. . . . . . . . . . . . . . . . . "\u7B26\u53F7\u4EBA\u5DE5\u667A\u6167\uFF08\u82F1\u8A9E\uFF1ASymbolic artificial intelligence\uFF09\u662F\u4EBA\u5DE5\u667A\u6167\u7814\u7A76\u4E2D\u7684\u4E00\u500B\u96C6\u5408\u8853\u8A9E\uFF0C\u6CDB\u6307\u6240\u6709\u300C\u57FA\u65BC\u554F\u984C\u3001\u903B\u8F91\u548C\u641C\u7D22\u7684\u9AD8\u7D1A\u300E\u7B26\u865F\u300F\uFF08\u4EBA\u985E\u53EF\u8B80\uFF09\u8868\u5FB5\u300D\u7684\u65B9\u6CD5\u3002\u4ECE1950\u5E74\u4EE3\u4E2D\u671F\u52301980\u5E74\u4EE3\u540E\u671F\uFF0C\u7B26\u865FAI\u4E00\u76F4\u662FAI\u7814\u7A76\u7684\u4E3B\u8981\u8303\u5F0F \u3002 1985\u5E74\uFF0C\u5728\u4ED6\u7684\u66F8\u300A \u4EBA\u5DE5\u667A\u6167\uFF1A\u975E\u5E38\u7684\u60F3\u6CD5 \u300B\u4E2D\u63A2\u8BA8\u4E86\u4EBA\u5DE5\u667A\u6167\u7814\u7A76\u7684\u54F2\u5B66\u542B\u4E49\uFF0C\u5C06\u7B26\u865F\u4EBA\u5DE5\u667A\u6167\u547D\u540D\u4E3AGOFAI\uFF08Good Old-Fashioned Artificial Intelligence\uFF0C\u6307\u7684\u662F\u201C\u6709\u6548\u7684\u8001\u5F0F\u4EBA\u5DE5\u667A\u6167\uFF09\u3002\u5728\u673A\u5668\u4EBA\u5B66\u9818\u57DF \uFF0C\u7C7B\u4F3C\u7684\u672F\u8BED\u662FGOFR\uFF08\u201C\u6709\u6548\u7684\u8001\u5F0F\u673A\u5668\u4EBA\u5B66\u201D\uFF09\u3002 \u8BE5\u65B9\u6CD5\u57FA\u4E8E\u8FD9\u6837\u7684\u5047\u8BBE\uFF1A\u300C\u667A\u6167\u7684\u8BB8\u591A\u7279\u5F81\u53EF\u4EE5\u900F\u904E\u7B26\u53F7\u8655\u7406\u6765\u5B9E\u73B0\u3002\u300D\u57281960\u5E74\u4EE3\u4E2D\u671F\uFF0C\u827E\u4F26\u00B7\u7EBD\u5384\u5C14\u548C\u8D6B\u4F2F\u7279\u00B7\u897F\u8499\u5C07\u8A72\u5047\u8BBE\u5B9A\u4E49\u4E3A\u300C\u7269\u7406\u7B26\u53F7\u7CFB\u7EDF\u5047\u8A2D\u300D\u3002 \u7B26\u865F\u4EBA\u5DE5\u667A\u6167\u4E2D\uFF0C\u6709\u4E00\u7A2E\u5E38\u7528\u7684\u5F62\u5F0F\u662F\u4E13\u5BB6\u7CFB\u7EDF \uFF0C\u8A72\u7CFB\u7D71\u4F7F\u7528\u7522\u51FA\u898F\u5247\u7DB2\u8DEF\u3002\u7522\u51FA\u898F\u5247\u662F\u4EE5\u7C7B\u4F3C\u300CIf-Then\u8BED\u53E5\u7684\u5173\u7CFB\u300D\u4F86\u8FDE\u63A5\u7B26\u53F7\u3002\u4E13\u5BB6\u7CFB\u7EDF\u6703\u4F7F\u7528\u4EBA\u7C7B\u53EF\u8BFB\u7684\u7B26\u53F7\u4F86\u5904\u7406\u89C4\u5219\uFF0C\u85C9\u6B64\u8FDB\u884C\u63A8\u8AD6\uFF0C\u5E76\u786E\u5B9A\u9084\u9700\u8981\u54EA\u4E9B\u5176\u4ED6\u4FE1\u606F\uFF0C\u4E5F\u5C31\u662F\u9084\u8981\u95EE\u4EC0\u4E48\u95EE\u9898\u3002 \u7B26\u865F\u65B9\u6CD5\u7684\u53CD\u5BF9\u8005\u5305\u62EC\u7F57\u5FB7\u5C3C\u00B7\u5E03\u9C81\u514B\u65AF\u7B49\u6A5F\u5668\u4EBA\u5B78\u5C08\u5BB6 \uFF0C\u4ED6\u4EEC\u6253\u7B97\u751F\u4EA7\u7121\u7B26\u865F\u8868\u5FB5\uFF08\u6216\u4EC5\u5177\u6700\u4F4E\u9650\u5EA6\u7684\u8868\u5FB5\uFF09\u7684\u81EA\u5F8B\u6A5F\u5668\u4EBA\uFF0C\u5176\u5B83\u53CD\u5BF9\u8005\u9084\u5305\u62EC\u7814\u7A76\u4EBA\u5458\uFF0C\u4ED6\u4EEC\u5E94\u7528\u8BF8\u5982\u4EBA\u5DE5\u795E\u7ECF\u7F51\u7EDC\u548C\u6700\u4F73\u5316\u4E4B\u7C7B\u7684\u6280\u672F\u6765\u89E3\u51B3\u673A\u5668\u5B66\u4E60\u548C\u63A7\u5236\u5DE5\u7A0B\u4E2D\u7684\u95EE\u9898 \u3002 \u7B26\u865F\u4EBA\u5DE5\u667A\u6167\u7684\u76EE\u7684\u662F\u5728\u6A5F\u5668\u4E2D\u7522\u751F\u901A\u7528\u7684\u3001\u985E\u4EBA\u7684\u667A\u6167\uFF0C\u800C\u5927\u591A\u6578\u73FE\u4EE3\u7814\u7A76\u662F\u91DD\u5C0D\u7279\u5B9A\u7684\u5B50\u554F\u984C\u3002\u76EE\u524D\u5C0D\u901A\u7528\u667A\u6167\u7684\u7814\u7A76\u96C6\u4E2D\u5728\u901A\u7528\u4EBA\u5DE5\u667A\u6167\u7684\u5B50\u9886\u57DF\u4E2D \u3002 \u6700\u521D\uFF0C\u6A5F\u5668\u88AB\u8A2D\u8A08\u6210\u300C\u6839\u64DA\u7B26\u865F\u8868\u793A\u7684\u8F38\u5165\u300D\u4F86\u5236\u5B9A\u8F38\u51FA\u3002\u7576\u8F38\u5165\u662F\u660E\u78BA\u7684\u4E14\u5C6C\u65BC\u78BA\u5B9A\u6027\u6642\uFF0C\u8F38\u51FA\u5C31\u6703\u4F7F\u7528\u7B26\u865F\u3002\u4F46\u662F\uFF0C\u7576\u5B58\u5728\u4E0D\u78BA\u5B9A\u6027\u6642\uFF0C\u8868\u5FB5\u6703\u4F7F\u7528\"\u6A21\u7CCA\u903B\u8F91\"\u5B8C\u6210\uFF08\u4F8B\u5982\uFF0C\u5728\u5236\u5B9A\u9810\u6E2C\u7684\u6642\u5019\uFF09\u3002\u9019\u5728\u4EBA\u5DE5\u795E\u7ECF\u7F51\u7EDC\u4E2D\u53EF\u4EE5\u770B\u5230\u3002"@zh . "In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the middle 1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the ultimate goal of their field. An early boom, with early successes such as the Logic Theorist and Samuel's Checker's Playing Program led to unrealistic expectations and promises and was followed by the First AI Winter as funding dried up. A second boom (1969\u20131986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace. That boom, and some early successes, e.g., with XCON at DEC, was followed again by later disappointment. Problems with difficulties in knowledge acquisition, maintaining large knowledge bases, and brittleness in handling out-of-domain problems arose. Another, second, AI Winter (1988\u20132011) followed. Subsequently, AI researchers focused on addressing underlying problems in handling uncertainty and in knowledge acquisition. Uncertainty was addressed with formal methods such as hidden Markov models, Bayesian reasoning, and statistical relational learning. Symbolic machine learning addressed the knowledge acquisition problem with contributions including Version Space, Valiant's PAC learning, Quinlan's ID3 decision-tree learning, case-based learning, and inductive logic programming to learn relations. Neural networks, a sub-symbolic approach, had been pursued from early days and was to reemerge strongly in 2012. Early examples are Rosenblatt's perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, and work in convolutional neural networks by LeCun et al. in 1989. However, neural networks were not viewed as successful until about 2012: \"Until Big Data became commonplace, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn't work that well, compared to other methods. ... A revolution came in 2012, when a number of people, including a team of researchers working with Hinton, worked out a way to use the power of GPUs to enormously increase the power of neural networks.