. . . "\u0623\u0641\u0636\u0644-\u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644 \u0647\u0648 \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0628\u062D\u062B \u062A\u0633\u062A\u0643\u0634\u0641 \u0645\u062C\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064A\u0627\u0646\u0627\u062A (Graph) \u0645\u0646 \u062E\u0644\u0627\u0644 \u062A\u0648\u0633\u064A\u0639 \u0646\u0637\u0627\u0642 \u0627\u0644\u0639\u0642\u062F\u0629 \u0627\u0644\u0623\u0643\u062B\u0631 \u0646\u062C\u0627\u062D\u0627\u064B \u0623\u064A \u0627\u0644\u0623\u0643\u062B\u0631 \u0645\u0637\u0627\u0628\u0642\u0629 \u0644\u0642\u0627\u0639\u062F\u0629 \u0645\u062D\u062F\u062F\u0629 \u064A\u062A\u0645 \u0627\u0644\u0628\u062D\u062B \u0628\u0648\u0627\u0633\u0637\u062A\u0647\u0627. \u064A\u0647\u0648\u062F\u0627 \u0628\u064A\u0631\u0644 \u0648\u0635\u0641 \u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644 \u0627\u0644\u0623\u0641\u0636\u0644 \u0645\u0646 \u062E\u0644\u0627\u0644 \u062A\u0642\u062F\u064A\u0631 \u062A\u0642\u062F\u064A\u0631 \u0646\u062C\u0627\u062D \u062A\u0644\u0643 \u0627\u0644\u0639\u0642\u062F\u0629 n \u00AB\u0648\u0630\u0644\u0643 \u0648\u0641\u0642\u0627\u064B \u0644\u062F\u0627\u0644\u0629 \u062A\u0642\u064A\u064A\u0645 \u0625\u0631\u0634\u0627\u062F\u064A\u0629 (heuristic evaluation function) \u0648\u0627\u0644\u062A\u064A \u0642\u062F \u062A\u0639\u062A\u0645\u062F \u0628\u0634\u0643\u0644 \u0639\u0627\u0645 \u0639\u0644\u0649 \u0643\u0644 \u0645\u0646: \u0648\u0635\u0641 n\u060C \u0648\u0635\u0641 \u0627\u0644\u0647\u062F\u0641\u060C \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062A \u0627\u0644\u062A\u064A \u062A\u0645 \u062C\u0645\u0639\u0647\u0627 \u0628\u0648\u0627\u0633\u0637\u0629 \u0627\u0644\u0628\u062D\u062B \u0639\u0646 \u062A\u0644\u0643 \u0627\u0644\u0646\u0642\u0637\u0629\u060C \u0648\u0627\u0644\u0623\u0647\u0645 \u0645\u0646 \u0630\u0644\u0643 \u0643\u0644\u0647\u060C \u0623\u064A \u0645\u0639\u0644\u0648\u0645\u0627\u062A \u0625\u0636\u0627\u0641\u064A\u0629 \u062D\u0648\u0644 \u0646\u0637\u0627\u0642 \u0627\u0644\u0645\u0639\u0636\u0644\u0629 (problem domain)\u00BB \u0627\u0633\u062A\u062E\u062F\u0645 \u0628\u0639\u0636 \u0627\u0644\u0643\u062A\u0627\u0628 \u00AB\u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644-\u0627\u0644\u0623\u0641\u0636\u0644\u00BB \u0644\u0644\u0625\u0634\u0627\u0631\u0629 \u0639\u0644\u0649 \u0648\u062C\u0647 \u0627\u0644\u062A\u062D\u062F\u064A\u062F \u0625\u0644\u0649 \u0627\u0644\u0628\u062D\u062B \u0628\u0648\u0627\u0633\u0637\u0629 \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0627\u0644\u0643\u0634\u0641 \u0639\u0646 \u0645\u062C\u0631\u064A\u0627\u062A \u0627\u0644\u0623\u0645\u0648\u0631 (heuristic) \u0627\u0644\u062A\u064A \u062A\u062D\u0627\u0648\u0644 \u0627\u0644\u062A\u0646\u0628\u0624 \u0628\u0645\u062F\u0649 \u0642\u0631\u0628 \u0646\u0647\u0627\u064A\u0629 \u0627\u0644\u0637\u0631\u064A\u0642 \u0645\u0646 \u0627\u0644\u062D\u0644\u060C \u0628\u062D\u064A\u062B \u062A\u0648\u0633\u0651\u0639 \u0627\u0644\u0645\u0633\u0627\u0631\u0627\u062A \u0627\u0644\u062A\u064A \u062A\u0639\u062A\u0628\u0631 \u0623\u0642\u0631\u0628 \u0625\u0644\u0649 \u0627\u0644\u062D\u0644 \u0644\u0644\u0648\u0635\u0648\u0644 \u0625\u0644\u0649 \u0627\u0644\u062D\u0644 \u0628\u0634\u0643\u0644 \u0623\u0633\u0631\u0639. \u0648\u064A\u0633\u0645\u0649 \u0647\u0630\u0627 \u0627\u0644\u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0628\u062D\u062B \u0628\u0640 \u0627\u0644\u0623\u0648\u0644-\u0627\u0644\u0623\u0641\u0636\u0644 \u0627\u0644\u062C\u0634\u0639 \u0623\u0648 \u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0625\u0631\u0634\u0627\u062F\u064A \u0627\u0644\u0646\u0642\u064A (pure heuristic search) - \u0628\u062D\u062B \u0627\u0644\u0643\u0634\u0641 \u0627\u0644\u0646\u0642\u064A \u0639\u0646 \u0645\u062C\u0631\u064A\u0627\u062A \u0627\u0644\u0623\u0645\u0648\u0631. \u0639\u0627\u062F\u0629\u064B \u0645\u0627 \u064A\u062A\u0645 \u062A\u0646\u0641\u064A\u0630 \u0643\u0641\u0627\u0621\u0629 \u0627\u062E\u062A\u064A\u0627\u0631 \u0623\u0641\u0636\u0644 \u0645\u0631\u0634\u062D \u0644\u064A\u062A\u0645 \u062A\u0648\u0633\u064A\u0639\u0647 \u0628\u0627\u0633\u062A\u062E\u062F\u0627\u0645 \u0637\u0627\u0628\u0648\u0631 \u0627\u0644\u0623\u0648\u0644\u0648\u064A\u0629 (priority queue). \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0627\u0644\u0628\u062D\u062B \u0628\u0623\u0648\u0644\u0648\u064A\u0629 \u0627\u0644\u0623\u0641\u0636\u0644 \u0647\u064A \u0645\u062B\u0627\u0644 \u0639\u0644\u0649 \u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644-\u0627\u0644\u0623\u0641\u0636\u0644 (\u062A\u0639\u0631\u0641 \u0628\u0640 A* \u0628\u0627\u0644\u0625\u0646\u062C\u0644\u064A\u0632\u064A\u0629)\u060C \u0623\u0645\u0627 \u0627\u0644\u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0627\u0644\u0645\u0639\u0631\u0648\u0641\u0629 \u0628\u0640 (B*) \u0641\u0647\u064A \u062A\u0633\u062A\u062E\u062F\u0645 \u063A\u0627\u0644\u0628\u0627\u064B \u0644\u0625\u064A\u062C\u0627\u062F \u0627\u0644\u0645\u0633\u0627\u0631\u0627\u062A \u0641\u064A (combinatorial search). \u0648\u0644\u0627 \u062A\u0639\u062A\u0628\u0631 \u0623\u064A \u0645\u0646 \u0623* \u0623\u0648 \u0628* \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0628\u062D\u062B \u0623\u0648\u0644-\u0623\u0641\u0636\u0644 \u062C\u0634\u0639 \u062D\u064A\u062B \u0623\u0646 \u0643\u0644\u0627\u0647\u0645\u0627 \u0644\u0627 \u062A\u062A\u0646\u0627\u063A\u0645\u0627\u0646 \u0645\u0639 \u0627\u0644\u0645\u0633\u0627\u0641\u0629 \u0627\u0644\u0645\u0642\u0637\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0628\u062F\u0627\u064A\u0629 \u0645\u062C\u0645\u0648\u0639\u0629 \u0645\u0639 \u0627\u0644\u0645\u0633\u0627\u0641\u0627\u062A \u0627\u0644\u0645\u0642\u062F\u0631\u0629 \u0646\u062D\u0648 \u0627\u0644\u0647\u062F\u0641 \u0648\u0647\u064A \u0635\u0641\u0629 \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644-\u0627\u0644\u0623\u0641\u0636\u0644 \u0627\u0644\u062C\u0634\u0639."@ar . . . . . . . "\uCD5C\uC0C1 \uC6B0\uC120 \uD0D0\uC0C9"@ko . . . "Best-first search is a class of search algorithms, which explore a graph by expanding the most promising node chosen according to a specified rule. Judea Pearl described the best-first search as estimating the promise of node n by a \"heuristic evaluation function which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to that point, and most importantly, on any extra knowledge about the problem domain.\" Efficient selection of the current best candidate for extension is typically implemented using a priority queue."@en . "\u6700\u826F\u512A\u5148\u63A2\u7D22\uFF08\u3055\u3044\u308A\u3087\u3046\u3086\u3046\u305B\u3093\u305F\u3093\u3055\u304F\u3001\u82F1: best-first search\uFF09\u306F\u3001\u5E45\u512A\u5148\u63A2\u7D22\uFF08\u82F1: breadth-first search\uFF09\u3092\u4F55\u3089\u304B\u306E\u898F\u5247\uFF08\u8A55\u4FA1\u95A2\u6570\uFF09\u306B\u5F93\u3063\u3066\u6B21\u306B\u63A2\u7D22\u3059\u308B\u6700\u3082\u671B\u307E\u3057\u3044\u30CE\u30FC\u30C9\u3092\u9078\u629E\u3059\u308B\u3088\u3046\u306B\u62E1\u5F35\u3057\u305F\u63A2\u7D22\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u3067\u3042\u308B\u3002 \u63A2\u7D22\u30CE\u30FC\u30C9\u3092\u52B9\u7387\u7684\u306B\u9078\u629E\u3059\u308B\u306B\u306F\u512A\u5148\u5EA6\u4ED8\u304D\u30AD\u30E5\u30FC\uFF08\u82F1: priority queue\uFF09\u3092\u7528\u3044\u3066\u5B9F\u88C5\u3059\u308B\u306E\u304C\u4E00\u822C\u7684\u3067\u3042\u308B\u3002\u30AD\u30E5\u30FC\u306B\u8CAF\u3081\u305A\u306B\u6700\u826F\u306E\u30CE\u30FC\u30C9\u3060\u3051\u3092\u6271\u3046\u3068\u5C71\u767B\u308A\u6CD5\u306B\u306A\u308B\u3002\u30AD\u30E5\u30FC\u3092\u8A55\u4FA1\u95A2\u6570\u3067\u30BD\u30FC\u30C8\u3057\u306A\u3044\u3068\u5E45\u512A\u5148\u63A2\u7D22\u306B\u306A\u308B\u3002 \u6700\u826F\u512A\u5148\u63A2\u7D22\u306E\u4F8B\u3068\u3057\u3066\u306F\u30C0\u30A4\u30AF\u30B9\u30C8\u30E9\u6CD5\uFF08\u82F1: Dijkstra's algorithm\uFF09\u3084A*\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\uFF08\u82F1: A* search algorithm\uFF09\u3084\u5747\u4E00\u30B3\u30B9\u30C8\u63A2\u7D22\u3092\u6319\u3052\u308B\u3053\u3068\u304C\u3067\u304D\u308B\u3002\u6700\u826F\u512A\u5148\u63A2\u7D22\u306F\u7D4C\u8DEF\u63A2\u7D22\u306B\u304A\u3044\u3066\u3057\u3070\u3057\u3070\u4F7F\u308F\u308C\u308B\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u3067\u3042\u308B\u3002\u30B3\u30F3\u30D4\u30E5\u30FC\u30BF\u5C06\u68CB\u30FB\u30B3\u30F3\u30D4\u30E5\u30FC\u30BF\u30C1\u30A7\u30B9\u306A\u3069\u3067\u3082\u6700\u826F\u512A\u5148\u63A2\u7D22\u3092\u62E1\u5F35\u3057\u305F\u7269\u304C\u4F7F\u308F\u308C\u3066\u3044\u308B\u3002"@ja . "Bestensuche"@de . . . . "Uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED (anglicky best-first search) je jeden z algoritm\u016F na prohled\u00E1v\u00E1n\u00ED stavov\u00E9ho prostoru. Na rozd\u00EDl od prohled\u00E1v\u00E1n\u00ED do hloubky, kter\u00E9 v\u017Edy pokra\u010Duje v hled\u00E1n\u00ED v nejvzd\u00E1len\u011Bj\u0161\u00EDm dostupn\u00E9m stavu, a prohled\u00E1v\u00E1n\u00ED do \u0161\u00ED\u0159ky, kter\u00E9 pokra\u010Duje naopak z jednoho k po\u010D\u00E1tku nejbli\u017E\u0161\u00EDch stav\u016F, funguje uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED tak, \u017Ee si z vrchol\u016F k prohled\u00E1v\u00E1n\u00ED vybere ten z hlediska hledan\u00E9ho stavu nejslibn\u011Bj\u0161\u00ED. P\u0159irozen\u00E1 implementace uspo\u0159\u00E1dan\u00E9ho prohled\u00E1v\u00E1n\u00ED pou\u017Eije k ukl\u00E1d\u00E1n\u00ED prioritn\u00ED frontu, zat\u00EDmco prohled\u00E1v\u00E1n\u00ED do \u0161\u00ED\u0159ky pou\u017E\u00EDv\u00E1 oby\u010Dejnou frontu a prohled\u00E1v\u00E1n\u00ED do hloubky z\u00E1sobn\u00EDk (obvykle se jedn\u00E1 v r\u00E1mci rekurze o z\u00E1sobn\u00EDk vol\u00E1n\u00ED). Na rozd\u00EDl od dvou ostatn\u00EDch zm\u00EDn\u011Bn\u00FDch algoritm\u016F je uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED algoritmem informovan\u00FDm, nebo\u0165 se p\u0159i uspo\u0159\u00E1d\u00E1v\u00E1n\u00ED vrchol\u016F op\u00EDr\u00E1 vstupn\u00ED data. Ty mohou obsahovat zn\u00E1mou cenu vrchol\u016F nebo hran nebo heuristiku, kterou se odhaduje jejich slibnost. Zejm\u00E9na u \u010Dist\u011B lok\u00E1ln\u00ED heuristiky se t\u00EDm tak\u00E9 jedn\u00E1 o algoritmus hladov\u00FD. Obdobnou my\u0161lenku m\u00E1 nebo rozv\u00EDj\u00ED \u0159ada specializovan\u011Bj\u0161\u00EDch vyhled\u00E1vac\u00EDch algoritm\u016F, nap\u0159\u00EDklad Dijkstr\u016Fv algoritmus, A*, nebo paprskov\u00E9 prohled\u00E1v\u00E1n\u00ED. Naopak na samotn\u00FD algoritmus uspo\u0159\u00E1dan\u00E9ho prohled\u00E1v\u00E1n\u00ED se lze d\u00EDvat jako na roz\u0161\u00ED\u0159en\u00ED gradientn\u00EDho prohled\u00E1v\u00E1n\u00ED o pam\u011B\u0165."@cs . "Uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED"@cs . . . . . "Uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED (anglicky best-first search) je jeden z algoritm\u016F na prohled\u00E1v\u00E1n\u00ED stavov\u00E9ho prostoru. Na rozd\u00EDl od prohled\u00E1v\u00E1n\u00ED do hloubky, kter\u00E9 v\u017Edy pokra\u010Duje v hled\u00E1n\u00ED v nejvzd\u00E1len\u011Bj\u0161\u00EDm dostupn\u00E9m stavu, a prohled\u00E1v\u00E1n\u00ED do \u0161\u00ED\u0159ky, kter\u00E9 pokra\u010Duje naopak z jednoho k po\u010D\u00E1tku nejbli\u017E\u0161\u00EDch stav\u016F, funguje uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED tak, \u017Ee si z vrchol\u016F k prohled\u00E1v\u00E1n\u00ED vybere ten z hlediska hledan\u00E9ho stavu nejslibn\u011Bj\u0161\u00ED. P\u0159irozen\u00E1 implementace uspo\u0159\u00E1dan\u00E9ho prohled\u00E1v\u00E1n\u00ED pou\u017Eije k ukl\u00E1d\u00E1n\u00ED prioritn\u00ED frontu, zat\u00EDmco prohled\u00E1v\u00E1n\u00ED do \u0161\u00ED\u0159ky pou\u017E\u00EDv\u00E1 oby\u010Dejnou frontu a prohled\u00E1v\u00E1n\u00ED do hloubky z\u00E1sobn\u00EDk (obvykle se jedn\u00E1 v r\u00E1mci rekurze o z\u00E1sobn\u00EDk vol\u00E1n\u00ED). Na rozd\u00EDl od dvou ostatn\u00EDch zm\u00EDn\u011Bn\u00FDch algoritm\u016F je uspo\u0159\u00E1dan\u00E9 prohled\u00E1v\u00E1n\u00ED algoritmem informovan\u00FDm, nebo\u0165 se p\u0159i uspo\u0159\u00E1d\u00E1v\u00E1n\u00ED vrchol\u016F op\u00EDr\u00E1 vstupn\u00ED d"@cs . "3773"^^ . . . . "Algorithme de recherche best-first"@fr . . "\u041F\u043E\u0301\u0448\u0443\u043A \u00AB\u041D\u0430\u0439\u043A\u0440\u0430\u0301\u0449\u0438\u0439 \u2014 \u043F\u0435\u0301\u0440\u0448\u0438\u0439\u00BB \u2014 \u0446\u0435 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0448\u0443\u043A\u0443, \u044F\u043A\u0438\u0439 \u0434\u043E\u0441\u043B\u0456\u0434\u0436\u0443\u0454 \u0433\u0440\u0430\u0444 \u0448\u043B\u044F\u0445\u043E\u043C \u0440\u043E\u0437\u0448\u0438\u0440\u0435\u043D\u043D\u044F \u043D\u0430\u0439\u043F\u0435\u0440\u0441\u043F\u0435\u043A\u0442\u0438\u0432\u043D\u0456\u0448\u0438\u0445 \u0432\u0443\u0437\u043B\u0456\u0432, \u044F\u043A\u0456 \u043E\u0431\u0438\u0440\u0430\u044E\u0442\u044C\u0441\u044F \u0432\u0456\u0434\u043F\u043E\u0432\u0456\u0434\u043D\u043E \u0434\u043E \u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043E\u0433\u043E \u043F\u0440\u0430\u0432\u0438\u043B\u0430."