10,607 research outputs found
Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price
Given (1) a set of clauses in some first-order language and (2)
a cost function , mapping each
ground atom in the Herbrand base to a non-negative real, then
the problem of finding a minimal cost Herbrand model is to either find a
Herbrand model of which is guaranteed to minimise the sum of the
costs of true ground atoms, or establish that there is no Herbrand model for
. A branch-cut-and-price integer programming (IP) approach to solving this
problem is presented. Since the number of ground instantiations of clauses and
the size of the Herbrand base are both infinite in general, we add the
corresponding IP constraints and IP variables `on the fly' via `cutting' and
`pricing' respectively. In the special case of a finite Herbrand base we show
that adding all IP variables and constraints from the outset can be
advantageous, showing that a challenging Markov logic network MAP problem can
be solved in this way if encoded appropriately
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs
Optimal power allocation for orthogonal frequency division multiplexing
(OFDM) wiretap channels with Gaussian channel inputs has already been studied
in some previous works from an information theoretical viewpoint. However,
these results are not sufficient for practical system design. One reason is
that discrete channel inputs, such as quadrature amplitude modulation (QAM)
signals, instead of Gaussian channel inputs, are deployed in current practical
wireless systems to maintain moderate peak transmission power and receiver
complexity. In this paper, we investigate the power allocation and artificial
noise design for OFDM wiretap channels with discrete channel inputs. We first
prove that the secrecy rate function for discrete channel inputs is nonconcave
with respect to the transmission power. To resolve the corresponding nonconvex
secrecy rate maximization problem, we develop a low-complexity power allocation
algorithm, which yields a duality gap diminishing in the order of
O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that
independent frequency-domain artificial noise cannot improve the secrecy rate
of single-antenna wiretap channels. Towards this end, we propose a novel
time-domain artificial noise design which exploits temporal degrees of freedom
provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and
boost the secrecy rate even with a single antenna at the transmitter}.
Numerical results are provided to illustrate the performance of the proposed
design schemes.Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Wireless
Communications, Jan. 201
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