Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Verification of Markov Decision Processes using Learning Algorithms   We present a general framework for applying machine-learning algorithms to
the verification of Markov decision processes (MDPs). The primary goal of these
techniques is to improve performance by avoiding an exhaustive exploration of
the state space. Our framework focuses on probabilistic reachability, which is
a core property for verification, and is illustrated through two distinct
instantiations. The first assumes that full knowledge of the MDP is available,
and performs a heuristic-driven partial exploration of the model, yielding
precise lower and upper bounds on the required probability. The second tackles
the case where we may only sample the MDP, and yields probabilistic guarantees,
again in terms of both the lower and upper bounds, which provides efficient
stopping criteria for the approximation. The latter is the first extension of
statistical model-checking for unbounded properties in MDPs. In contrast with
other related approaches, we do not restrict our attention to time-bounded
(finite-horizon) or discounted properties, nor assume any particular properties
of the MDP. We also show how our techniques extend to LTL objectives. We
present experimental results showing the performance of our framework on
several examples.

27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga