Learning from Humans as an I-POMDP
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The interactive partially observable Markov decision process (I-POMDP) is a recently developed framework which extends the POMDP to the multi-agent setting by including agent models in the state space. This paper argues for formulating the problem of an agent learning interactively from a human teacher as an I-POMDP, where the agent programming to be learned is captured by random variables in the agent's state space, all signals from the human teacher are treated as observed random variables, and the human teacher, modeled as a distinct agent, is explicitly represented in the agent's state space. The main benefits of this approach are: i. a principled action selection mechanism, ii. a principled belief update mechanism, iii. support for the most common teacher signals, and iv. the anticipated production of complex beneficial interactions. The proposed formulation, its benefits, and several open questions are presented.
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