281 research outputs found
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
Synthesizing Multiple Boolean Functions using Interpolation on a Single Proof
It is often difficult to correctly implement a Boolean controller for a
complex system, especially when concurrency is involved. Yet, it may be easy to
formally specify a controller. For instance, for a pipelined processor it
suffices to state that the visible behavior of the pipelined system should be
identical to a non-pipelined reference system (Burch-Dill paradigm). We present
a novel procedure to efficiently synthesize multiple Boolean control signals
from a specification given as a quantified first-order formula (with a specific
quantifier structure). Our approach uses uninterpreted functions to abstract
details of the design. We construct an unsatisfiable SMT formula from the given
specification. Then, from just one proof of unsatisfiability, we use a variant
of Craig interpolation to compute multiple coordinated interpolants that
implement the Boolean control signals. Our method avoids iterative learning and
back-substitution of the control functions. We applied our approach to
synthesize a controller for a simple two-stage pipelined processor, and present
first experimental results.Comment: This paper originally appeared in FMCAD 2013,
http://www.cs.utexas.edu/users/hunt/FMCAD/FMCAD13/index.shtml. This version
includes an appendix that is missing in the conference versio
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning
algorithms for quantified Boolean formulas through deep reinforcement learning.
We focus on a backtracking search algorithm, which can already solve formulas
of impressive size - up to hundreds of thousands of variables. The main
challenge is to find a representation of these formulas that lends itself to
making predictions in a scalable way. For a family of challenging problems, we
learned a heuristic that solves significantly more formulas compared to the
existing handwritten heuristics
05241 Abstracts Collection -- Synthesis and Planning
From 12.06.05 to 17.06.2005 the Dagstuhl Seminar 05241 ``Synthesis and Planning\u27\u27
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Partial Quantifier Elimination By Certificate Clauses
We study partial quantifier elimination (PQE) for propositional CNF formulas.
In contrast to full quantifier elimination, in PQE, one can limit the set of
clauses taken out of the scope of quantifiers to a small subset of target
clauses. The appeal of PQE is twofold. First, PQE can be dramatically simpler
than full quantifier elimination. Second, it provides a language for performing
incremental computations. Many verification problems (e.g. equivalence checking
and model checking) are inherently incremental and so can be solved in terms of
PQE. Our approach is based on deriving clauses depending only on unquantified
variables that make the target clauses . Proving redundancy
of a target clause is done by construction of a ``certificate'' clause implying
the former. We describe a PQE algorithm called that employs
the approach above. We apply to generating properties of a
design implementation that are not implied by specification. The existence of
an property means that this implementation is buggy. Our
experiments with HWMCC-13 benchmarks suggest that can be used
for generating properties of real-life designs
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