129 research outputs found
Automated Design of Elevator Systems: Experimenting with Constraint-Based Approaches
System configuration and design is a well-established topic
in AI. While many successful applications exists, there are still areas of
manufacturing where AI techniques find little or no application. We focus
on one such area, namely building and installation of elevator systems,
for which we are developing an automated design and configuration tool.
The questions that we address in this paper are: (i) What are the best
ways to encode some subtasks of elevator design into constraint-based
representations? (ii) What are the best tools available to solve the encodings? We contribute an empirical analysis to address these questions
in our domain of interest, as well as the complete set of benchmarks to
foster further researc
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
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