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\u5317\u4eac\u65f6\u95f4 8 \u6708 9 \u65e5\u4e0b\u5348\u56db\u70b9\uff0c\u7b2c 33 \u5c4a\u56fd\u9645\u4eba\u5de5\u667a\u80fd\u8054\u5408\u4f1a\u8bae IJCAI \u6b63\u5f0f\u7ed3\u675f\uff0c\u6b64\u6b21\u5927\u4f1a\u4e3a\u671f 7 \u5929\uff0c\u4e8e 8 \u6708 3 \u65e5\u5230 8 \u6708 9 \u65e5\u5728\u97e9\u56fd\u6d4e\u5dde\u5c9b\u4e3e\u884c\uff0c\u6240\u6709\u76f8\u5173\u5956\u9879\u5747\u5df2\u516c\u5e03\u3002<\/p>\n

\u6839\u636e IJCAI \u5b98\u65b9\u6570\u636e\uff0cIJCAI 2024 \u5df2\u7ecf\u6210\u4e3a\u8be5\u4f1a\u5386\u53f2\u4e0a\u89c4\u6a21\u6700\u5927\u7684\u4e00\u5c4a\uff0c\u4eca\u5e74\u7684\u6295\u7a3f\u6570\u91cf\u8fbe\u5230\u4e86\u521b\u7eaa\u5f55\u7684 5651 \u7bc7\uff0c\u73b0\u573a\u53c2\u4f1a\u4eba\u6570\u63a5\u8fd1\u4e09\u5343\u4eba\uff0c\u5176\u4e2d\u4e2d\u56fd\u4eba\u7ea6\u6709\u4e00\u5343\u4eba\u3002<\/p>\n

\u4f5c\u4e3a\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684 CCF-A \u7c7b\u9876\u7ea7\u56fd\u9645\u4f1a\u8bae\uff0c\u7b2c\u4e00\u5c4a IJCAI \u4f1a\u8bae\u8981\u8ffd\u6eaf\u5230 1969 \u5e74\u52a0\u5229\u798f\u5c3c\u4e9a\uff0c\u4e4b\u540e\u6bcf\u4e24\u5e74\u53ec\u5f00\u4e00\u6b21\uff0c\u4ece 2016 \u5e74\u5f00\u59cb IJCAI \u8f6c\u4e3a\u6bcf\u5e74\u4e3e\u529e\u4e00\u6b21\uff0c\u53bb\u5e74 IJCAI-23 \u5728\u6fb3\u95e8\u4e3e\u884c\uff0c\u65e0\u6570\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u4eba\u5458\u4ece\u5168\u7403\u6c47\u805a\u4e8e\u6b64\uff0c\u5728\u8fd9\u4e2a\u5e73\u53f0\u4e0a\u5c55\u793a\u524d\u6cbf\u79d1\u5b66\u6210\u679c\u3001\u5c55\u793a\u8bba\u6587\uff0c\u8fdb\u884c\u5b66\u672f\u4ea4\u6d41\u3002<\/p>\n

\u4e0d\u540c\u4e8e\u5728\u7f8e\u56fd\u897f\u96c5\u56fe\u521a\u521a\u4e3e\u529e\u7684 CVPR \u4f1a\u8bae\u4ee5\u53ca\u63a5\u4e0b\u6765\u5728\u6cf0\u56fd\u5c06\u8981\u4e3e\u884c\u7684 ACL \u4f1a\u8bae\uff0cIJCAI \u662f\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u7efc\u5408\u7c7b\u4f1a\u8bae\uff0c\u672c\u6b21\u4f1a\u8bae\u6db5\u76d6\u4e86Learning\u3001Neural Networks\u3001Models\u3001Transformer\u3001Contrastive\u3001Reinforcement\u7b49\u5173\u952e\u8bcd\uff0c\u5305\u542b\u4e86\u591a\u4e2a\u9886\u57df\uff0cUncertainty in \u00a0AI\uff0cRobotics\uff0cSearch\uff0cPlanning and Scheduling\u2026\u2026\u5171 15 \u4e2a\u65b9\u5411\u3002<\/p>\n<\/p>\n

\u5728 8 \u6708 6 \u65e5\u7684\u5f00\u5e55\u5f0f\u4e0a\uff0cIJCAI \u516c\u5e03\u4e86\u4eca\u5e74\u7684\u8bba\u6587\u63a5\u6536\u7ed3\u679c\uff1a5651 \u7bc7\u63d0\u4ea4\u8bba\u6587\uff0c\u5176\u4e2d 791 \u7bc7\u88ab\u63a5\u6536\uff0c\u5f55\u7528\u7387\u4e3a 14.0%\u3002<\/p>\n

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\u5bf9\u6bd4\u4e0a\u5c4a\uff0cIJCAI-23 \u5171\u6709 4566 \u7bc7\u8bba\u6587\u88ab\u63d0\u4ea4\uff0c643 \u7bc7\u8bba\u6587\u88ab\u63a5\u6536\uff0c\u5f55\u7528\u7387\u4e3a 14.1%\u3002\u4eca\u5e74\u7684\u8bba\u6587\u63d0\u4ea4\u91cf\u521b\u4e0b\u65b0\u9ad8\uff0c\u63d0\u9ad8\u4e86 23.8%\uff0c\u4f46\u5f55\u7528\u7387\u8ddf\u4e0a\u5e74\u6301\u5e73\u3002<\/p>\n

\u81ea 2020 \u5e74\u8d77\uff0cIJCAI \u5f55\u7528\u7387\u4fbf\u4e00\u8def\u8d70\u4f4e\uff0c\u63a5\u4e0b\u6765\u7684\u51e0\u5e74\u4e00\u76f4\u5728 14% \u6c34\u5e73\u7ebf\u4e0a\u4e0b\u5f98\u5f8a\uff0c\u5bf9\u6bd4\u6b64\u524d\u5f55\u7528\u7387\u4fdd\u6301\u5728 20%\u201428% \u8303\u56f4\u5185\uff0c\u81ea\u6b64\u7f51\u7edc\u4e0a\u5173\u4e8e IJCAI \u5f55\u7528\u4e25\u82db\u7684\u8ba8\u8bba\u58f0\u8d77\uff0c\u4eca\u5e74\u4e5f\u4e0d\u4f8b\u5916\u3002<\/p>\n

