Abstract
It is commonplace in artificial intelligence to draw a distinction between the explicit knowledge appearing in an agent's memory and the implicit knowledge it represents. Many AI theories of knowledge assume this representation relation is logical, that is, that implicit knowledge is derived from explicit knowledge via a logic. Such theories, however, are limited in their ability to treat incomplete or inconsistent knowledge in useful ways. We suggest that a more illuminating theory of implicit knowledge is that it is the result of rational representation, in which the agent rationally (in the sense of decision theory) chooses interpretations of its explicit knowledge. This research was supported by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, monitored by the Air Force Avionics Laboratory under Contract F33615-87-C-1499. The views and conclusions contained in this document are those of the author, and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Government of the United States of America.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
