104 research outputs found
Computing the Shapley value in allocation problems: approximations and bounds, with an application to the Italian VQR research assessment program
In allocation problems, a given set of goods are assigned to agents in such a way that the social welfare is maximised, that is, the largest possible global worth is achieved. When goods are indivisible, it is possible to use money compensation to perform a fair allocation taking into account the actual contribution of all agents to the social welfare. Coalitional games provide a formal mathematical framework to model such problems, in particular the Shapley value is a solution concept widely used for assigning worths to agents in a fair way. Unfortunately, computing this value is a #P-hard problem, so that applying this good theoretical notion is often quite difficult in real-world problems.
We describe useful properties that allow us to greatly simplify the instances of allocation problems,
without affecting the Shapley value of any player. Moreover, we propose algorithms for computing lower bounds and upper bounds of the Shapley value, which in some cases provide the exact result and that can be combined with approximation algorithms.
The proposed techniques have been implemented and tested on a real-world application of allocation problems, namely, the Italian research assessment program known as VQR (Verifica della Qualità della Ricerca, or Research Quality Assessment)1. For the large university considered in the experiments, the
problem involves thousands of agents and goods (here, researchers and their research products). The
algorithms described in the paper are able to compute the Shapley value for most of those agents, and to
get a good approximation of the Shapley value for all of the
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
Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators
[ES] Se desarrolla una técnica CSP para buscar soluciones robustas en el problema job-shop
scheduling. La técnica esta desarrollada en tres pasos. El primer paso resuelve el
problema sin tener en cuenta operadores. El segundo paso introduce las restricciones de
los operadores y obtiene soluciones teniendo en cuenta el makespan y la robustez. En el
tercer paso se mejora la robustez redistribuyendo los buffers. Para probar las robustez de
las soluciones obtenidas se aplican incidencias virtuales en las soluciones.[EN] A CSP technique have been developed for finding robust solutions in job-shop scheduling
problems with operators. The technique is developed in three steps. The first step solve
the problem without operators minimizing the makespan. The second step introduce the
operator constraints and give solutions take into account makespan and robustness. The
third step improve the robustness redistributing the buffer. Some virtual incidences are
created and to check the robustness of the solutions.Escamilla Fuster, J. (2012). Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators. http://hdl.handle.net/10251/18029Archivo delegad
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
Solving Linux Upgradeability Problems Using Boolean Optimization
Managing the software complexity of package-based systems can be regarded as
one of the main challenges in software architectures. Upgrades are required on
a short time basis and systems are expected to be reliable and consistent after
that. For each package in the system, a set of dependencies and a set of
conflicts have to be taken into account. Although this problem is
computationally hard to solve, efficient tools are required. In the best
scenario, the solutions provided should also be optimal in order to better
fulfill users requirements and expectations. This paper describes two different
tools, both based on Boolean satisfiability (SAT), for solving Linux
upgradeability problems. The problem instances used in the evaluation of these
tools were mainly obtained from real environments, and are subject to two
different lexicographic optimization criteria. The developed tools can provide
optimal solutions for many of the instances, but a few challenges remain.
Moreover, it is our understanding that this problem has many similarities with
other configuration problems, and therefore the same techniques can be used in
other domains.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)
Article in monograph or in proceedingsLeiden Inst Advanced Computer Science
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