\" Over the next several years, deep learning had spectacular success in handling vision, speech recognition, speech synthesis, image generation, and machine translation. However, since 2020, as inherent difficulties with bias, explanation, comprehensibility, and robustness became more apparent with deep learning approaches; an increasing number of AI researchers have called for combining the best of both the symbolic and neural network approaches and addressing areas that both approaches have difficulty with, such as common-sense reasoning."@en . . . . . . . . . "Intelligenza artificiale simbolica"@it . . . . . . "L'intelligenza artificiale simbolica indica i metodi nella ricerca sull'intelligenza artificiale che si basano su rappresentazioni di problemi \"simboliche\" di (leggibili dall'uomo), logica e ricerca. L'IA simbolica \u00E8 stata il paradigma dominante della ricerca sull'IA dalla met\u00E0 degli anni '50 fino alla fine degli anni '80."@it . . . . . . . . . . . . . . . . . . . . . . . "La intel\u00B7lig\u00E8ncia artificial simb\u00F2lica, tamb\u00E9 anomenada GOFAI (les sigles de Good Old-Fashioned AI) es refereix a la intel\u00B7lig\u00E8ncia artificial m\u00E9s primitiva, cl\u00E0ssica i simb\u00F2lica. La intel\u00B7lig\u00E8ncia artificial normal, en canvi, engloba programaci\u00F3 i rob\u00F2tica evolucionada. L\u2019objectiu d\u2019aquesta \u00E9s construir sistemes inform\u00E0tics \u00FAtils que substitueixin o ajudin a tasques humanes. L\u2019objectiu de la GOFAI, en canvi, \u00E9s desenvolupar teories de la ment explicatives."@ca . . . . . "86699"^^ . . . . . . . . . . . . . . . . . . . . . . "1124907986"^^ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems."@en . . . . . . . . . "Intel\u00B7lig\u00E8ncia artificial simb\u00F2lica"@ca . . . . . . . . . . . . . . . . . "\u0421\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u044B\u0439 \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442 \u2014 \u0441\u043E\u0431\u0438\u0440\u0430\u0442\u0435\u043B\u044C\u043D\u043E\u0435 \u043D\u0430\u0437\u0432\u0430\u043D\u0438\u0435 \u0434\u043B\u044F \u0432\u0441\u0435\u0445 \u043C\u0435\u0442\u043E\u0434\u043E\u0432 \u0438\u0441\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u043D\u0438\u044F \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0430, \u043E\u0441\u043D\u043E\u0432\u0430\u043D\u043D\u044B\u0445 \u043D\u0430 \u0432\u044B\u0441\u043E\u043A\u043E\u0443\u0440\u043E\u0432\u043D\u0435\u0432\u043E\u043C \u00AB\u0441\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u043E\u043C\u00BB (\u0447\u0435\u043B\u043E\u0432\u0435\u043A\u043E\u0447\u0438\u0442\u0430\u0435\u043C\u043E\u043C) \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u0438\u0438 \u0437\u0430\u0434\u0430\u0447, \u043B\u043E\u0433\u0438\u043A\u0438 \u0438 \u043F\u043E\u0438\u0441\u043A\u0430. \u0421\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0418\u0418 \u043B\u0451\u0433 \u0432 \u043E\u0441\u043D\u043E\u0432\u0443 \u0434\u043E\u043C\u0438\u043D\u0438\u0440\u0443\u044E\u0449\u0435\u0439 \u043F\u0430\u0440\u0430\u0434\u0438\u0433\u043C\u044B \u0438\u0441\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u043D\u0438\u0439 \u0418\u0418 \u0441 \u0441\u0435\u0440\u0435\u0434\u0438\u043D\u044B 1950-\u0445 \u0434\u043E \u043A\u043E\u043D\u0446\u0430 1980-\u0445."@ru . . . . . . . . . . . . . "Inteligencia artificial simb\u00F3lica es el nombre colectivo para todos los m\u00E9todos de investigaci\u00F3n de la inteligencia artificial que se basan en representaciones de alto nivel \"simb\u00F3lico\" de los problemas, la l\u00F3gica matem\u00E1tica y la b\u00FAsqueda. IA simb\u00F3lica fue el paradigma dominante de la investigaci\u00F3n en IA desde mediados de los cincuenta hasta finales de los a\u00F1os ochenta. Despu\u00E9s, se introdujeron enfoques sub-simb\u00F3licos m\u00E1s recientes a la IA, basado en redes neuronales, estad\u00EDstica, optimizaci\u00F3n num\u00E9rica y otras t\u00E9cnicas. La IA simb\u00F3lica se sigue aplicando en algunos dominios m\u00E1s peque\u00F1os (como la representaci\u00F3n del conocimiento), pero la mayor\u00EDa de las aplicaciones de IA en el siglo XXI no emplean s\u00EDmbolos legibles como sus objetos primarios."@es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "\u0421\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u044B\u0439 \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442"@ru . . . . . . . . . . . . . . . . . . "La intel\u00B7lig\u00E8ncia artificial simb\u00F2lica, tamb\u00E9 anomenada GOFAI (les sigles de Good Old-Fashioned AI) es refereix a la intel\u00B7lig\u00E8ncia artificial m\u00E9s primitiva, cl\u00E0ssica i simb\u00F2lica. La intel\u00B7lig\u00E8ncia artificial normal, en canvi, engloba programaci\u00F3 i rob\u00F2tica evolucionada. L\u2019objectiu d\u2019aquesta \u00E9s construir sistemes inform\u00E0tics \u00FAtils que substitueixin o ajudin a tasques humanes. L\u2019objectiu de la GOFAI, en canvi, \u00E9s desenvolupar teories de la ment explicatives."