\u5bf9\u6b64\uff0c\u5927\u4f1a\u4e3b\u5e2d\u5f20\u6210\u5947\u8ba4\u4e3a\uff0c\u4f5c\u8005\u4f1a\u5bf9\u8bba\u6587\u662f\u5426\u6295\u4e2d\u7684\u7ed3\u679c\u6709\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7684\u4e0d\u540c\u610f\u89c1\uff0c\u8fd9\u662f\u6b63\u5e38\u73b0\u8c61\u3002\u5bf9\u4e8e\u8fd9\u4e00\u4e89\u8bae\uff0c\u5176\u4ed6\u4f1a\u8bae\u4e0a\u4e5f\u4f1a\u5b58\u5728\u3002<\/p>\n

\u636eAI \u79d1\u6280\u8bc4\u8bba\u89c2\u5bdf\uff0c\u968f\u7740\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u7684\u84ec\u52c3\u53d1\u5c55\uff0cIJCAI \u7b49\u9876\u7ea7\u4f1a\u8bae\u7684\u6295\u7a3f\u91cf\u6025\u5267\u589e\u52a0\uff0c\u7ade\u4e89\u8d8a\u53d1\u6fc0\u70c8\uff0c\u7279\u522b\u4ece\u4eca\u5e74\u7684\u63d0\u4ea4\u6570\u91cf\u9ad8\u51fa\u53bb\u5e74 23.8% \u4fbf\u53ef\u770b\u51fa\u3002\u5728\u8fd9\u4e2a\u9636\u6bb5\uff0c\u5b66\u672f\u754c\u8d8a\u6765\u8d8a\u91cd\u89c6\u7814\u7a76\u8d28\u91cf\u800c\u975e\u6570\u91cf\uff0c\u53ef\u80fd\u5bfc\u81f4\u4f1a\u8bae\u7ec4\u7ec7\u8005\u66f4\u52a0\u8c28\u614e\u9009\u62e9\u6536\u5f55\u7684\u8bba\u6587\uff0c\u5982 IJCAI \u8fd9\u6837\u7684\u9876\u7ea7\u4f1a\u8bae\u5f00\u59cb\u5448\u73b0\u51fa\u4ee5\u4fdd\u8bc1\u4f1a\u8bae\u7684\u5b66\u672f\u6c34\u5e73\u548c\u5f71\u54cd\u529b\u3001\u800c\u964d\u4f4e\u5f55\u53d6\u7387\u7684\u8d8b\u52bf\u3002<\/p>\n

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IJCAI \u00a0\u9881\u5956\u73af\u8282<\/strong><\/span><\/p>\n

Distinguished Papers<\/strong><\/p>\n

\u5173\u6ce8\u5ea6\u8f83\u9ad8\u7684\u4e09\u7bc7 Distinguished Papers \u4e2d\uff0c\u6ca1\u6709\u4e2d\u56fd\u5b66\u8005\u4e0a\u699c\u3002<\/p>\n

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\u7b2c\u4e00\u7bc7\u9881\u53d1\u7ed9\u4e86 MIT\u3001University of Chicago\u3001TTIC \u5171\u540c\u53d1\u8868\u7684\u300aOnline Combinatorial Optimization with Group Fairness Constraints\u300b\uff0c\u8be5\u8bba\u6587\u63a2\u8ba8\u4e86\u4e8e\u5728\u7ebf\u7ec4\u5408\u4f18\u5316\u9886\u57df\u5f15\u5165\u7fa4\u4f53\u516c\u5e73\u6027\u7ea6\u675f\u7684\u65b0\u89c6\u89d2\u3002<\/p>\n

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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.ijcai.org\/Proceedings\/2024\/0044.pdf<\/p>\n

\u8bba\u6587\u4ecb\u7ecd\uff1a\u4e3a\u4e86\u89e3\u51b3\u6570\u5b57\u5e02\u573a\u4e2d\u7684\u5b9e\u65bd\u516c\u5e73\u7ea6\u675f\u7684\u95ee\u9898\uff0c\u8bba\u6587\u4e2d\u63d0\u51fa\u4e86\u4e00\u4e2a\u5c06\u9c81\u68d2\u6027\u548c\u516c\u5e73\u6027\u7eb3\u5165 NP \u5b8c\u5168\u95ee\u9898\u7684\u901a\u7528\u6846\u67b6\uff0c\u4f8b\u5982\u4f18\u5316\u4ea7\u54c1\u6392\u540d\u548c\u6700\u5927\u5316\u5b50\u6a21\u5757\u51fd\u6570\u3002<\/p>\n

\u5229\u7528\u8fd9\u4e00\u6846\u67b6\u53ef\u4ee5\u5c06\u95ee\u9898\u8f6c\u5316\u4e3a\u65e8\u5728\u6700\u5927\u5316\u5e73\u53f0\u76ee\u6807\u7684\u539f\u59cb\u73a9\u5bb6\u548c\u8d1f\u8d23\u7fa4\u4f53\u516c\u5e73\u7ea6\u675f\u7684\u53cc\u91cd\u73a9\u5bb6\u4e4b\u95f4\u7684\u6700\u5927\u6700\u5c0f\u535a\u5f08\u3002\u6700\u7ec8\u8868\u660e\uff0c\u901a\u8fc7\u6539\u53d8\u516c\u5e73\u7ea6\u675f\u7684\u9608\u503c\uff0c\u53ef\u4ee5\u8ffd\u8e2a\u6574\u4e2a\u5e15\u7d2f\u6258\u516c\u5e73\u66f2\u7ebf\u3002<\/p>\n

\u7b2c\u4e8c\u6279\u9881\u53d1\u7ed9\u4e86\u7531 Sapienza University of Rome\u3001University of Bergamo \u5171\u540c\u53d1\u8868\u7684\u300a Enhancing Controlled Query Evaluation Through Epistemic Policies\u300b\uff0c\u8be5\u8bba\u6587\u63d0\u51fa\u4e86\u901a\u8fc7\u8ba4\u77e5\u7b56\u7565\u589e\u5f3a\u53d7\u63a7\u67e5\u8be2\u8bc4\u4f30\u7684\u65b9\u6cd5\u3002<\/p>\n

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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.ijcai.org\/Proceedings\/2024\/0366.pdf<\/p>\n

