THE ASSUMPTIONS ON KNOWLEDGE AND RESOURCES IN MODELS OF RATIONALITYPEI WANG Temple University, Philadelphia, USA Intelligence can be understood as a form of rationality, in the sense that an intelligent system does its best when its knowledge and resources are insufficient with respect to the problems to be solved. The traditional models of rationality typically assume some form of sufficiency of knowledge and resources, so cannot solve many theoretical and practical problems in Artificial Intelligence (AI). New models based on the Assumption of Insufficient Knowledge and Resources (AIKR) cannot be obtained by minor revisions or extensions of the traditional models, and have to be established fully according to the restrictions and freedoms provided by AIKR. The practice of NARS, an AI project, shows that such new models are feasible and promising in providing a new theoretical foundation for the study of rationality, intelligence, consciousness, and mind. Keywords: Rationality; intelligence; adaptation; logic; probability; computation Cited by (2): Alexander Toschev, Max Talanov, Salvatore Distefano. 2016. Evolution of Thinking Models in Automatic Incident Processing Systems. Agent and Multi-Agent Systems: Technology and Applications, 271-280. [CrossRef] Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink. 2016. Why Artificial Intelligence Needs a Task Theory. Artificial General Intelligence, 118-128. [CrossRef] Access to the content you have requested requires one of the following:
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