AI textbooks and papers often discuss the big ques-
\ntions, such as how to reason with uncertainty”, how
\nto reason e ciently”, or how to improve performance
\nthrough learning.” It is more di cult, however, to nd
\ndescriptions of concrete problems or challenges that are
\nstill ambitious and interesting, yet not so open-ended.
\nThe goal of this panel is to formulate a set of such
\nchallenge problems for the eld. Each panelist was
\nasked to formulate one or more challenges. The em-
\nphasis is on problems for which there is a good chance
\nthat they will be resolved within the next ve to ten
\nyears.<\/p>\n","protected":false},"excerpt":{"rendered":"
AI textbooks and papers often discuss the big ques- tions, such as how to reason with uncertainty”, how to reason e ciently”, or how to improve performance through learning.” It is more di cult, however, to nd descriptions of concrete problems or challenges that are still ambitious and interesting, yet not so open-ended. The goal […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of AAAI-96, Thirteenth National Conference on Artificial Intelligence, Portland, Oregon, August 1996. 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