@uk . "148271"^^ . "\u041F\u043E\u0438\u0441\u043A \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB (\u0430\u043D\u0433\u043B. best-first search) \u2014 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0438\u0441\u043A\u0430, \u0438\u0441\u0441\u043B\u0435\u0434\u0443\u044E\u0449\u0438\u0439 \u0433\u0440\u0430\u0444 \u043F\u0443\u0442\u0451\u043C \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043D\u0438\u044F \u043D\u0430\u0438\u0431\u043E\u043B\u0435\u0435 \u043F\u0435\u0440\u0441\u043F\u0435\u043A\u0442\u0438\u0432\u043D\u044B\u0445 \u0443\u0437\u043B\u043E\u0432, \u0432\u044B\u0431\u0438\u0440\u0430\u0435\u043C\u044B\u0445 \u0432 \u0441\u043E\u043E\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0438 \u0441 \u0443\u043A\u0430\u0437\u0430\u043D\u043D\u044B\u043C \u043F\u0440\u0430\u0432\u0438\u043B\u043E\u043C. \u0414\u0436\u0443\u0434\u0430 \u041F\u0435\u0440\u043B (\u0430\u043D\u0433\u043B. Judea Pearl) \u043E\u043F\u0438\u0441\u0430\u043B \u043F\u043E\u0438\u0441\u043A \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB, \u0432\u0437\u044F\u0432 \u0432 \u043A\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043E\u0446\u0435\u043D\u043A\u0438 \u0443\u0437\u043B\u0430 \u0437\u043D\u0430\u0447\u0435\u043D\u0438\u0435 \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0439 \u00AB\u044D\u0432\u0440\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u0444\u0443\u043D\u043A\u0446\u0438\u0438 \u043E\u0446\u0435\u043D\u043A\u0438 , \u043A\u043E\u0442\u043E\u0440\u0430\u044F, \u0432\u043E\u043E\u0431\u0449\u0435 \u0433\u043E\u0432\u043E\u0440\u044F, \u043C\u043E\u0436\u0435\u0442 \u0437\u0430\u0432\u0438\u0441\u0435\u0442\u044C \u043E\u0442 \u043F\u0440\u0438\u0440\u043E\u0434\u044B , \u043E\u043F\u0438\u0441\u0430\u043D\u0438\u044F \u0446\u0435\u043B\u0438, \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u0438 \u0441\u043E\u0431\u0440\u0430\u043D\u043D\u043E\u0439 \u043F\u043E\u0438\u0441\u043A\u043E\u043C \u043D\u0430 \u0434\u0430\u043D\u043D\u044B\u0439 \u043C\u043E\u043C\u0435\u043D\u0442 \u0438, \u0441\u0430\u043C\u043E\u0435 \u0433\u043B\u0430\u0432\u043D\u043E\u0435, \u043E\u0442 \u043A\u0430\u043A\u0438\u0445-\u043B\u0438\u0431\u043E \u0434\u043E\u043F\u043E\u043B\u043D\u0438\u0442\u0435\u043B\u044C\u043D\u044B\u0445 \u0437\u043D\u0430\u043D\u0438\u0439 \u043E \u043F\u0440\u0435\u0434\u043C\u0435\u0442\u043D\u043E\u0439 \u043E\u0431\u043B\u0430\u0441\u0442\u0438\u00BB. \u041D\u0435\u043A\u043E\u0442\u043E\u0440\u044B\u0435 \u0430\u0432\u0442\u043E\u0440\u044B \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u043E\u0432\u0430\u043B\u0438 \u043F\u043E\u0438\u0441\u043A \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB \u0441\u043F\u0435\u0446\u0438\u0430\u043B\u044C\u043D\u043E \u0434\u043B\u044F \u043E\u043F\u0438\u0441\u0430\u043D\u0438\u044F \u043F\u043E\u0438\u0441\u043A\u0430 \u0441 \u044D\u0432\u0440\u0438\u0441\u0442\u0438\u043A\u043E\u0439, \u0441\u043B\u0443\u0436\u0430\u0449\u0435\u0439 \u043C\u0435\u0440\u043E\u0439 \u0431\u043B\u0438\u0437\u043E\u0441\u0442\u0438 \u043A \u0446\u0435\u043B\u0435\u0432\u043E\u043C\u0443 \u0441\u043E\u0441\u0442\u043E\u044F\u043D\u0438\u044E, \u043F\u043E\u044D\u0442\u043E\u043C\u0443 \u043F\u0443\u0442\u0438 \u0441 \u043B\u0443\u0447\u0448\u0435\u0439 \u044D\u0432\u0440\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u043E\u0446\u0435\u043D\u043A\u043E\u0439 \u0440\u0430\u0441\u0441\u043C\u0430\u0442\u0440\u0438\u0432\u0430\u044E\u0442\u0441\u044F \u043F\u0435\u0440\u0432\u044B\u043C\u0438. \u042D\u0442\u043E\u0442 \u0441\u043F\u0435\u0446\u0438\u0444\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0442\u0438\u043F \u043F\u043E\u0438\u0441\u043A\u0430 \u043D\u0430\u0437\u044B\u0432\u0430\u0435\u0442\u0441\u044F \u0436\u0430\u0434\u043D\u044B\u043C \u043F\u043E\u0438\u0441\u043A\u043E\u043C \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB. \u042D\u0444\u0444\u0435\u043A\u0442\u0438\u0432\u043D\u044B\u0439 \u0432\u044B\u0431\u043E\u0440 \u0442\u0435\u043A\u0443\u0449\u0435\u0433\u043E \u043B\u0443\u0447\u0448\u0435\u0433\u043E \u043A\u0430\u043D\u0434\u0438\u0434\u0430\u0442\u0430 \u0434\u043B\u044F \u043F\u0440\u043E\u0434\u043E\u043B\u0436\u0435\u043D\u0438\u044F \u043F\u043E\u0438\u0441\u043A\u0430 \u043C\u043E\u0436\u0435\u0442 \u0431\u044B\u0442\u044C \u0440\u0435\u0430\u043B\u0438\u0437\u043E\u0432\u0430\u043D \u0441 \u043F\u043E\u043C\u043E\u0449\u044C\u044E \u043E\u0447\u0435\u0440\u0435\u0434\u0438 \u0441 \u043F\u0440\u0438\u043E\u0440\u0438\u0442\u0435\u0442\u043E\u043C. \u0410\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0438\u0441\u043A\u0430 