ゲノム情報科学研究教育機構  アブストラクト
Date June 9, 2006
Speaker Dr. Gyorgy Turan, University of Illinois at Chicago, Chicago, USA and Hungarian Academy of Sciences
Title Remarks on learning and commonsense reasoning
Abstract   Providing agents with commonsense reasoning capability is a fundamental task of artificial intelligence, and it is also important for applications such as user interfaces and natural language processing. Commonsense reasoning is a huge area with interesting mathematical theories. It is often pointed out that the existing frameworks should be extended to include learning. We give a brief introduction to this direction of research, formulating some open problems. A point of entry into the many-faceted area of commonsense reasoning and learning is belief revision, the study of how to revise a knowledge base if new information is received that may be inconsistent with what is known. Here one usually begins with postulates required of a rational revision process,b such as the AGM postulates, aimed at formalizing the requirement of minimal change. There are representation results, constructions (akin to learning algorithms) and connections to probabilistic reasoning. It seems to be a challenging general question whether successful learning and rational revision can be combined. So far, this has been considered mostly in inductive inference, but it is also discussed in machine learning.
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