5,776 research outputs found
Logic learning and optimized drawing: two hard combinatorial problems
Nowadays, information extraction from large datasets is a recurring operation in countless fields of applications. The purpose leading this thesis is to ideally follow the data flow along its journey, describing some hard combinatorial problems that arise from two key processes, one consecutive to the other: information extraction and representation. The approaches here considered will focus mainly on metaheuristic algorithms, to address the need for fast and effective optimization methods. The problems studied include data extraction instances, as Supervised Learning in Logic Domains and the Max Cut-Clique Problem, as well as two different Graph Drawing Problems. Moreover, stemming from these main topics, other additional themes will be discussed, namely two different approaches to handle Information Variability in Combinatorial Optimization Problems (COPs), and Topology Optimization of lightweight concrete structures
Toward an automaton Constraint for Local Search
We explore the idea of using finite automata to implement new constraints for
local search (this is already a successful technique in constraint-based global
search). We show how it is possible to maintain incrementally the violations of
a constraint and its decision variables from an automaton that describes a
ground checker for that constraint. We establish the practicality of our
approach idea on real-life personnel rostering problems, and show that it is
competitive with the approach of [Pralong, 2007]
Induction of Topological Environment Maps from Sequences of Visited Places
In this paper we address the problem of topologically mapping environments which contain inherent perceptual aliasing caused by repeated environment structures. We propose an approach that does not use motion or odometric information but only a sequence of deterministic measurements observed by traversing an environment. Our algorithm implements a stochastic local search to build a small map which is consistent with local adjacency information extracted from a sequence of observations. Moreover, local adjacency information is incorporated to disambiguate places which are physically different but appear identical to the robots senses. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that it infers a small map quickly
Integrating Conflict Driven Clause Learning to Local Search
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions
ASAP: An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner out-
performs all the others in every known benchmark domain. This observation motivates
the idea of selecting different planning algorithms for different domains. Moreover, the
planners’ performances are affected by the structure of the search space, which depends
on the encoding of the considered domain. In many domains, the performance of a plan-
ner can be improved by exploiting additional knowledge, for instance, in the form of
macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for
Planning that: (i) for a given domain initially learns additional knowledge, in the form
of macro-operators and entanglements, which is used for creating different encodings
of the given planning domain and problems, and (ii) explores the 2 dimensional space
of available algorithms, defined as encodings–planners couples, and then (iii) selects the
most promising algorithm for optimising either the runtimes or the quality of the solution
plans
Approximation Strategies for Incomplete MaxSAT
Incomplete MaxSAT solving aims to quickly find a solution
that attempts to minimize the sum of the weights of the unsati
sfied soft
clauses without providing any optimality guarantees. In th
is paper, we
propose two approximation strategies for improving incomp
lete MaxSAT
solving. In one of the strategies, we cluster the weights and
approximate
them with a representative weight. In another strategy, we b
reak up
the problem of minimizing the sum of weights of unsatisfiable
clauses
into multiple minimization subproblems. Experimental res
ults show that
approximation strategies can be used to find better solution
s than the
best incomplete solvers in the MaxSAT Evaluation 2017
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