇÁãºÍ²©ÞÄ1.00/1.00

3¶àÑ¡(1·Ö)Select the following true statements regarding the concept of minimax rule for a zero sum game.´ÓÈçϹØÓÚÁãºÍ²©ÞÄmaximum¸ÅÄîÖÐÑ¡ÔñÕýÈ·µÄ´ð°¸¡£µÃ·Ö/×Ü·Ö

? A.Each player maximizes the maximum payoff

possible for itself.ÿ¸öÍæ¼Ò»áʹ×Ô¼º¿ÉÄܵÄ×î´óÊÕÒæ±äµÃ×î´ó¡£0.50/1.00

? B.Each player maximizes the maximum loss

possible for the other.ÿ¸öÍæ¼Ò»áʹ¶ÔÊÖ¿ÉÄܵÄ×î´óËðʧ±äµÃ×î´ó¡£0.50/1.00

? C.Each player minimizes the maximum payoff

possible for itself.ÿ¸öÍæ¼Ò»áʹ×Ô¼º¿ÉÄܵÄ×î´óÊÕÒæ±äµÃ×îС¡£

? D.Each player minimizes the maximum loss

possible for the other.ÿ¸öÍæ¼Ò»áʹ¶ÔÊÖ¿ÉÄܵÄ×î´óËðʧ±äµÃ×îС¡£

4¶àÑ¡(1·Ö)Which of the following statements are true about alpha-beta pruning?ÒÔϹØÓÚalpha¨Cbeta¼ôÖ¦µÄ³ÂÊöÄÄЩÊÇÕýÈ·µÄ£¿µÃ·Ö/×Ü·Ö

? A.Alpha¨Cbeta pruning is to add large parts that

are evaluated by the minimax algorithm in its search

tree.Alpha¨Cbeta¼ôÖ¦Ö¼ÔÚÌí¼ÓÆäËÑË÷Ê÷ÖÐÓÉminimaxËã·¨ÆÀ¼ÛµÄ´ó²¿·Ö¡£

? B.Alpha¨Cbeta pruning is to eliminate large parts

that are evaluated by the minimax algorithm in its search tree.Alpha¨Cbeta¼ôÖ¦Ö¼ÔÚÏû³ýÆäËÑË÷Ê÷ÖÐÓÉminimaxËã·¨ÆÀ¼ÛµÄ´ó²¿·Ö¡£0.50/1.00

5Ìî¿Õ(1·Ö)Claude Shannon proposed instead that programs should cut off the search earlier and apply a _______________ to states in the search, effectively turning nonterminal nodes into terminal leaves.¿ËÀ͵¡¤ÏãÅ©Ìá³ö£º³ÌÐòÓ¦¸ÃÔçһЩ¼ô¶ÏËÑË÷£¬²¢ÔÚËÑË÷ÖжÔ״̬ӦÓÃ________________£¬ÓÐЧµØ½«·ÇÖն˽ڵãת»»ÎªÖÕ¶ËÒ¶½Úµã¡££¨ÇëÌîдÖÐÎĴ𰸣© Æô·¢Ê½ÆÀ¹Àº¯Êý 0.00/1.00

6Ìî¿Õ(1·Ö)____________ is a dynamic game with probabilistic

transitions played by one or more players.____________ÊÇÒ»ÖÖ¾ßÓиÅÂÊת»»µÄ¶¯Ì¬²©ÞÄ£¬ÓÐÒ»¸ö»ò¶à¸öÍæ¼Ò¡££¨ÇëÌîдÖÐÎĴ𰸣©

Ëæ»ú²©ÞÄ 1.00/1.00

7Ìî¿Õ(1·Ö)Monte-Carlo methods are a broad class of computational algorithms that rely on ________________ to obtain numerical results.ÃÉÌØ¿¨ÂÞ·½·¨ÊÇÒ»´óÀà¼ÆËãËã·¨£¬Ëüƾ

½è________________À´»ñµÃÊýÖµ½á¹û¡££¨ÇëÌîдÖÐÎĴ𰸣© ÖØ¸´Ëæ»ú²ÉÑù 1.00/1.00

8Ìî¿Õ(1·Ö)___________ tree search is on the analysis of the most promising moves, expanding the search tree based on

random sampling of the search space.___________Ê÷ËÑË÷¶Ô×îÓÐÀûµÄ¶¯×÷½øÐзÖÎö£¬¸ù¾ÝËÑË÷¿Õ¼äµÄËæ»ú²ÉÑùÀ´À©Õ¹ËÑË÷Ê÷¡££¨ÇëÌîдÖÐÎĴ𰸣© ÃÉÌØ¿¨ÂÞ 1.00/1.00 Quizzes for Chapter 6

1µ¥Ñ¡(1·Ö)Select the following true one that is used to the state representation for constraint satisfaction problems (CSPs).´ÓÈç

ÏÂÓÃÓÚÔ¼ÊøÂú×ãÎÊÌâ (CSP)µÄ״̬±íʾÖÐÑ¡ÔñÕýÈ·µÄ´ð°¸¡£µÃ·Ö/×Ü·Ö

? A.atomicÔ­×Ó0.00/1.00 ? B.Molecular·Ö×Ó ?

