Learning World Models in Environments with Manifest Causal Structure
This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containin...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/6777 |