\u8bba\u6587\u4ecb\u7ecd\uff1a\u8be5\u8bba\u6587\u63d0\u51fa\u4e86\u5728\u53d7\u63a7\u67e5\u8be2\u8bc4\u4f30\uff08CQE\uff09\u4e2d\uff0c\u4f7f\u7528\u8ba4\u77e5\u4f9d\u8d56\u6765\u8868\u8fbe\u6570\u636e\u4fdd\u62a4\u7b56\u7565\uff0c\u8fd9\u662f\u4e00\u79cd\u5728\u672c\u4f53\u548c\u6570\u636e\u5e93\u4e0a\u4fdd\u6301\u673a\u5bc6\u6027\u7684\u67e5\u8be2\u5e94\u7b54\u5f62\u5f0f\u3002\u8bba\u6587\u4e2d\u8fd8\u5c55\u793a\u4e86\u8fd9\u4e00\u6846\u67b6\u7684\u8868\u8fbe\u80fd\u529b\uff0c\u5e76\u7814\u7a76\u4e86\u5f53\u5728\u63cf\u8ff0\u903b\u8f91 DL LiteR \u4e2d\u6307\u5b9a\u672c\u4f53\u65f6\uff0cCQE \u5bf9\u4e8e\u8fde\u8bcd\u67e5\u8be2\u7684\u6570\u636e\u590d\u6742\u6027\u3002\u867d\u7136\u8fd9\u4e2a\u95ee\u9898\u901a\u5e38\u5f88\u96be\u89e3\u51b3\uff0c\u4f46\u8bba\u6587\u4e2d\u8036\u901a\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5408\u9002\u7684\u67e5\u8be2\u91cd\u5199\u7b97\u6cd5\uff0c\u8bc1\u660e\u4e86\u975e\u5faa\u73af\u8ba4\u77e5\u4f9d\u8d56\u60c5\u51b5\u4e0b\u7684\u53ef\u5904\u7406\u6027\u3002<\/p>\n

\u7b2c\u4e09\u7bc7\u9881\u7ed9\u4e86Sorbonne University\u00a0\u53d1\u8868\u7684\u300aOnline Learning of Capacity-Based Preference Models\u300b\uff0c\u7814\u7a76\u4e86\u57fa\u4e8e\u80fd\u529b\u504f\u597d\u6a21\u578b\u7684\u5728\u7ebf\u5b66\u4e60\u3002<\/p>\n

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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.ijcai.org\/Proceedings\/2024\/0787.pdf<\/p>\n

\u8bba\u6587\u4ecb\u7ecd\uff1a\u5728\u591a\u51c6\u5219\u51b3\u7b56\u4e2d\uff0c\u5c06\u57fa\u4e8e\u5bb9\u91cf\u7684\u51b3\u7b56\u6a21\u578b\u62df\u5408\u5230\u7ed9\u5b9a\u7684\u51b3\u7b56\u8005\u662f\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\u3002\u8be5\u8bba\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u5728\u7ebf\u7b97\u6cd5\uff0c\u7528\u4e8e\u5b66\u4e60\u5bb9\u91cf\u7684\u7a00\u758f\u8868\u793a\uff0c\u8be5\u7b97\u6cd5\u4e13\u4e3a\u504f\u597d\u793a\u4f8b\u6309\u987a\u5e8f\u53ef\u7528\u7684\u51b3\u7b56\u73af\u5883\u800c\u8bbe\u8ba1\u3002\u57fa\u4e8e\u6b63\u5219\u5316\u5bf9\u5076\u5e73\u5747\u7684\u65b9\u6cd5\uff0c\u4e5f\u5f88\u597d\u5730\u9002\u7528\u4e8e\u6d89\u53ca\u5927\u91cf\u504f\u597d\u793a\u4f8b\u6216\u5927\u91cf\u6807\u51c6\u7684\u51b3\u7b56\u73af\u5883\u3002\u6b64\u5916\uff0c\u6587\u4e2d\u63d0\u51fa\u4e86\u4e00\u79cd\u53d8\u4f53\uff0c\u4f7f\u5176\u53ef\u4ee5\u5728\u4fdd\u6301\u53ef\u6269\u5c55\u6027\u7684\u540c\u65f6\uff0c\u57fa\u4e8e\u4e58\u6570\u7684\u4ea4\u66ff\u65b9\u5411\u65b9\u6cd5\uff0c\u5305\u62ec\u5bf9\u5bb9\u91cf\u7684\u89c4\u8303\u7ea6\u675f\u3002<\/p>\n

\u6b64\u5916\uff0cIJCAI \u6bcf\u5e74\u90fd\u4f1a\u8bc4\u9009\u51fa Award for Research Excellence\u3001Computers and Thought Award\u3001John McCarthy Award\u3001Donald E. Walker \u56db\u4e2a\u5956\u9879\uff0c\u6388\u4e88\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u505a\u51fa\u6770\u51fa\u8d21\u732e\u7684\u79d1\u5b66\u5bb6\u3002<\/p>\n

\u5176\u4e2d\u524d\u4e09\u9879\u7684\u83b7\u5956\u8005\u65e9\u5728\u4f1a\u8bae\u5f00\u59cb\u524d\u4e00\u4e2a\u6708\u5c31\u5728\u5b98\u7f51\u516c\u5f00\uff0c8.6 \u65e5\u7684\u5f00\u5e55\u5f0f\u4e0a\u63ed\u6653\u4e86Donald E. Walker\u7684\u83b7\u5956\u8005\u3002<\/p>\n