A* (\u0410-\u0437\u0432\u0435\u0437\u0434\u043E\u0447\u043A\u0430) \u044F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u043F\u0440\u0438\u043C\u0435\u0440\u043E\u043C \u043E\u043F\u0442\u0438\u043C\u0430\u043B\u044C\u043D\u043E\u0433\u043E \u043F\u043E\u0438\u0441\u043A\u0430 \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB. \u0410\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB \u0447\u0430\u0441\u0442\u043E \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u044E\u0442\u0441\u044F \u0434\u043B\u044F \u043F\u043E\u0438\u0441\u043A\u0430 \u043F\u0443\u0442\u0438 \u0432 \u043A\u043E\u043C\u0431\u0438\u043D\u0430\u0442\u043E\u0440\u043D\u043E\u043C \u043F\u043E\u0438\u0441\u043A\u0435."@ru . "O algoritmo de busca melhor-primeiro ou \"best-first\" usa a fun\u00E7\u00E3o heur\u00EDstica F(n)=h(n) de procura ao n\u00F3 de destino. Esta procura expandir o n\u00F3 que \u00E9 mais pr\u00F3ximo ao objetivo, implicando numa condu\u00E7\u00E3o r\u00E1pida at\u00E9 o n\u00F3 destino. A heur\u00EDstica \u00E9 aplicada globalmente, isto \u00E9, o caminho a ser seguido \u00E9 selecionado entre todos os n\u00F3s abertos at\u00E9 o momento, o n\u00F3 aberto com a melhor nota \u00E9 escolhido para a expans\u00E3o."@pt . . . . "Best-first search (letteralmente \"ricerca prima il migliore\") \u00E8 una strategia di utilizzata per la risoluzione di problemi basati sulla ricerca ed \u00E8 alla base dei moderni algoritmi di intelligenza artificiale. Rispetto alle strategie di , di cui si fa uso nel caso in cui non si hanno informazioni specifiche sugli stati del problema oltre la definizione del problema, la ricerca best-first, come tutte le altre strategie di ricerca informata, sfrutta la conoscenza di ulteriori dettagli sugli stati del problema da risolvere."@it . "\u6700\u826F\u512A\u5148\u63A2\u7D22\uFF08\u3055\u3044\u308A\u3087\u3046\u3086\u3046\u305B\u3093\u305F\u3093\u3055\u304F\u3001\u82F1: best-first search\uFF09\u306F\u3001\u5E45\u512A\u5148\u63A2\u7D22\uFF08\u82F1: breadth-first search\uFF09\u3092\u4F55\u3089\u304B\u306E\u898F\u5247\uFF08\u8A55\u4FA1\u95A2\u6570\uFF09\u306B\u5F93\u3063\u3066\u6B21\u306B\u63A2\u7D22\u3059\u308B\u6700\u3082\u671B\u307E\u3057\u3044\u30CE\u30FC\u30C9\u3092\u9078\u629E\u3059\u308B\u3088\u3046\u306B\u62E1\u5F35\u3057\u305F\u63A2\u7D22\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u3067\u3042\u308B\u3002 \u63A2\u7D22\u30CE\u30FC\u30C9\u3092\u52B9\u7387\u7684\u306B\u9078\u629E\u3059\u308B\u306B\u306F\u512A\u5148\u5EA6\u4ED8\u304D\u30AD\u30E5\u30FC\uFF08\u82F1: priority queue\uFF09\u3092\u7528\u3044\u3066\u5B9F\u88C5\u3059\u308B\u306E\u304C\u4E00\u822C\u7684\u3067\u3042\u308B\u3002\u30AD\u30E5\u30FC\u306B\u8CAF\u3081\u305A\u306B\u6700\u826F\u306E\u30CE\u30FC\u30C9\u3060\u3051\u3092\u6271\u3046\u3068\u5C71\u767B\u308A\u6CD5\u306B\u306A\u308B\u3002\u30AD\u30E5\u30FC\u3092\u8A55\u4FA1\u95A2\u6570\u3067\u30BD\u30FC\u30C8\u3057\u306A\u3044\u3068\u5E45\u512A\u5148\u63A2\u7D22\u306B\u306A\u308B\u3002 \u6700\u826F\u512A\u5148\u63A2\u7D22\u306E\u4F8B\u3068\u3057\u3066\u306F\u30C0\u30A4\u30AF\u30B9\u30C8\u30E9\u6CD5\uFF08\u82F1: Dijkstra's algorithm\uFF09\u3084A*\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\uFF08\u82F1: A* search algorithm\uFF09\u3084\u5747\u4E00\u30B3\u30B9\u30C8\u63A2\u7D22\u3092\u6319\u3052\u308B\u3053\u3068\u304C\u3067\u304D\u308B\u3002\u6700\u826F\u512A\u5148\u63A2\u7D22\u306F\u7D4C\u8DEF\u63A2\u7D22\u306B\u304A\u3044\u3066\u3057\u3070\u3057\u3070\u4F7F\u308F\u308C\u308B\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u3067\u3042\u308B\u3002\u30B3\u30F3\u30D4\u30E5\u30FC\u30BF\u5C06\u68CB\u30FB\u30B3\u30F3\u30D4\u30E5\u30FC\u30BF\u30C1\u30A7\u30B9\u306A\u3069\u3067\u3082\u6700\u826F\u512A\u5148\u63A2\u7D22\u3092\u62E1\u5F35\u3057\u305F\u7269\u304C\u4F7F\u308F\u308C\u3066\u3044\u308B\u3002"@ja . "La recherche best-first (litt\u00E9ralement : le meilleur en premier) est un algorithme de recherche qui parcourt un graphe en explorant le n\u0153ud le plus \"prometteur\" selon une r\u00E8gle sp\u00E9cifique. Judea Pearl d\u00E9crit la recherche best-first comme l'estimation de la qualit\u00E9 d'un n\u0153ud n par une \"fonction heuristique d'\u00E9valuation qui, en g\u00E9n\u00E9ral, peut d\u00E9pendre de la description de n, de l'\u00E9tat d'arriv\u00E9e, des informations amass\u00E9es par l'algorithme au moment de l'\u00E9valuation et, surtout, de connaissances suppl\u00E9mentaires \u00E0 propos du probl\u00E8me\"."