[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fCak4_ySVGMupNQs4-xXmVeSSMiLT5hTIPb8SxFpsmlo":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":13,"related":14,"source":20,"type":21},[],"2024-11-25 08:37:17",999757239,[8,9],"对","错",{"courseImg":11,"courseName":12},"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fcf3bb414b5ea2367f316b2d3561124c7.jpg","[共享课]人工智能","在alpha-beta剪枝算法中,对于MAX节点,当它的效用值比当前的alpha低时可以进行剪枝.( )",[15,22,32,43,46,55,64,69,78,87],{"answer":16,"createTime":5,"id":17,"options":18,"question":19,"source":20,"type":21},[],999757131,[8,9],"广度优先搜索可以找到步数最短的搜索路径,并且能保证路径的代价最小.( )","v2",3,{"answer":23,"createTime":5,"id":24,"options":25,"question":30,"source":20,"type":31},[],999757169,[26,27,28,29],"\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F337926b18a7ceaabdfad5b2639b7f157.jpg\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F4380e14a56df3bb7de25cefb3358a2f9.jpg\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fc960c31c4270d294fcb0f674bb6fc0af.jpg\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5f869f433b69cf3430fc9bb56d268ccd.jpg\">","在强化学习值函数近似中,蒙特卡罗方法对参数的更新公式是( )",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":41,"source":20,"type":42},[],999757203,[36,37,38,39,40],"有限状态集合S","有限动作集合A","状态转移函数P","奖励函数R","衰减因子\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F942742265767fda821bd734bdc136d44.png\">","在无模型设定的强化学习中,马尔可夫决策过程的五元组已知部分有( )",1,{"answer":44,"createTime":5,"id":6,"options":45,"question":13,"source":20,"type":21},[],[8,9],{"answer":47,"createTime":5,"id":48,"options":49,"question":54,"source":20,"type":42},[],999757271,[50,51,52,53],"如果启发式函数是可采纳的,则A*树搜索是最优的","UCS是A*算法的一个特殊情况","如果启发式函数是一致的,则A*图搜索是最优的","UCS图搜索和树搜索都是最优的","以下关于启发式函数和A*算法的描述正确的是( )",{"answer":56,"createTime":5,"id":57,"options":58,"question":63,"source":20,"type":31},[],999757328,[59,60,61,62],"更新=步长*预测误差","更新=步长*预测误差+特征值","更新=步长*预测误差*特征值","更新=步长*特征值+预测误差","在有模型的强化学习中,属于动态规划求解的是( )",{"answer":65,"createTime":5,"id":66,"options":67,"question":68,"source":20,"type":21},[],999757348,[8,9],"蒙特卡洛方法是基于转移\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fd14c6ffa1ca1cd5b96b8bc3ad047453c.png\">进行评估的.( )",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":20,"type":42},[],999757392,[73,74,75,76],"预测是计算信念状态,使用前向算法","平滑是给定已知证据序列,计算过去某一状态的后验概率分布过程,使用前向后向算法","滤波是给定已知证据序列,计算未来状态的后验概率分布,通过前向算法和转移模型计算","最可能解释是找到最可能产生观测结果的状态序列,可以使用维特比算法","下列说法正确的有( )",{"answer":79,"createTime":5,"id":80,"options":81,"question":86,"source":20,"type":31},[],999757412,[82,83,84,85],"O(TX^2),O(TX)","O(TM^2),O(TM)","O(TMX),O(TX)","O(TMX),O(TM)","若转移矩阵是一个稀疏矩阵,且任何一个隐藏状态只能转移到M个可能的状态,使用维特比算法求最可能状态序列时可以忽略那些转移概率为0的路径,这时时间复杂度和空间复杂度为( )",{"answer":88,"createTime":5,"id":89,"options":90,"question":95,"source":20,"type":31},[],999757422,[91,92,93,94],"获胜状态的效用应该高于平局","可以通过特征线性组合的方式来计算","计算时间消耗应该尽量小","效用值的大小与赢得游戏的几率无关","估值函数不满足的特点是( )"]