IJCAI 2024\u00a0Award for Research Excellence \uff08\u5353\u8d8a\u7814\u7a76\u5956\uff09\u901a\u5e38\u529e\u6cd5\u7ed9\u9881\u53d1\u7ed9\u5728\u6574\u4e2a\u804c\u4e1a\u751f\u6daf\u4e2d\u59cb\u7ec8\u575a\u6301\u9ad8\u8d28\u91cf\u7814\u7a76\u8ba1\u5212\u5e76\u53d6\u5f97\u591a\u9879\u91cd\u5927\u6210\u679c\u7684\u79d1\u5b66\u5bb6\u30022024 \u5e74\u5353\u8d8a\u7814\u7a76\u5956\u9881\u7ed9\u4e86Thomas Dietterich\uff0c\u4ed6\u662f\u7f8e\u56fd\u4fc4\u52d2\u5188\u5dde\u7acb\u5927\u5b66\u534f\u4f5c\u673a\u5668\u4eba\u4e0e\u667a\u80fd\u7cfb\u7edf\u7814\u7a76\u6240 (CoRIS) \u7684\u6770\u51fa\u6559\u6388\uff08\u540d\u8a89\uff09\u517c\u667a\u80fd\u7cfb\u7edf\u4e3b\u4efb\u3002Dietterich \u6559\u6388\u7684\u7814\u7a76\u805a\u7126\u5728\u673a\u5668\u5b66\u4e60\u3001\u987a\u5e8f\u51b3\u7b56\u3001\u673a\u5668\u5b66\u4e60\u7cfb\u7edf\u7684\u5b89\u5168\u90e8\u7f72\u3001\u751f\u6001\u7cfb\u7edf\u7ba1\u7406\u4e2d\u5b9e\u9645\u95ee\u9898\u7684\u5e94\u7528\u7b49\u65b9\u9762\uff0c\u6570\u5341\u5e74\u6765\u5728\u673a\u5668\u5b66\u4e60\u9886\u57df\u6709\u7740\u91cd\u8981\u7684\u9886\u5bfc\u5730\u4f4d\uff0c\u53d6\u5f97\u4e86\u591a\u9879\u5f00\u521b\u6027\u5de5\u4f5c\u3002<\/p>\n

IJCAI\u00a02024 Computers and Thought Award\uff08\u8ba1\u7b97\u673a\u4e0e\u601d\u60f3\u5956\uff09\u9881\u7ed9\u4e86\u52a0\u62ff\u5927\u591a\u4f26\u591a\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u526f\u6559\u6388\u00a0Nisarg Shah\u3002<\/p>\n

Shah \u535a\u58eb\u7684\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u662f\u5229\u7528\u793e\u4f1a\u9009\u62e9\u7406\u8bba\u539f\u7406\u5728\u7b97\u6cd5\u516c\u5e73\u6027\u7406\u8bba\u65b9\u9762\u7684\u57fa\u7840\u6027\u5de5\u4f5c\u3002<\/p>\n

IJCAI 2024\u00a0John McCarthy Award\uff08\u7ea6\u7ff0\u00b7\u9ea6\u5361\u9521\u5956\uff09\u7684\u83b7\u5956\u8005\u662f\u7f8e\u56fd\u54e5\u4f26\u6bd4\u4e9a\u5927\u5b66\u7edf\u8ba1\u5b66\u548c\u8ba1\u7b97\u673a\u79d1\u5b66\u6559\u6388\u00a0David Blei\u3002<\/p>\n

Blei \u6559\u6388\u56e0\u5176\u5728\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u5f00\u521b\u6027\u5de5\u4f5c\u800c\u83b7\u5f97\u8ba4\u53ef\uff0c\u5c24\u5176\u662f\u4ed6\u5728\u4e3b\u9898\u6a21\u578b\u548c\u968f\u673a\u53d8\u5206\u63a8\u7406\u9886\u57df\u7684\u5f00\u521b\u6027\u8d21\u732e\u3002<\/p>\n

IJCAI 2024\u00a0Donald E. Walker\uff08\u6770\u51fa\u670d\u52a1\u5956\uff09\u7531 IJCAI \u7406\u4e8b\u4f1a\u4e8e 1979 \u5e74\u8bbe\u7acb\uff0c\u65e8\u5728\u8868\u5f70\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u8d44\u6df1\u79d1\u5b66\u5bb6\u5728\u5176\u804c\u4e1a\u751f\u6daf\u4e2d\u5bf9\u8be5\u9886\u57df\u7684\u8d21\u732e\u548c\u670d\u52a1\u30022024 \u5e74\u8be5\u5956\u9879\u9881\u7ed9\u4e86\u7f8e\u56fd\u660e\u5c3c\u82cf\u8fbe\u5927\u5b66\u53cc\u57ce\u5206\u6821\u7684\u00a0Maira Gini\u00a0\u6559\u6388\u3002<\/p>\n

AIJ \u6770\u51fa\u8bba\u6587\u5956\uff08AIJ Prominent Paper Award\uff09\u4e0e AIJ \u7ecf\u5178\u8bba\u6587\u5956\uff08Classic AIJ Paper Award\uff09<\/p>\n

\u5f00\u5e55\u5f0f\u4e0a\u8fd8\u516c\u5e03\u4e86\u4e24\u5927\u5956\u9879\uff0c\u5206\u522b\u662f AIJ \u6770\u51fa\u8bba\u6587\u5956\uff08AIJ Prominent Paper Award 2021\uff09\u4e0e AIJ \u7ecf\u5178\u8bba\u6587\u5956\uff08Classic AIJ Paper Award\uff09\u3002<\/p>\n

AIJ \u7684\u5168\u79f0\u4e3a\u201cArtificial Intelligence Journal\u201d\uff0c\u5373\u300a\u4eba\u5de5\u667a\u80fd\u671f\u520a\u300b\uff0c\u59cb\u5efa\u4e8e 1970 \u5e74\uff0c\u662f\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9886\u57df\u7684\u9876\u7ea7\u5b66\u672f\u671f\u520a\uff0c\u5177\u6709\u516c\u8ba4\u7684\u6743\u5a01\u6027\u4e0e\u77e5\u540d\u6027\u3002<\/p>\n

2024 AIJ \u6770\u51fa\u8bba\u6587\u5956\u9881\u53d1\u7ed9\u4e86\u7531PUCRS\uff08\u5357\u91cc\u5965\u683c\u5170\u5fb7\u5929\u4e3b\u6559\u5927\u5b66\uff09\u3001\u963f\u4f2f\u4e01\u5927\u5b66\u4e8e 2019 \u5e74\u5171\u540c\u53d1\u8868\u7684\u300aLandmark-Based Approaches for Goal Recognition as Planning\u300b\u3002<\/p>\n

\u8be5\u7814\u7a76\u4e3b\u8981\u8ba8\u8bba\u4e86\u5728\u81ea\u52a8\u5316\u89c4\u5212\u6280\u672f\u4e2d\uff0c\u5982\u4f55\u9ad8\u6548\u5730\u8bc6\u522b\u76ee\u6807\u548c\u8ba1\u5212\u3002\u7279\u522b\u662f\u5728\u9700\u8981\u5feb\u901f\u4e14\u51c6\u786e\u8bc6\u522b\u76ee\u6807\u548c\u8ba1\u5212\u7684\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u4f5c\u8005\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u9896\u7684\u76ee\u6807\u8bc6\u522b\u65b9\u6cd5\u3002\u8fd9\u4e9b\u65b9\u6cd5\u57fa\u4e8e\u89c4\u5212\u6280\u672f\uff0c\u7279\u522b\u662f\u4f9d\u8d56\u4e8e\u89c4\u5212\u4e2d\u7684”\u91cc\u7a0b\u7891”\uff08landmarks\uff09\u6982\u5ff5\u3002<\/p>\n

\u83b7\u5f97\u7ecf\u5178\u8bba\u6587\u5956\uff08Classic AIJ Paper Award\uff09\u7684\u662f\u300a Agent-oriented-programming\u300b \u3002\u8fd9\u4e00\u7bc7\u8bba\u6587\u53d1\u8868\u4e8e 1993 \u5e74\uff0c\u4f5c\u8005\u4e3a\u65af\u5766\u798f\u5927\u5b66\u7684 Yoav Sgoham \u6559\u6388\u3002<\/p>\n

\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/www.sciencedirect.com\/science\/article\/pii\/0004370293900349<\/p>\n

\u8bba\u6587\u4e2d\u63d0\u51fa\u4e86\u5f53\u65f6\u7684\u4e00\u79cd\u65b0\u7684\u8ba1\u7b97\u6846\u67b6\uff0c\u5373 AOP\uff0c\u5b83\u53ef\u4ee5\u88ab\u89c6\u4e3a\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u7684\u4e13\u95e8\u5316\u3002\u8bba\u6587\u4e2d\u5c06 Agent \u7684\u72b6\u6001\u5728\u6807\u51c6\u8ba4\u77e5\u903b\u8f91\u7684\u6269\u5c55\u4e2d\u5f97\u5230\u4e86\u5f62\u5f0f\u5316\u7684\u63cf\u8ff0\uff1a\u9664\u4e86\u5c06\u77e5\u8bc6\u548c\u4fe1\u5ff5\u8fd0\u7b97\u7b26\u65f6\u95f4\u5316\u4e4b\u5916\uff0cAOP\u8fd8\u5f15\u5165\u4e86\u4e49\u52a1\u3001\u51b3\u7b56\u548c\u80fd\u529b\u8fd0\u7b97\u7b26\u3002<\/p>\n

IJCAI-JAIR \u6700\u4f73\u8bba\u6587\u5956<\/strong><\/p>\n

\u81ea 2003 \u5e74\u8d77\uff0cIJCAI-JAIR \u6700\u4f73\u8bba\u6587\u5956\u6bcf\u5e74\u4ece\u6700\u8fd1 5 \u5e74\u53d1\u8868\u5728 JAIR \u7684\u8bba\u6587\u4e2d\u8bc4\u9009\u5e76\u8868\u5f70\u4e00\u7bc7\u6770\u51fa\u8bba\u6587\u3002\u8bc4\u5ba1\u7684\u6807\u51c6\u57fa\u4e8e\u8bba\u6587\u7684\u91cd\u8981\u6027\u548c presentation \u7684\u8d28\u91cf\u3002<\/p>\n

\u672c\u5c4a IJCAI-JAIR \u6700\u4f73\u8bba\u6587\u5956\u6388\u4e88\u4e86\u6765\u81ea MIT \u548c \u00a0Google Research \u56e2\u961f\u5171\u540c\u53d1\u8868\u7684\u300aConfident Learning\uff1aEstimating Uncertainty in Dataset Labels\u300b\u3002<\/p>\n

<\/a><\/p>\n

\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/1911.00068v3<\/p>\n

\u4f5c\u8005\uff1aMIT \u7684 Curtis G. Northcutt \u548c Isaac L. Chuang\uff0cGoogle Research \u7684 Lu Jiang<\/p>\n

\u8bba\u6587\u4ecb\u7ecd\uff1a\u8be5\u7bc7\u8bba\u6587\u805a\u7126\u4e8e\u4eba\u5de5\u667a\u80fd\u884c\u4e1a\u957f\u65f6\u95f4\u4ee5\u6765\u7684\u75db\u70b9\u6570\u636e\u9886\u57df\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u80fd\u591f\u63d0\u9ad8\u6807\u7b7e\u8d28\u91cf\u7684\u65b9\u6cd5 Confident Learning \uff08CL\uff09\u3002\u8bba\u6587\u4e2d\u6307\u51fa\u4e86 CL \u51c6\u786e\u53d1\u73b0\u6807\u7b7e\u9519\u8bef\u7684\u5145\u5206\u6761\u4ef6\uff0c\u5e76\u5c55\u793a\u4e86 CL \u6027\u80fd\u8d85\u8fc7\u4e86 CIFAR \u6570\u636e\u96c6\u4e0a\u4f7f\u7528\u566a\u58f0\u6807\u7b7e\u8fdb\u884c\u5b66\u4e60\u7684\u4e03\u79cd\u6700\u5148\u8fdb\u7684\u65b9\u6cd5\u3002<\/p>\n

<\/p>\n

AI \u4e0d\u6b62\u5927\u6a21\u578b<\/strong><\/span><\/p>\n<\/p>\n

\u6839\u636e IJCAI \u5b98\u65b9\u516c\u5e03\u7684\u6570\u636e\uff0c\u5bf9 IJCAI 2024 \u5f55\u7528\u8bba\u6587\u6570\u636e\u7684\u7edf\u8ba1\u5206\u6790\u6765\u770b\uff0c\u8bba\u6587\u4e3b\u8981\u6db5\u76d6\u4e86 15 \u4e2a\u4e3b\u9898\u9886\u57df\uff0c\u6392\u540d\u524d\u5341\u7684\u6709\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u673a\u5668\u5b66\u4e60\u3001\u6570\u636e\u6316\u6398\u3001\u591a\u5b66\u79d1\u4e3b\u9898\u548c\u5e94\u7528\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u535a\u5f08\u8bba\u548c\u7ecf\u6d4e\u8303\u5f0f\u3001AI \u4f26\u7406\u4fe1\u4efb\u548c\u516c\u5e73\u6027\u3001Agent- based\u548c\u591a agent \u7cfb\u7edf\u3001\u77e5\u8bc6\u8868\u793a\u548c\u77e5\u8bc6\u63a8\u7406\u3001\u4eba\u7c7b\u4e0e\u4eba\u5de5\u667a\u80fd\u3002<\/p>\n

