Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Symbolic Models of Stochastic Domains   In this article, we work towards the goal of developing agents that can learn
to act in complex worlds. We develop a probabilistic, relational planning rule
representation that compactly models noisy, nondeterministic action effects,
and show how such rules can be effectively learned. Through experiments in
simple planning domains and a 3D simulated blocks world with realistic physics,
we demonstrate that this learning algorithm allows agents to effectively model
world dynamics.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th