@fr . "La recherche best-first (litt\u00E9ralement : le meilleur en premier) est un algorithme de recherche qui parcourt un graphe en explorant le n\u0153ud le plus \"prometteur\" selon une r\u00E8gle sp\u00E9cifique. Judea Pearl d\u00E9crit la recherche best-first comme l'estimation de la qualit\u00E9 d'un n\u0153ud n par une \"fonction heuristique d'\u00E9valuation qui, en g\u00E9n\u00E9ral, peut d\u00E9pendre de la description de n, de l'\u00E9tat d'arriv\u00E9e, des informations amass\u00E9es par l'algorithme au moment de l'\u00E9valuation et, surtout, de connaissances suppl\u00E9mentaires \u00E0 propos du probl\u00E8me\". Certains auteurs utilisent le terme best-first pour d\u00E9signer sp\u00E9cifiquement une recherche dont l'heuristique essaie de pr\u00E9dire la distance entre la fin d'un chemin et la solution, de sorte \u00E0 explorer en priorit\u00E9 le chemin le plus proche de la solution. Ce type pr\u00E9cis de recherche est nomm\u00E9 best-first glouton. La s\u00E9lection du meilleur candidat \u00E0 l'exploration est typiquement impl\u00E9ment\u00E9e en utilisant une file \u00E0 priorit\u00E9s. L'algorithme A* est un exemple de recherche best-first. Ce type de recherche est souvent utilis\u00E9e pour rechercher des chemins en optimisation combinatoire."@fr . . "\u041F\u043E\u0438\u0441\u043A \u043F\u043E \u043F\u0435\u0440\u0432\u043E\u043C\u0443 \u043D\u0430\u0438\u043B\u0443\u0447\u0448\u0435\u043C\u0443 \u0441\u043E\u0432\u043F\u0430\u0434\u0435\u043D\u0438\u044E"@ru . "Best-first Search"@pt . . "Best-first search"@en . . . "\u0628\u062D\u062B \u0623\u0648\u0644-\u0623\u0641\u0636\u0644"@ar . "\u041F\u043E\u0438\u0441\u043A \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB (\u0430\u043D\u0433\u043B. best-first search) \u2014 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0438\u0441\u043A\u0430, \u0438\u0441\u0441\u043B\u0435\u0434\u0443\u044E\u0449\u0438\u0439 \u0433\u0440\u0430\u0444 \u043F\u0443\u0442\u0451\u043C \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043D\u0438\u044F \u043D\u0430\u0438\u0431\u043E\u043B\u0435\u0435 \u043F\u0435\u0440\u0441\u043F\u0435\u043A\u0442\u0438\u0432\u043D\u044B\u0445 \u0443\u0437\u043B\u043E\u0432, \u0432\u044B\u0431\u0438\u0440\u0430\u0435\u043C\u044B\u0445 \u0432 \u0441\u043E\u043E\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0438 \u0441 \u0443\u043A\u0430\u0437\u0430\u043D\u043D\u044B\u043C \u043F\u0440\u0430\u0432\u0438\u043B\u043E\u043C. \u0414\u0436\u0443\u0434\u0430 \u041F\u0435\u0440\u043B (\u0430\u043D\u0433\u043B. Judea Pearl) \u043E\u043F\u0438\u0441\u0430\u043B \u043F\u043E\u0438\u0441\u043A \u00AB\u043B\u0443\u0447\u0448\u0438\u0439 \u2014 \u043F\u0435\u0440\u0432\u044B\u0439\u00BB, \u0432\u0437\u044F\u0432 \u0432 \u043A\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043E\u0446\u0435\u043D\u043A\u0438 \u0443\u0437\u043B\u0430 \u0437\u043D\u0430\u0447\u0435\u043D\u0438\u0435 \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0439 \u00AB\u044D\u0432\u0440\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u0444\u0443\u043D\u043A\u0446\u0438\u0438 \u043E\u0446\u0435\u043D\u043A\u0438 , \u043A\u043E\u0442\u043E\u0440\u0430\u044F, \u0432\u043E\u043E\u0431\u0449\u0435 \u0433\u043E\u0432\u043E\u0440\u044F, \u043C\u043E\u0436\u0435\u0442 \u0437\u0430\u0432\u0438\u0441\u0435\u0442\u044C \u043E\u0442 \u043F\u0440\u0438\u0440\u043E\u0434\u044B , \u043E\u043F\u0438\u0441\u0430\u043D\u0438\u044F \u0446\u0435\u043B\u0438, \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u0438 \u0441\u043E\u0431\u0440\u0430\u043D\u043D\u043E\u0439 \u043F\u043E\u0438\u0441\u043A\u043E\u043C \u043D\u0430 \u0434\u0430\u043D\u043D\u044B\u0439 \u043C\u043E\u043C\u0435\u043D\u0442 \u0438, \u0441\u0430\u043C\u043E\u0435 \u0433\u043B\u0430\u0432\u043D\u043E\u0435, \u043E\u0442 \u043A\u0430\u043A\u0438\u0445-\u043B\u0438\u0431\u043E \u0434\u043E\u043F\u043E\u043B\u043D\u0438\u0442\u0435\u043B\u044C\u043D\u044B\u0445 \u0437\u043D\u0430\u043D\u0438\u0439 \u043E \u043F\u0440\u0435\u0434\u043C\u0435\u0442\u043D\u043E\u0439 \u043E\u0431\u043B\u0430\u0441\u0442\u0438\u00BB."@ru . . "\uCD5C\uC0C1 \uC6B0\uC120 \uD0D0\uC0C9\uC740 \uD655\uC7A5 \uC911\uC778 \uB178\uB4DC\uB4E4 \uC911\uC5D0\uC11C \uBAA9\uD45C \uB178\uB4DC\uAE4C\uC9C0 \uB0A8\uC740 \uAC70\uB9AC\uAC00 \uAC00\uC7A5 \uC9E7\uC740 \uB178\uB4DC\uB97C \uD655\uC7A5\uD558\uC5EC \uD0D0\uC0C9\uD558\uB294 \uBC29\uBC95\uC774\uB2E4."@ko . . "\u0623\u0641\u0636\u0644-\u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644 \u0647\u0648 \u062E\u0648\u0627\u0631\u0632\u0645\u064A\u0629 \u0628\u062D\u062B \u062A\u0633\u062A\u0643\u0634\u0641 \u0645\u062C\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064A\u0627\u0646\u0627\u062A (Graph) \u0645\u0646 \u062E\u0644\u0627\u0644 \u062A\u0648\u0633\u064A\u0639 \u0646\u0637\u0627\u0642 \u0627\u0644\u0639\u0642\u062F\u0629 \u0627\u0644\u0623\u0643\u062B\u0631 \u0646\u062C\u0627\u062D\u0627\u064B \u0623\u064A \u0627\u0644\u0623\u0643\u062B\u0631 \u0645\u0637\u0627\u0628\u0642\u0629 \u0644\u0642\u0627\u0639\u062F\u0629 \u0645\u062D\u062F\u062F\u0629 \u064A\u062A\u0645 \u0627\u0644\u0628\u062D\u062B \u0628\u0648\u0627\u0633\u0637\u062A\u0647\u0627. \u064A\u0647\u0648\u062F\u0627 \u0628\u064A\u0631\u0644 \u0648\u0635\u0641 \u0627\u0644\u0628\u062D\u062B \u0627\u0644\u0623\u0648\u0644 \u0627\u0644\u0623\u0641\u0636\u0644 \u0645\u0646 \u062E\u0644\u0627\u0644 \u062A\u0642\u062F\u064A\u0631 \u062A\u0642\u062F\u064A\u0631 \u0646\u062C\u0627\u062D \u062A\u0644\u0643 \u0627\u0644\u0639\u0642\u062F\u0629 n \u00AB\u0648\u0630\u0644\u0643 \u0648\u0641\u0642\u0627\u064B \u0644\u062F\u0627\u0644\u0629 \u062A\u0642\u064A\u064A\u0645 \u0625\u0631\u0634\u0627\u062F\u064A\u0629 (heuristic evaluation function) \u0648\u0627\u0644\u062A\u064A \u0642\u062F \u062A\u0639\u062A\u0645\u062F \u0628\u0634\u0643\u0644 \u0639\u0627\u0645 \u0639\u0644\u0649 \u0643\u0644 \u0645\u0646: \u0648\u0635\u0641 n\u060C \u0648\u0635\u0641 \u0627\u0644\u0647\u062F\u0641\u060C \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062A \u0627\u0644\u062A\u064A \u062A\u0645 \u062C\u0645\u0639\u0647\u0627 \u0628\u0648\u0627\u0633\u0637\u0629 \u0627\u0644\u0628\u062D\u062B \u0639\u0646 \u062A\u0644\u0643 \u0627\u0644\u0646\u0642\u0637\u0629\u060C \u0648\u0627\u0644\u0623\u0647\u0645 \u0645\u0646 \u0630\u0644\u0643 \u0643\u0644\u0647\u060C \u0623\u064A \u0645\u0639\u0644\u0648\u0645\u0627\u062A \u0625\u0636\u0627\u0641\u064A\u0629 \u062D\u0648\u0644 \u0646\u0637\u0627\u0642 \u0627\u0644\u0645\u0639\u0636\u0644\u0629 (problem domain)\u00BB \u0639\u0627\u062F\u0629\u064B \u0645\u0627 \u064A\u062A\u0645 \u062A\u0646\u0641\u064A\u0630 \u0643\u0641\u0627\u0621\u0629 \u0627\u062E\u062A\u064A\u0627\u0631 \u0623\u0641\u0636\u0644 \u0645\u0631\u0634\u062D \u0644\u064A\u062A\u0645 \u062A\u0648\u0633\u064A\u0639\u0647 \u0628\u0627\u0633\u062A\u062E\u062F\u0627\u0645 \u0637\u0627\u0628\u0648\u0631 \u0627\u0644\u0623\u0648\u0644\u0648\u064A\u0629 (priority queue)."@ar . . . . . "Bestensuche (engl. best-first search) ist ein Algorithmus zum Durchsuchen eines Graphen, bei dem in jeder Iteration der vielversprechendste Knoten gew\u00E4hlt wird, bewertet nach einer gewissen Heuristik. Damit z\u00E4hlt er zu den informierten Such-Algorithmen. Um den vielversprechendsten Folgeknoten zu bestimmen, wird in konkreten Implementierungen oftmals eine Vorrangwarteschlange verwendet. Ein bekanntes Beispiel f\u00FCr die Bestensuche ist der A*-Algorithmus. Zur Wegfindung wird Bestensuche auch oftmals in der kombinatorischen Optimierung eingesetzt."@de . . . . . . "O algoritmo de busca melhor-primeiro ou \"best-first\" usa a fun\u00E7\u00E3o heur\u00EDstica F(n)=h(n) de procura ao n\u00F3 de destino. Esta procura expandir o n\u00F3 que \u00E9 mais pr\u00F3ximo ao objetivo, implicando numa condu\u00E7\u00E3o r\u00E1pida at\u00E9 o n\u00F3 destino. A heur\u00EDstica \u00E9 aplicada globalmente, isto \u00E9, o caminho a ser seguido \u00E9 selecionado entre todos os n\u00F3s abertos at\u00E9 o momento, o n\u00F3 aberto com a melhor nota \u00E9 escolhido para a expans\u00E3o."@pt . . . "1074258180"^^ . . "Best-first search"@it . "\u6700\u826F\u512A\u5148\u63A2\u7D22"@ja . "Best-first search (letteralmente \"ricerca prima il migliore\") \u00E8 una strategia di utilizzata per la risoluzione di problemi basati sulla ricerca ed \u00E8 alla base dei moderni algoritmi di intelligenza artificiale. Rispetto alle strategie di , di cui si fa uso nel caso in cui non si hanno informazioni specifiche sugli stati del problema oltre la definizione del problema, la ricerca best-first, come tutte le altre strategie di ricerca informata, sfrutta la conoscenza di ulteriori dettagli sugli stati del problema da risolvere. Si presuppone che il problema sia rappresentato come un albero di ricerca, in cui ogni