<\/a><\/p>\n

\u5176\u4e2d\uff0c\u8ddf\u5927\u6a21\u578b\u76f8\u5173\u7684\u7814\u7a76\u9886\u57df\u4f9d\u7136\u662f 2024 IJCAI \u6700\u70ed\uff0c\u5c3d\u7ba1 CVPR \u521a\u8fc7\u53bb\u4e0d\u4e45\uff0c\u6392\u5728\u9996\u4f4d\u7684\u5173\u952e\u8bcd\u4f9d\u7136\u88ab\u8ba1\u7b97\u673a\u89c6\u89c9\u9738\u5360\uff0c\u8ddf IJCAI-23 \u7684\u5927\u70ed\u95e8\u4e00\u6837\uff0c\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u63d0\u4ea4\u548c\u63a5\u6536\u7684\u8bba\u6587\u6570\u91cf\u6700\u591a\uff0c\u5728\u4e00\u4f17\u9886\u57df\u4e2d\u4e00\u9a91\u7edd\u5c18\u3002<\/p>\n

\u8ba1\u7b97\u673a\u89c6\u89c9\u6709 1616 \u7bc7\u8bba\u6587\u88ab\u63d0\u4ea4\uff0c\u88ab\u5f55\u7528 135 \u7bc7\uff0c\u5f55\u7528\u7387\u8fbe\u5230 8.4%\uff0c\u7d27\u968f\u5176\u540e\u7684\u673a\u5668\u5b66\u4e60\u6709 1542 \u7bc7\u8bba\u6587\u88ab\u63d0\u4ea4\uff0c234 \u7bc7\u88ab\u5f55\u7528\uff0c\u5f55\u7528\u7387\u8fbe\u5230 15.2%\uff0c\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u4e24\u500d\u3002<\/p>\n

\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5728\u6b64\u6b21 IJCAI \u5927\u4f1a\u4e0a\u7684\u8bba\u6587\u63d0\u4ea4\u548c\u63a5\u6536\u6392\u5230\u7b2c\u4e94\uff0c\u8bba\u6587\u63d0\u4ea4 366 \u7bc7\uff0c\u63a5\u53d7 57 \u7bc7\uff0c\u5f55\u7528\u7387\u4e3a 15.6%\u3002<\/p>\n

\u800c\u6392\u5728\u7b2c\u4e09\u4f4d\u7684\u6570\u636e\u6316\u6398\u8bba\u6587\u63d0\u4ea4\u6570\u636e\u51fa\u73b0\u5927\u65ad\u5c42\uff0c\u6709 499 \u7bc7\u8bba\u6587\u88ab\u63d0\u4ea4\uff0c\u4e0d\u5230\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60\u7684\u4e09\u5206\u4e4b\u4e00\uff0c\u88ab\u5f55\u7528 71 \u7bc7\uff0c\u5f55\u7528\u7387\u4e3a 14.2%\u3002<\/p>\n

Agent \u4f5c\u4e3a\u5f53\u524d\u5927\u6a21\u578b\u843d\u5730\u7684\u672a\u6765\u8d8b\u52bf\uff0c\u6b64\u6b21 IJCAI \u4e3b\u9898\u6f14\u8bb2\u4e4b\u4e00\u4fbf\u662f\u7531\u5357\u6d0b\u7406\u5de5\u5927\u5b66\u5b89\u6ce2\u6559\u6388\u5206\u4eab\u7684\u300aFrom Algorithmic and RL-based to LLM-powered Agents\u300b\uff0c\u6307\u51fa\u4e86\u76ee\u524d\u81ea\u4e3b Agent \u9047\u5230\u7684\u95ee\u9898\u5305\u62ec\u968f\u673a\u4e14\u5f00\u653e\u7684\u73af\u5883\u3001\u5206\u5e03\u5f0f\u4f18\u5316\u7b49\u3002<\/p>\n

\u540c\u65f6\uff0c\u4e5f\u5206\u6790\u4e86\u4e0d\u540c\u65f6\u671f\u89e3\u51b3\u95ee\u9898\u7684\u9014\u5f84\uff1a\u4ece 2006 \u5e74\u5f00\u59cb\uff0c\u8fd0\u7528\u4f18\u5316\u6280\u672f\u53bb\u89e3\u51b3\u5c0f\u8303\u56f4\u95ee\u9898\uff1b2017 \u5e74\uff0c\u53ef\u4ee5\u4f7f\u7528\u5f3a\u5316\u5b66\u4e60\u7684\u65b9\u6cd5\u4e3a\u53bb\u89e3\u51b3\u7279\u5b9a\u95ee\u9898\uff1b\u76f4\u5230 2023 \u5e74\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u7684\u4f7f\u7528\u53ef\u4ee5\u53bb\u89e3\u51b3\u901a\u7528\u95ee\u9898\u3002\u8fd9\u4e00\u6f14\u8bb2\u4e5f\u7ed9 Agent \u7684\u5e95\u5c42\u67b6\u6784\u7814\u7a76\u63d0\u4f9b\u4e86\u65b0\u7684\u542f\u53d1\u4e0e\u601d\u8def\u3002<\/p>\n

\u4e4b\u5916\uff0c Agent \u5c42\u9762\u8fd8\u51fa\u73b0\u4e86\u5f88\u591a\u521b\u65b0\u6027\u7684\u5de5\u4f5c\u3002\u4e2d\u56fd\u79d1\u5b66\u9662\u5927\u5b66\u9ec4\u51ef\u5947\u56e2\u961f\uff0c\u505a\u4e86\u4ee5\u6e38\u620f\u4e3a\u573a\u666f\u7684\u00a0Multi-Agent\u00a0\u5f3a\u5316\u5b66\u4e60\u7814\u7a76\u3002\u4e0e\u8fc7\u53bb\u5b66\u672f\u754c\u504f\u9759\u6001\u7684\u591a\u6837\u6027\u7814\u7a76\u4e0d\u540c\uff0c\u8fd9\u4e00\u5de5\u4f5c\u7684\u521b\u65b0\u70b9\u5728\u4e8e\u5f15\u5165\u6a21\u5757\u5316\u7684\u667a\u80fd\u4f53\u7f51\u7edc\uff0c\u4fc3\u8fdb\u4e86\u591a\u667a\u80fd\u4f53\u6d8c\u73b0\u52a8\u6001\u81ea\u9002\u5e94\u7684\u591a\u6837\u6027\u5408\u4f5c\u8868\u73b0\u3002<\/p>\n

