113,929 research outputs found
Adaptive Parallel Iterative Deepening Search
Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications
Convergence-Optimal Quantizer Design of Distributed Contraction-based Iterative Algorithms with Quantized Message Passing
In this paper, we study the convergence behavior of distributed iterative
algorithms with quantized message passing. We first introduce general iterative
function evaluation algorithms for solving fixed point problems distributively.
We then analyze the convergence of the distributed algorithms, e.g. Jacobi
scheme and Gauss-Seidel scheme, under the quantized message passing. Based on
the closed-form convergence performance derived, we propose two quantizer
designs, namely the time invariant convergence-optimal quantizer (TICOQ) and
the time varying convergence-optimal quantizer (TVCOQ), to minimize the effect
of the quantization error on the convergence. We also study the tradeoff
between the convergence error and message passing overhead for both TICOQ and
TVCOQ. As an example, we apply the TICOQ and TVCOQ designs to the iterative
waterfilling algorithm of MIMO interference game.Comment: 17 pages, 9 figures, Transaction on Signal Processing, accepte
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