27,994 research outputs found
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
The optimization of algorithm (hyper-)parameters is crucial for achieving
peak performance across a wide range of domains, ranging from deep neural
networks to solvers for hard combinatorial problems. The resulting algorithm
configuration (AC) problem has attracted much attention from the machine
learning community. However, the proper evaluation of new AC procedures is
hindered by two key hurdles. First, AC benchmarks are hard to set up. Second
and even more significantly, they are computationally expensive: a single run
of an AC procedure involves many costly runs of the target algorithm whose
performance is to be optimized in a given AC benchmark scenario. One common
workaround is to optimize cheap-to-evaluate artificial benchmark functions
(e.g., Branin) instead of actual algorithms; however, these have different
properties than realistic AC problems. Here, we propose an alternative
benchmarking approach that is similarly cheap to evaluate but much closer to
the original AC problem: replacing expensive benchmarks by surrogate benchmarks
constructed from AC benchmarks. These surrogate benchmarks approximate the
response surface corresponding to true target algorithm performance using a
regression model, and the original and surrogate benchmark share the same
(hyper-)parameter space. In our experiments, we construct and evaluate
surrogate benchmarks for hyperparameter optimization as well as for AC problems
that involve performance optimization of solvers for hard combinatorial
problems, drawing training data from the runs of existing AC procedures. We
show that our surrogate benchmarks capture overall important characteristics of
the AC scenarios, such as high- and low-performing regions, from which they
were derived, while being much easier to use and orders of magnitude cheaper to
evaluate
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning
Motivated by what is required for real-time path planning, the paper starts
out by presenting sRMPD, a new recursive "local" planner founded on the key
notion that, unless made necessary by an obstacle, there must be no deviation
from the shortest path between any two points, which would normally be a
straight line path in the configuration space. Subsequently, we increase the
power of sRMPD by using it as a "connect" subroutine call in a higher-level
sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence,
mRMPD spawns a larger number of space exploring trees in regions of the
configuration space that are characterized by a higher density of obstacles.
The overall effect is a hybrid tree growing strategy with a trade-off between
random exploration as made possible by multi-RRT based logic and immediate
exploitation of opportunities to connect two states as made possible by sRMPD.
The mRMPD planner can be biased with regard to this trade-off for solving
different kinds of planning problems efficiently. Based on the test cases we
have run, our experiments show that mRMPD can reduce planning time by up to 80%
compared to basic RRT
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitful applications in a number of domains have resulted in a large amount
of data, but the community lacks a standard format or repository for this data.
This situation makes it difficult to share and compare different approaches
effectively, as is done in other, more established fields. It also
unnecessarily hinders new researchers who want to work in this area. To address
this problem, we introduce a standardized format for representing algorithm
selection scenarios and a repository that contains a growing number of data
sets from the literature. Our format has been designed to be able to express a
wide variety of different scenarios. Demonstrating the breadth and power of our
platform, we describe a set of example experiments that build and evaluate
algorithm selection models through a common interface. The results display the
potential of algorithm selection to achieve significant performance
improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014
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