[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fcarMbeh67ntBWVPRtma8hMxxMFaj8sR57faXwa8Z-6o":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":18,"related":19,"source":29,"type":30},[],"2024-05-09 08:48:39",986808867,[8,9,10,11],"给定两个状态,可能不存在两个状态之间的路径;也可能存在两个状态之间的路径,但不存在最短路径(如考虑存在负值的回路情况)","假设状态数量有限,当所有单步代价都相同且大于0时,深度优先的图搜索是最优的","假设状态数量有限,当所有单步代价都相同且大于0时,广度优先的图搜索是最优的","图搜索算法通常比树搜索算法的时间效率更高",{"courseId":13,"courseImg":14,"courseName":15,"workId":16,"workName":17},"1000076607","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fd7ea7086329261ddbccb0e9e00c955b1.jpg","[智慧共享课]人工智能引论","57250193","第三章单元测试","以下关于用搜索算法求解最短路径问题的说法中,不正确的是( )",[20,31,40,49,58],{"answer":21,"createTime":5,"id":22,"options":23,"question":28,"source":29,"type":30},[],986808669,[24,25,26,27],"选择过程体现了探索与利用的平衡","算法进入扩展步骤时,当前节点的所有子节点必然都未被扩展","模拟步骤采取的策略与选择步骤不一定要相同","反向传播只需要更新当前路径上已被扩展的节点","下列关于蒙特卡洛树搜索算法的说法中,不正确的是( )","v2",0,{"answer":32,"createTime":5,"id":33,"options":34,"question":39,"source":29,"type":30},[],986808733,[35,36,37,38],"在多臂赌博机问题中,过度探索会导致算法很少主动去选择比较好的摇臂","在多臂赌博机问题中,过度利用可能导致算法对部分臂膀额奖励期望估计不准确","在\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5e543256c480ac577d30f76f9120eb74.webp\">贪心算法中,\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5e543256c480ac577d30f76f9120eb74.webp\">的值越大,表示算法越倾向于探索","在多臂赌博机问题中,某时刻UCB1算法选择的臂膀置信上界为\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5e543256c480ac577d30f76f9120eb74.webp\">,则此时任意摇动一个臂膀,得到的硬币数量不会超过\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5e543256c480ac577d30f76f9120eb74.webp\">","下列关于探索与利用的说法中,不正确的是( )",{"answer":41,"createTime":5,"id":42,"options":43,"question":48,"source":29,"type":30},[],986808775,[44,45,46,47],"由双方轮流落子,改为黑方连落两子后白方落一子","双方互相不知道对方落子的位置","由两人对弈改为三人对弈","终局时黑方所占的每目(即每个交叉点)计1分,且事先给定了白方在棋盘上每个位置取得一目所获取的分数,假设这些分数各不相同.双方都以取得最高得分为目标","假如可以对围棋的规则做出如下修改,其中哪个修改方案不影响使用本章介绍的Minimax算法求解该问题?( )",{"answer":50,"createTime":5,"id":51,"options":52,"question":57,"source":29,"type":30},[],986808821,[53,54,55,56],"启发函数不会过高估计从当前节点到目标结点之间的实际代价","取值恒为0的启发函数必然是可容的","评价函数通常是对当前节点到目标节点距离的估计","如果启发函数满足可容性,那么在树搜索A*算法中节点的评价函数值按照扩展顺序单调非减;启发函数满足一致性时图搜索A*算法也满足该性质","以下关于启发函数和评价函数的说法中正确的是( )",{"answer":59,"createTime":5,"id":6,"options":60,"question":18,"source":29,"type":30},[],[8,9,10,11]]