\u5728\u73b0\u573a\uff0cAI \u79d1\u6280\u8bc4\u8bba\u542c\u5230\u4e86\u8fd9\u6837\u7684\u4e00\u79cd\u8bc4\u8bba\uff1aIJCAI 2024 \u6709\u4e00\u4e9b\u590d\u53e4\u7684\u8272\u5f69\uff0c\u56e0\u4e3a\u5b83\u5e76\u6ca1\u6709\u5b8c\u5168\u88ab\u706b\u70ed\u7684\u5927\u6a21\u578b\u5360\u636e\uff0c\u4ecd\u7136\u6709\u5f88\u591a\u4eba\u5de5\u667a\u80fd\u9886\u57df\u4f20\u7edf\u7684\u7814\u7a76\u65b9\u5411\u548c\u8bdd\u9898\uff0c\u7ed9\u4f20\u7edf\u7814\u7a76\u63d0\u4f9b\u4e86\u4e00\u5e2d\u4e4b\u5730\u3002<\/p>\n

\u8fd9\u4e00\u7279\u70b9\u4ece\u8bae\u7a0b\u4e2d\u4e5f\u53ef\u4ee5\u7aa5\u77e5\u4e00\u4e8c\u3002\u5728 Main Confrence \u4e2d\uff0c\u6bcf\u5929\u5747\u81f3\u5c11\u8bbe\u6709\u516d\u4e2a\u5173\u4e8e ML \u7ec6\u5206\u65b9\u5411\u7684\u6f14\u8bb2\uff0c\u662f\u6240\u6709\u65b9\u5411\u4e2d\u8bba\u6587\u6570\u91cf\u6700\u591a\u7684\u4e00\u4e2a\u3002<\/p>\n

<\/a><\/p>\n

\u9664\u6b64\u4e4b\u5916\uff0c AI \u79d1\u6280\u8bc4\u8bba\u4e5f\u89c2\u5bdf\u5230 IJCAI 2024 \u6709\u5f88\u591a\u5173\u4e8e\u7eb3\u4ec0\u5747\u8861\uff08NE\uff09\u7b49\u535a\u5f08\u8bba\u7684\u5178\u578b\u7814\u7a76\u3002\u5357\u6d0b\u7406\u5de5\u5927\u5b66\u738b\u65b0\u6da6\u7b49\u4eba\u7684 \u300aReinforcement Nash Equilibrium Solver\u300b\u63d0\u51fa\u4e86\u5f3a\u5316\u7eb3\u4ec0\u5747\u8861\u6c42\u89e3\u5668\uff08RENES\uff09\uff0c\u5b83\u53ef\u4ee5\u8bad\u7ec3\u4e00\u4e2a\u5355\u4e00\u7684\u7b56\u7565\u6765\u4fee\u6539\u4e0d\u540c\u89c4\u6a21\u7684\u535a\u5f08\uff0c\u5e76\u5c06\u6c42\u89e3\u5668\u5e94\u7528\u4e8e\u4fee\u6539\u540e\u7684\u535a\u5f08\uff0c\u5728\u539f\u59cb\u535a\u5f08\u4e0a\u8bc4\u4f30\u83b7\u5f97\u7684\u89e3\u3002<\/p>\n

\u8fd9\u9879\u5de5\u4f5c\u521d\u6b65\u9a8c\u8bc1\u4e86\u5728 LLM \u4e2d\u53d6\u5f97\u6210\u529f\u7684\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u7684\u65b9\u6cd5\u53ef\u4ee5\u5728 game theory \u9886\u57df\u83b7\u5f97\u5e94\u7528\uff0c\u5e76\u4e14\u8fbe\u6210\u4e86zero-shot\u7684 \u6cdb\u5316\uff0c\u662f\u8fc8\u5411 game foundation model \u7684\u4e00\u5c0f\u6b65\u3002<\/p>\n

\u9664\u5927\u70ed\u95e8\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60\u4e4b\u5916\uff0c\u4eca\u5e74 \u00a0IJCAI \u8fd8\u65b0\u589e\u52a0\u4e86\u4e09\u4e2a\u7279\u522b\u7684\u4e3b\u9898\u9886\u57df\uff0c\u5206\u522b\u662fAI\uff0cArts\uff0cand Creativity\uff08\u4eba\u5de5\u667a\u80fd\u3001\u827a\u672f\u548c\u521b\u9020\u529b\uff09\uff0cAI for Social Good \uff08\u4eba\u5de5\u667a\u80fd\u9020\u798f\u793e\u4f1a\uff09\uff0cHuman-Centred AI\uff08\u4ee5\u4eba\u4e3a\u4e2d\u5fc3\u7684\u4eba\u5de5\u667a\u80fd\uff09\u3002<\/p>\n

\u201cAI\uff0cArts\uff0cand Creativity\u201d\u662f\u827a\u672f\u8ddfAI\u7ed3\u5408\u7684\u4ea4\u53c9\u9886\u57df\uff0c\u5305\u62ec\u827a\u672f\u521b\u4f5c\u3001\u827a\u672f\u54c1\u5206\u6790\u3001\u97f3\u4e50\u521b\u4f5c\u3001\u521b\u4e1a\u5199\u4f5c\u7b49\u65b9\u5411\u3002<\/p>\n