@ca . . "\u7B26\u53F7\u4EBA\u5DE5\u667A\u6167\uFF08\u82F1\u8A9E\uFF1ASymbolic artificial intelligence\uFF09\u662F\u4EBA\u5DE5\u667A\u6167\u7814\u7A76\u4E2D\u7684\u4E00\u500B\u96C6\u5408\u8853\u8A9E\uFF0C\u6CDB\u6307\u6240\u6709\u300C\u57FA\u65BC\u554F\u984C\u3001\u903B\u8F91\u548C\u641C\u7D22\u7684\u9AD8\u7D1A\u300E\u7B26\u865F\u300F\uFF08\u4EBA\u985E\u53EF\u8B80\uFF09\u8868\u5FB5\u300D\u7684\u65B9\u6CD5\u3002\u4ECE1950\u5E74\u4EE3\u4E2D\u671F\u52301980\u5E74\u4EE3\u540E\u671F\uFF0C\u7B26\u865FAI\u4E00\u76F4\u662FAI\u7814\u7A76\u7684\u4E3B\u8981\u8303\u5F0F \u3002 1985\u5E74\uFF0C\u5728\u4ED6\u7684\u66F8\u300A \u4EBA\u5DE5\u667A\u6167\uFF1A\u975E\u5E38\u7684\u60F3\u6CD5 \u300B\u4E2D\u63A2\u8BA8\u4E86\u4EBA\u5DE5\u667A\u6167\u7814\u7A76\u7684\u54F2\u5B66\u542B\u4E49\uFF0C\u5C06\u7B26\u865F\u4EBA\u5DE5\u667A\u6167\u547D\u540D\u4E3AGOFAI\uFF08Good Old-Fashioned Artificial Intelligence\uFF0C\u6307\u7684\u662F\u201C\u6709\u6548\u7684\u8001\u5F0F\u4EBA\u5DE5\u667A\u6167\uFF09\u3002\u5728\u673A\u5668\u4EBA\u5B66\u9818\u57DF \uFF0C\u7C7B\u4F3C\u7684\u672F\u8BED\u662FGOFR\uFF08\u201C\u6709\u6548\u7684\u8001\u5F0F\u673A\u5668\u4EBA\u5B66\u201D\uFF09\u3002 \u8BE5\u65B9\u6CD5\u57FA\u4E8E\u8FD9\u6837\u7684\u5047\u8BBE\uFF1A\u300C\u667A\u6167\u7684\u8BB8\u591A\u7279\u5F81\u53EF\u4EE5\u900F\u904E\u7B26\u53F7\u8655\u7406\u6765\u5B9E\u73B0\u3002\u300D\u57281960\u5E74\u4EE3\u4E2D\u671F\uFF0C\u827E\u4F26\u00B7\u7EBD\u5384\u5C14\u548C\u8D6B\u4F2F\u7279\u00B7\u897F\u8499\u5C07\u8A72\u5047\u8BBE\u5B9A\u4E49\u4E3A\u300C\u7269\u7406\u7B26\u53F7\u7CFB\u7EDF\u5047\u8A2D\u300D\u3002 \u7B26\u865F\u4EBA\u5DE5\u667A\u6167\u4E2D\uFF0C\u6709\u4E00\u7A2E\u5E38\u7528\u7684\u5F62\u5F0F\u662F\u4E13\u5BB6\u7CFB\u7EDF \uFF0C\u8A72\u7CFB\u7D71\u4F7F\u7528\u7522\u51FA\u898F\u5247\u7DB2\u8DEF\u3002\u7522\u51FA\u898F\u5247\u662F\u4EE5\u7C7B\u4F3C\u300CIf-Then\u8BED\u53E5\u7684\u5173\u7CFB\u300D\u4F86\u8FDE\u63A5\u7B26\u53F7\u3002\u4E13\u5BB6\u7CFB\u7EDF\u6703\u4F7F\u7528\u4EBA\u7C7B\u53EF\u8BFB\u7684\u7B26\u53F7\u4F86\u5904\u7406\u89C4\u5219\uFF0C\u85C9\u6B64\u8FDB\u884C\u63A8\u8AD6\uFF0C\u5E76\u786E\u5B9A\u9084\u9700\u8981\u54EA\u4E9B\u5176\u4ED6\u4FE1\u606F\uFF0C\u4E5F\u5C31\u662F\u9084\u8981\u95EE\u4EC0\u4E48\u95EE\u9898\u3002 \u7B26\u865F\u65B9\u6CD5\u7684\u53CD\u5BF9\u8005\u5305\u62EC\u7F57\u5FB7\u5C3C\u00B7\u5E03\u9C81\u514B\u65AF\u7B49\u6A5F\u5668\u4EBA\u5B78\u5C08\u5BB6 \uFF0C\u4ED6\u4EEC\u6253\u7B97\u751F\u4EA7\u7121\u7B26\u865F\u8868\u5FB5\uFF08\u6216\u4EC5\u5177\u6700\u4F4E\u9650\u5EA6\u7684\u8868\u5FB5\uFF09\u7684\u81EA\u5F8B\u6A5F\u5668\u4EBA\uFF0C\u5176\u5B83\u53CD\u5BF9\u8005\u9084\u5305\u62EC\u7814\u7A76\u4EBA\u5458\uFF0C\u4ED6\u4EEC\u5E94\u7528\u8BF8\u5982\u4EBA\u5DE5\u795E\u7ECF\u7F51\u7EDC\u548C\u6700\u4F73\u5316\u4E4B\u7C7B\u7684\u6280\u672F\u6765\u89E3\u51B3\u673A\u5668\u5B66\u4E60\u548C\u63A7\u5236\u5DE5\u7A0B\u4E2D\u7684\u95EE\u9898 \u3002"@zh . . . . . . . . . . . . . . . . . . . . . . . . . . . . "\u7B26\u865F\u4EBA\u5DE5\u667A\u80FD"@zh . . . . . . . . . . . . . . "\u0421\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u044B\u0439 \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442 \u2014 \u0441\u043E\u0431\u0438\u0440\u0430\u0442\u0435\u043B\u044C\u043D\u043E\u0435 \u043D\u0430\u0437\u0432\u0430\u043D\u0438\u0435 \u0434\u043B\u044F \u0432\u0441\u0435\u0445 \u043C\u0435\u0442\u043E\u0434\u043E\u0432 \u0438\u0441\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u043D\u0438\u044F \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0430, \u043E\u0441\u043D\u043E\u0432\u0430\u043D\u043D\u044B\u0445 \u043D\u0430 \u0432\u044B\u0441\u043E\u043A\u043E\u0443\u0440\u043E\u0432\u043D\u0435\u0432\u043E\u043C \u00AB\u0441\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u043E\u043C\u00BB (\u0447\u0435\u043B\u043E\u0432\u0435\u043A\u043E\u0447\u0438\u0442\u0430\u0435\u043C\u043E\u043C) \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u0438\u0438 \u0437\u0430\u0434\u0430\u0447, \u043B\u043E\u0433\u0438\u043A\u0438 \u0438 \u043F\u043E\u0438\u0441\u043A\u0430. \u0421\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0418\u0418 \u043B\u0451\u0433 \u0432 \u043E\u0441\u043D\u043E\u0432\u0443 \u0434\u043E\u043C\u0438\u043D\u0438\u0440\u0443\u044E\u0449\u0435\u0439 \u043F\u0430\u0440\u0430\u0434\u0438\u0433\u043C\u044B \u0438\u0441\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u043D\u0438\u0439 \u0418\u0418 \u0441 \u0441\u0435\u0440\u0435\u0434\u0438\u043D\u044B 1950-\u0445 \u0434\u043E \u043A\u043E\u043D\u0446\u0430 1980-\u0445. \u0412 1985 \u0433\u043E\u0434\u0443 (\u0430\u043D\u0433\u043B. John Haugeland) \u0434\u0430\u043B \u0441\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u043E\u043C\u0443 \u0418\u0418 \u043D\u0430\u0437\u0432\u0430\u043D\u0438\u0435 GOFAI (\u0430\u043D\u0433\u043B. Good Old-Fashioned Artificial Intelligence, \u00AB\u0441\u0442\u0430\u0440\u044B\u0439 \u0434\u043E\u0431\u0440\u044B\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u044B\u0439 \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u00BB) \u0432 \u0441\u0432\u043E\u0435\u0439 \u043A\u043D\u0438\u0433\u0435 Artificial Intelligence: The Very Idea, \u043F\u043E\u0441\u0432\u044F\u0449\u0451\u043D\u043D\u043E\u0439 \u0444\u0438\u043B\u043E\u0441\u043E\u0444\u0441\u043A\u043E\u043C\u0443 \u043E\u0442\u0440\u0430\u0436\u0435\u043D\u0438\u044E \u043F\u043E\u0441\u043B\u0435\u0434\u0441\u0442\u0432\u0438\u0439 \u0438\u0441\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u043D\u0438\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0430. \u0412 \u0440\u043E\u0431\u043E\u0442\u043E\u0442\u0435\u0445\u043D\u0438\u043A\u0435 \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0435\u0442\u0441\u044F \u0430\u043D\u0430\u043B\u043E\u0433\u0438\u0447\u043D\u044B\u0439 \u0442\u0435\u0440\u043C\u0438\u043D GOFAIR (\u00AB\u0441\u0442\u0430\u0440\u044B\u0439 \u0434\u043E\u0431\u0440\u044B\u0439 \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u044B\u0439 \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442 \u0432 \u0440\u043E\u0431\u043E\u0442\u043E\u0442\u0435\u0445\u043D\u0438\u043A\u0435\u00BB). \u041D\u0430\u0438\u0431\u043E\u043B\u0435\u0435 \u0443\u0441\u043F\u0435\u0448\u043D\u0430\u044F \u0444\u043E\u0440\u043C\u0430 \u0441\u0438\u043C\u0432\u043E\u043B\u0438\u0447\u0435\u0441\u043A\u043E\u0433\u043E \u0418\u0418 \u2014 \u044D\u0442\u043E \u044D\u043A\u0441\u043F\u0435\u0440\u0442\u043D\u044B\u0435 \u0441\u0438\u0441\u0442\u0435\u043C\u044B, \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u044E\u0449\u0438\u0435 \u0441\u0435\u0442\u044C \u043F\u0440\u043E\u0434\u0443\u043A\u0446\u0438\u043E\u043D\u043D\u044B\u0445 \u043F\u0440\u0430\u0432\u0438\u043B. \u041F\u0440\u043E\u0434\u0443\u043A\u0446\u0438\u043E\u043D\u043D\u044B\u0435 \u043F\u0440\u0430\u0432\u0438\u043B\u0430 \u043E\u0431\u044A\u0435\u0434\u0438\u043D\u044F\u044E\u0442 \u0441\u0438\u043C\u0432\u043E\u043B\u044B \u0432 \u043E\u0442\u043D\u043E\u0448\u0435\u043D\u0438\u044F, \u043F\u043E\u0445\u043E\u0436\u0438\u0435 \u043D\u0430 \u043E\u043F\u0435\u0440\u0430\u0442\u043E\u0440 \u00AB\u0435\u0441\u043B\u0438-\u0442\u043E\u00BB. \u042D\u043A\u0441\u043F\u0435\u0440\u0442\u043D\u0430\u044F \u0441\u0438\u0441\u0442\u0435\u043C\u0430, \u043E\u0431\u0440\u0430\u0431\u0430\u0442\u044B\u0432\u0430\u044F \u044D\u0442\u0438 \u043F\u0440\u0430\u0432\u0438\u043B\u0430, \u0434\u0435\u043B\u0430\u0435\u0442 \u043B\u043E\u0433\u0438\u0447\u0435\u0441\u043A\u0438\u0435 \u0432\u044B\u0432\u043E\u0434\u044B \u0438 \u043E\u043F\u0440\u0435\u0434\u0435\u043B\u044F\u0435\u0442, \u043A\u0430\u043A\u0430\u044F \u0434\u043E\u043F\u043E\u043B\u043D\u0438\u0442\u0435\u043B\u044C\u043D\u0430\u044F \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u044F \u0435\u0439 \u043D\u0435\u043E\u0431\u0445\u043E\u0434\u0438\u043C\u0430, \u0442\u043E \u0435\u0441\u0442\u044C \u043A\u0430\u043A\u0438\u0435 \u0441\u043B\u0435\u0434\u0443\u0435\u0442 \u0437\u0430\u0434\u0430\u0442\u044C \u0432\u043E\u043F\u0440\u043E\u0441\u044B, \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u044F \u0447\u0435\u043B\u043E\u0432\u0435\u043A\u043E\u0447\u0438\u0442\u0430\u0435\u043C\u044B\u0435 \u0441\u0438\u043C\u0432\u043E\u043B\u044B."@ru . . . . . . . . . . . "L'intelligenza artificiale simbolica indica i metodi nella ricerca sull'intelligenza artificiale che si basano su rappresentazioni di problemi \"simboliche\" di (leggibili dall'uomo), logica e ricerca. L'IA simbolica \u00E8 stata il paradigma dominante della ricerca sull'IA dalla met\u00E0 degli anni '50 fino alla fine degli anni '80."@it . . . . "339417"^^ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Inteligencia artificial simb\u00F3lica es el nombre colectivo para todos los m\u00E9todos de investigaci\u00F3n de la inteligencia artificial que se basan en representaciones de alto nivel \"simb\u00F3lico\" de los problemas, la l\u00F3gica matem\u00E1tica y la b\u00FAsqueda. IA simb\u00F3lica fue el paradigma dominante de la investigaci\u00F3n en IA desde mediados de los cincuenta hasta finales de los a\u00F1os ochenta. Despu\u00E9s, se introdujeron enfoques sub-simb\u00F3licos m\u00E1s recientes a la IA, basado en redes neuronales, estad\u00EDstica, optimizaci\u00F3n num\u00E9rica y otras t\u00E9cnicas. La IA simb\u00F3lica se sigue aplicando en algunos dominios m\u00E1s peque\u00F1os (como la representaci\u00F3n del conocimiento), pero la mayor\u00EDa de las aplicaciones de IA en el siglo XXI no emplean s\u00EDmbolos legibles como sus objetos primarios. dio el nombre GOFAI (por sus siglas en ingl\u00E9s, Good Old-Fashioned Artificial Intelligence) a la IA simb\u00F3lica en su libro de 1985 Inteligencia Artificial: La Idea Pura, que explor\u00F3 las implicaciones filos\u00F3ficas de la investigaci\u00F3n de la inteligencia artificial. En rob\u00F3tica el t\u00E9rmino an\u00E1logo es GOFAIR (por sus siglas en ingl\u00E9s, \"Good Old-Fashioned Robotics\"). Este enfoque se basa en la suposici\u00F3n de que muchos aspectos de la inteligencia se pueden lograr mediante la manipulaci\u00F3n de s\u00EDmbolos, un supuesto definido como la \"\" por Allen Newell y Herbert A. Simon a mediados de los a\u00F1os 60\u2019s: La forma m\u00E1s exitosa de la IA simb\u00F3lica son los sistemas expertos, los cuales utilizan una red de . Las normas de producci\u00F3n conectan s\u00EDmbolos en una relaci\u00F3n similar a una instrucci\u00F3n \u201CSi-Entonces\u201D (If-Then). El sistema experto procesa las reglas para hacer deducciones y determinar qu\u00E9 informaci\u00F3n adicional se necesita, por ejemplo, qu\u00E9 preguntas hacer, usando simbolog\u00EDa legible para el ser humano. Los opositores a la aproximaci\u00F3n simb\u00F3lica incluyen a expertos en rob\u00F3tica como Rodney Brooks, quien tiene como objetivo producir robots aut\u00F3nomos sin representaci\u00F3n simb\u00F3lica (o con s\u00F3lo una m\u00EDnima representaci\u00F3n) y los investigadores de inteligencia computacional, que aplican t\u00E9cnicas como las redes neuronales y optimizaci\u00F3n para resolver problemas en el aprendizaje autom\u00E1tico y la ingenier\u00EDa de control. La IA simb\u00F3lica estaba destinada a producir inteligencia similar a la humana en una m\u00E1quina, mientras que la mayor parte de la investigaci\u00F3n moderna se dirige a subproblemas espec\u00EDficos. La investigaci\u00F3n sobre la inteligencia general est\u00E1 en estudio del subcampo llamado Inteligencia Artificial General."@es . . . . . . . . . "Symbolic artificial intelligence"@en . . . "Inteligencia artificial simb\u00F3lica"@es . . . . . . . . . . . . . .