Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Humans as an I-POMDP   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 \emph{programming} to be learned is captured by random
variables in the agent's state space, all \emph{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
\emph{signals}, and iv. the anticipated production of complex beneficial
interactions. The proposed formulation, its benefits, and several open
questions are presented.

3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci