David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
traction from reinforcement learners It addresses two ap proaches towards knowledge extraction the extraction of ex plicit symbolic rules from neural reinforcement learners and the extraction of complete plans from such learners The advantages of such knowledge extraction include the improvement of learning especially with the rule extraction approach and the improvement of the usability of re sults of learning..
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Christopher Cox, Christopher Manning & Pat Langley, Template Sampling for Leveraging Domain Knowledge in Information Extraction.
Ron Sun, Supplementing Neural Reinforcement Learning with Symbolic Methods Possibilities and Challenges.
Added to index2009-06-13
Total downloads8 ( #389,504 of 1,796,529 )
Recent downloads (6 months)1 ( #466,501 of 1,796,529 )
How can I increase my downloads?