nodo rappresenta uno stato ben preciso del problema e i nodi foglia costituiscono gli stati obiettivi. La radice \u00E8 lo stato iniziale del problema. Ogni singolo cammino dalla radice ad una qualsiasi foglia dell'albero rappresenta una soluzione al problema. L'obiettivo \u00E8 quello di trovare la soluzione pi\u00F9 efficiente in termini di velocit\u00E0 di esecuzione e di occupazione di memoria. La strategia di best-first search implementa un'apposita funzione di valutazione che ha il compito di selezionare, ad ogni passo della ricerca, il prossimo nodo da espandere. Ad ogni passo, dunque, tra tutti i nodi possibili da espandere l'algoritmo sceglie il nodo con la funzione di valutazione pi\u00F9 bassa (da cui best-first). Una funzione di questo tipo viene generalmente detta euristica e ha il compito di selezionare, di volta in volta, il nodo che sembra condurre alla soluzione ottimale del problema."@it . "\uCD5C\uC0C1 \uC6B0\uC120 \uD0D0\uC0C9\uC740 \uD655\uC7A5 \uC911\uC778 \uB178\uB4DC\uB4E4 \uC911\uC5D0\uC11C \uBAA9\uD45C \uB178\uB4DC\uAE4C\uC9C0 \uB0A8\uC740 \uAC70\uB9AC\uAC00 \uAC00\uC7A5 \uC9E7\uC740 \uB178\uB4DC\uB97C \uD655\uC7A5\uD558\uC5EC \uD0D0\uC0C9\uD558\uB294 \uBC29\uBC95\uC774\uB2E4."@ko . . "\u041F\u043E\u0448\u0443\u043A \u0437\u0430 \u043F\u0435\u0440\u0448\u0438\u043C \u043D\u0430\u0439\u043A\u0440\u0430\u0449\u0438\u043C \u0437\u0431\u0456\u0433\u043E\u043C"@uk . "Best-first search is a class of search algorithms, which explore a graph by expanding the most promising node chosen according to a specified rule. Judea Pearl described the best-first search as estimating the promise of node n by a \"heuristic evaluation function which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to that point, and most importantly, on any extra knowledge about the problem domain.\" Some authors have used \"best-first search\" to refer specifically to a search with a heuristic that attempts to predict how close the end of a path is to a solution (or, goal), so that paths which are judged to be closer to a solution (or, goal) are extended first. This specific type of search is called greedy best-first search or pure heuristic search. Efficient selection of the current best candidate for extension is typically implemented using a priority queue. The A* search algorithm is an example of a best-first search algorithm, as is B*. Best-first algorithms are often used for path finding in combinatorial search. Neither A* nor B* is a greedy best-first search, as they incorporate the distance from the start in addition to estimated distances to the goal."@en . . . . "\u041F\u043E\u0301\u0448\u0443\u043A \u00AB\u041D\u0430\u0439\u043A\u0440\u0430\u0301\u0449\u0438\u0439 \u2014 \u043F\u0435\u0301\u0440\u0448\u0438\u0439\u00BB \u2014 \u0446\u0435 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0448\u0443\u043A\u0443, \u044F\u043A\u0438\u0439 \u0434\u043E\u0441\u043B\u0456\u0434\u0436\u0443\u0454 \u0433\u0440\u0430\u0444 \u0448\u043B\u044F\u0445\u043E\u043C \u0440\u043E\u0437\u0448\u0438\u0440\u0435\u043D\u043D\u044F \u043D\u0430\u0439\u043F\u0435\u0440\u0441\u043F\u0435\u043A\u0442\u0438\u0432\u043D\u0456\u0448\u0438\u0445 \u0432\u0443\u0437\u043B\u0456\u0432, \u044F\u043A\u0456 \u043E\u0431\u0438\u0440\u0430\u044E\u0442\u044C\u0441\u044F \u0432\u0456\u0434\u043F\u043E\u0432\u0456\u0434\u043D\u043E \u0434\u043E \u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043E\u0433\u043E \u043F\u0440\u0430\u0432\u0438\u043B\u0430."@uk . . . . "Bestensuche (engl. best-first search) ist ein Algorithmus zum Durchsuchen eines Graphen, bei dem in jeder Iteration der vielversprechendste Knoten gew\u00E4hlt wird, bewertet nach einer gewissen Heuristik. Damit z\u00E4hlt er zu den informierten Such-Algorithmen. Judea Pearl beschrieb die Bestensuche als das Absch\u00E4tzen der Kosten eines Knotens n nach einer \"heuristischen Bewertungsfunktion , die im Allgemeinen abh\u00E4ngig von der Beschreibung von n ist, der Beschreibung des Ziels, den vom Algorithmus bis zu diesem Zeitpunkt gesammelten Informationen und vor allem vom Zusatzwissen \u00FCber die Problemdom\u00E4ne selbst.\" Um den vielversprechendsten Folgeknoten zu bestimmen, wird in konkreten Implementierungen oftmals eine Vorrangwarteschlange verwendet. Ein bekanntes Beispiel f\u00FCr die Bestensuche ist der A*-Algorithmus. Zur Wegfindung wird Bestensuche auch oftmals in der kombinatorischen Optimierung eingesetzt."@de .