Performance of a Single Action Partially Observable Markov Decision Process in a Recognition Task
نویسندگان
چکیده
Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to basic recognition tasks, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Markov Decision Process (SA-POMDP) is then compared to a well-known pattern recognizer, the Hidden Markov Model (HMM). Our results indicate that the SA-POMDP’s performance surpasses that of the HMM in simple recognition tasks and exhibits a unique resistance to noisy inputs during the recognition process.
منابع مشابه
Performance of a Single Action POMDP in a Recognition Task
Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to a basic recognition task, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Ma...
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