Learning prediction and abstraction in partially observable models
Markov models have been a keystone in Artificial Intelligence for many decades. However, they remain unsatisfactory when the environment modeled is partially observable. There are pathological examples where no history of fixed length is sufficient for accurate deci- sion making. On the other han...
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| Format: | Others |
| Language: | en |
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McGill University
2007
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| Online Access: | http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18471 |