[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fYN92NOPD7hZ0Qwb0Ci96Nvt4sTAQVMZ9CWQ6Arofyko":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":16,"related":17,"source":23,"type":24},[],"2023-05-07 18:50:38",12467277,[8,9],"对","错",{"courseId":11,"courseImg":12,"courseName":13,"workId":14,"workName":15},"1000009004","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Ff4e4dd841924a91dc656054650011ffd.jpeg","人工智能基础导学","7005794","人工智能基础导学教程考试","多隐层的人工神经网络具有优异的特征学习能力",[18,25,30,35,45,50,53,62,67,76],{"answer":19,"createTime":5,"id":20,"options":21,"question":22,"source":23,"type":24},[],12467251,[8,9],"模拟大脑的视觉处理过程就是卷积神经网络的思路","v2",3,{"answer":26,"createTime":5,"id":27,"options":28,"question":29,"source":23,"type":24},[],12467256,[8,9],"卷积神经网络模拟了人类视觉信息处理的过程",{"answer":31,"createTime":5,"id":32,"options":33,"question":34,"source":23,"type":24},[],12467261,[8,9],"对权重的训练直到某个权重对所有样本均不产生错误,或者错误不再降低",{"answer":36,"createTime":5,"id":37,"options":38,"question":43,"source":23,"type":44},[],12467266,[39,40,41,42],"贪婪搜索够在&quot;0-1背包问题&quot;中获得全局最优解","农夫过桥问题的状态图有明确的&quot;解状态&quot;,即全都过河","启发式搜索中h(n)的比重过大会导致问题找不到最优解","固定深度的博弈搜索根据人们在实际对弈中往往只向前考虑几步的情况提出","下列说法不正确的是()",0,{"answer":46,"createTime":5,"id":47,"options":48,"question":49,"source":23,"type":24},[],12467269,[8,9],"人工智能的第二次浪潮中最核心的是西蒙和纽厄尔推崇的自动定理证明方法",{"answer":51,"createTime":5,"id":6,"options":52,"question":16,"source":23,"type":24},[],[8,9],{"answer":54,"createTime":5,"id":55,"options":56,"question":61,"source":23,"type":44},[],12467284,[57,58,59,60],"极大极小策略适合棋局对弈游戏,能够在实际中很好的运用","A*算法中规定启发函数h(n)必须小于等于h*(n)","固定深度博弈通过设计启发函数来评估叶节点的得分","启发式搜索在统计语音识别、机器翻译问题中都得到应用","下列说法不正确的是( )",{"answer":63,"createTime":5,"id":64,"options":65,"question":66,"source":23,"type":24},[],12467285,[8,9],"&quot;人工神经网络&quot;用计算机模拟神经元及其连接,以实现自主识别、判断",{"answer":68,"createTime":5,"id":69,"options":70,"question":75,"source":23,"type":44},[],12467286,[71,72,73,74],"3","2","1","0","以下说法正确的个数有()a) 在八格游戏中使用宽度优先搜索,先搜索&quot;空位&quot;可能的移动状态b) 贪婪算法求得的结果可能是局部最优结果c) 通用搜索策略在搜索的过程中不对状态优劣进行判断",{"answer":77,"createTime":5,"id":78,"options":79,"question":84,"source":23,"type":44},[],12467290,[80,81,82,83],"启发函数的性能与启发知识的数量成正比","启发式搜索在生活中有很多应用,如语音识别等","博弈搜索考虑的是多个角色的最优路径选择问题","博弈搜索中,可以将最大化对方的得分转化为最小化我方得分","以下说法错误的是"]