<\/a><\/p>\n

\u8fd9\u4e00\u4e3b\u9898\u7684\u6f14\u8bb2\u73b0\u573a\uff0c\u4eba\u6ee1\u4e3a\u60a3\uff0c\u73b0\u573a\u5ea7\u4f4d\u5bb9\u7eb3\u4e0d\u4e0b\uff0c\u4ee5\u81f3\u4e8e\u4f1a\u8bae\u5ba4\u8fc7\u9053\u3001\u95e8\u53e3\u90fd\u6324\u6ee1\u4e86\u4eba\uff0c\u73b0\u573a\u975e\u5e38\u70ed\u95f9\uff0c\u636e AI \u79d1\u6280\u8bc4\u8bba\u73b0\u573a\u89c2\u5bdf\uff0c\u4eba\u5de5\u667a\u80fd\u4e0e\u827a\u672f\u7684\u7ed3\u5408\u7684\u7814\u7a76\u5728\u6574\u4e2a IJCAI \u4e2d\u975e\u5e38\u53d7\u6b22\u8fce\u3002\u8fd9\u4e5f\u8bf4\u660e\u4e86\u5927\u6a21\u578b\u5e94\u7528\u843d\u5730\u4f9d\u7136\u662f\u5b66\u754c\u8ddf\u4ea7\u4e1a\u754c\u5171\u540c\u5173\u6ce8\u7684\u8bdd\u9898\u3002<\/p>\n

\u8fd9\u4e00\u9886\u57df\u7684\u8bdd\u9898\u5927\u591a\u56f4\u7ed5\u5f53\u4e0b\u9887\u53d7\u5173\u6ce8\u7684\u6587\u751f\u56fe\u548c\u89c6\u9891\u751f\u6210\u9886\u57df\uff0c\u63a2\u7a76\u66f4\u591a\u98ce\u683c\u7684\u751f\u6210\u4ee5\u53ca\u5982\u4f55\u505a\u51fa\u66f4\u5177\u7f8e\u611f\u7684\u89c6\u9891\u3002<\/p>\n

\u201cAI for Social Good\u201d\u662f\u6307\u5e94\u7528\u4eba\u5de5\u667a\u80fd\u6280\u672f\u89e3\u51b3\u793e\u4f1a\u6311\u6218\u5e76\u6539\u5584\u4e2a\u4eba\u548c\u793e\u533a\u7684\u798f\u7949\uff0c\u5305\u62ecGoogle \u7684 AI for Social Good \u8ba1\u5212\uff0c\u4eba\u5de5\u667a\u80fd\u5728\u5065\u5eb7\u65b9\u9762\u7684\u5e94\u7528\u7b49\u7b49\u3002<\/p>\n

“Human-centred AI” \u6307\u7684\u662f\u4ee5\u4eba\u4e3a\u4e2d\u5fc3\u7684\u4eba\u5de5\u667a\u80fd\uff0c\u8fd9\u662f\u4e00\u79cd\u8bbe\u8ba1\u548c\u5f00\u53d1\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u7684\u65b9\u6cd5\uff0c\u5f3a\u8c03\u4ee5\u7528\u6237\u7684\u9700\u6c42\u3001\u4ef7\u503c\u89c2\u548c\u4f53\u9a8c\u4e3a\u6838\u5fc3\u3002\u8fd9\u79cd\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u65e8\u5728\u589e\u5f3a\u4eba\u7c7b\u7684\u80fd\u529b\uff0c\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n

<\/p>\n

\u5199\u5728\u6700\u540e<\/strong><\/span><\/p>\n

\u5728\u672c\u6b21 IJCAI \u6709 36 \u9879 WorkShop \u5165\u9009\uff0c\u9664\u4e86\u5f53\u4e0b\u6700\u70ed\u7684\u5927\u6a21\u578b\u7814\u7a76\u4e4b\u5916\uff0c\u8fd8\u5305\u542b\u4e86\u5982\u8111\u7814\u7a76\u3001AI4\u533b\u7597\u3001AI4TS\u3001\u65e0\u4ee3\u7801\u52a9\u7406\u3001\u63a8\u8350\u7cfb\u7edf\u7b49\u591a\u4e2a\u524d\u6cbf\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u3002<\/p>\n

\u6b63\u5982 AI \u79d1\u6280\u8bc4\u8bba\u6240\u63a5\u89e6\u7684\u53c2\u4f1a\u4eba\u5458\u53cd\u9988\uff0c\u4ed6\u4eec\u5171\u540c\u7684\u611f\u53d7\u662f 2024 IJCAI \u7684\u786e\u7b26\u5408\u5b83\u7efc\u5408\u4f1a\u8bae\u7684\u5b9a\u4e49\uff0c\u672c\u6b21\u5927\u4f1a\u8986\u76d6\u8303\u56f4\u5f88\u5e7f\u3001\u975e\u5e38\u5168\u9762\u3002<\/p>\n

\u6709\u73b0\u573a\u7684\u5b66\u8005\u544a\u8bc9 AI \u79d1\u6280\u8bc4\u8bba\uff0c\u7531\u4e8e\u81ea\u5df1\u7684\u7814\u7a76\u65b9\u5411\u5e76\u975e\u662f NLP \u548c CV \uff0c\u5bfc\u81f4\u81ea\u5df1\u80fd\u6295\u7684\u5782\u76f4\u9886\u57df\u4f1a\u8bae\u8f83\u5c11\uff0c\u800c IJCAI \u662f\u4e3a\u6570\u4e0d\u591a\u9002\u5408\u6295\u7684\u9876\u4f1a\u3002<\/p>\n

\u6709\u7740 56 \u5e74\u5386\u53f2\u7684 IJCAI\uff0c\u4e00\u65b9\u9762\u5728\u66f4\u591a\u5730\u5c55\u793a\u5176\u5305\u5bb9\u6027\u4e0e\u4e30\u5bcc\u6027\uff0c\u53e6\u4e00\u65b9\u9762\uff0c\u4e5f\u5b55\u80b2\u4e86 AI \u672a\u6765\u66f4\u591a\u65b0\u7684\u53ef\u80fd\u6027\u3002<\/p>\n

\u96f7\u5cf0\u7f51\u96f7\u5cf0\u7f51\u96f7\u5cf0\u7f51(\u516c\u4f17\u53f7\uff1a\u96f7\u5cf0\u7f51)<\/span><\/span><\/p>\n

<\/p>\n

\u96f7\u5cf0\u7f51\u539f\u521b\u6587\u7ae0\uff0c\u672a\u7ecf\u6388\u6743\u7981\u6b62\u8f6c\u8f7d\u3002\u8be6\u60c5\u89c1\u8f6c\u8f7d\u987b\u77e5<\/a>\u3002<\/p>\n

\n
\n <\/a>
\n <\/a>\n <\/p>\n

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