Active Chemical Sensing With Partially Observable Markov Decision Processes
نویسندگان
چکیده
We present an active-perception strategy to optimize the temperature program of metal-oxide sensors in real time, as the sensor reacts with its environment. We model the problem as a partially observable Markov decision process (POMDP), where actions correspond to measurements at particular temperatures, and the agent is to find a temperature sequence that minimizes the Bayes risk. We validate the method on a binary classification problem with a simulated sensor. Our results show that the method provides a balance between classification rate and sensing costs.
منابع مشابه
Partially observable Markov decision processes
For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov...
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