C.Structure½á¹¹

? D.FactoredÒò×Ó

2µ¥Ñ¡(1·Ö)Assume that {A, B, C, D} are variables, the domain of each variable is {u, v, w}, and != denotes \which of the following expressions is a binary constraint on CSP formalism?{A, B, C, D}Ϊ±äÁ¿£¬Ã¿¸ö±äÁ¿µÄÓòÊÇ{u, v, w}£¬ÇÒ¡°!=¡±±íʾ²»µÈÓÚ£¬´ÓÈçϱí´ïʽÖÐÑ¡ÔñÄǸöÊÇCSPÐÎʽ»¯µÄ2ÔªÔ¼Êø£¿µÃ·Ö/×Ü·Ö

5¶àÑ¡(1·Ö)Select the following true statements regarding the concept of \´ÓÈçÏÂÓйء°»ØËÝËÑË÷¡±¸ÅÄîÖÐÑ¡ÔñÕýÈ·µÄ´ð°¸¡£µÃ·Ö/×Ü·Ö

? A.It incrementally builds candidates to the

solutions, and abandons each partial candidate c, as soon as it determines that c cannot possibly be completed to a valid solution.ÿ´ÎΪ±äÁ¿Ñ¡ÔñÖµ²¢ÇÒµ±±äÁ¿ÓÐÒ»¸ö»ò¶à¸öºÏ·¨¸³ÖµÊ±»ØËÝ¡£

? A.Alldiff(A, B, C, D)

? B.<(A), A = v>0.00/1.00 ? C.Diff(A, D) ?

D.A + B = C

3¶àÑ¡(1·Ö)Compare CSP and state-space search, and select correct statements from following ones. ±È½ÏCSPºÍ״̬¿Õ¼äËÑË÷£¬²¢´ÓÏÂÁÐÐðÊöÖÐÑ¡ÔñÕýÈ·µÄ´ð°¸¡£µÃ·Ö/×Ü·Ö

? A.CSP solving system can be slower than

state-space search solving system.CSPÇó½âϵͳ»á±È״̬¿Õ¼äËÑË÷Çó½âϵͳÂý¡£

? B.CSP solving system can be faster than

state-space search solving system.CSPÇó½âϵͳ»á±È״̬¿Õ¼äËÑË÷Çó½âϵͳ¿ì¡£0.50/1.00

? C.State-space search can quickly eliminate large

swatches of the search space.״̬¿Õ¼äËÑË÷¿ÉÒÔ¿ìËÙÅųý´óµÄËÑË÷¿Õ¼äÑù±¾¡£

? D.CSP can quickly eliminate large swatches of

the search space.CSP¿ÉÒÔ¿ìËÙÅųý´óµÄËÑË÷¿Õ¼äÑù±¾¡£0.50/1.00

4¶àÑ¡(1·Ö)Which of the following statements are true types of types of local consistency for constraint propagation?ÈçϳÂÊöÖÐÄÄЩÊÇÔ¼Êø´«²¥¾Ö²¿Ò»ÖÂÐÔµÄÕýÈ·ÀàÐÍ£¿µÃ·Ö/×Ü·Ö

? A.Path consistency·¾¶Ò»Ö¸ÃÌâÎÞ·¨µÃ·Ö/1.00 ? B.Loop consistency»·Â·Ò»ÖÂ

? C.Tree consistencyÊ÷Ò»Ö¸ÃÌâÎÞ·¨µÃ·Ö/1.00

?

D.Node consistency½ÚµãÒ»Ö¸ÃÌâÎÞ·¨µÃ·Ö/1.00

?

B.It incrementally builds candidates to the

solutions, and abandons each partial candidate c, as soon as it determines that c cannot possibly be completed to an invalid solution. µÝÔöµØ¹¹½¨½âµÄºòÑ¡£¬²¢ÇÒÒ»µ©È·¶¨²¿·ÖºòÑ¡c²»ÄܳÉΪºÏ·¨µÄ½â£¬¾Í½«cÅׯú¡£0.50/1.00

? C.It chooses values for one variable at a time and

backtracks when a variable has no legal values left to assign.ÿ´ÎΪ±äÁ¿Ñ¡ÔñÖµ²¢ÇÒµ±±äÁ¿Ã»ÓкϷ¨¸³ÖµÊ±»ØËÝ¡£0.50/1.00

? D.It chooses values for one variable at a time and

backtracks when a variable has one more legal value left to assign.µÝÔöµØ¹¹½¨½âµÄºòÑ¡£¬²¢ÇÒÒ»µ©È·¶¨²¿·ÖºòÑ¡c²»ÄܳÉΪ·Ç·¨µÄ½â£¬¾Í½«cÅׯú¡£

1µ¥Ñ¡(1·Ö)Select the following true one that is used to the state

representation for constraint satisfaction problems (CSPs).´ÓÈçÏÂÓÃÓÚÔ¼ÊøÂú×ãÎÊÌâ (CSP)µÄ״̬±íʾÖÐÑ¡ÔñÕýÈ·µÄ´ð°¸¡£

µÃ·Ö/×Ü·Ö

? A.Molecular·Ö×Ó ?

B. NetworkedÍøÂç ? C. atomicÔ­×Ó

?

D.FactoredÒò×Ó1.00/1.00

2µ¥Ñ¡(1·Ö)Assume that {A, B, C, D} are variables, the domain of each variable is {u, v, w}, and != denotes \which of the following expressions is a binary constraint on CSP formalism?Éè{A, B, C, D}Ϊ±äÁ¿£¬Ã¿¸ö±äÁ¿µÄÓòÊÇ{u, v, w}£¬ÇÒ¡°!=¡±

±íʾ²»µÈÓÚ£¬´ÓÈçϱí´ïʽÖÐÑ¡ÔñÄǸöÊÇCSPÐÎʽ»¯µÄ2ÔªÔ¼Êø£¿µÃ·Ö/×Ü·Ö

?

A.Diff(A, D)1.00/1.00 ?

B.A + B = C