5,038 research outputs found
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]
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of constraints. The aim of this paper is to investigate the
use of Constraint Programming (CP) to model and mine sequential patterns in a
sequence database. Our CP approach offers a natural way to simultaneously
combine in a same framework a large set of constraints coming from various
origins. Experiments show the feasibility and the interest of our approach
A Compositional Framework for Preference-Aware Agents
A formal description of a Cyber-Physical system should include a rigorous
specification of the computational and physical components involved, as well as
their interaction. Such a description, thus, lends itself to a compositional
model where every module in the model specifies the behavior of a
(computational or physical) component or the interaction between different
components. We propose a framework based on Soft Constraint Automata that
facilitates the component-wise description of such systems and includes the
tools necessary to compose subsystems in a meaningful way, to yield a
description of the entire system. Most importantly, Soft Constraint Automata
allow the description and composition of components' preferences as well as
environmental constraints in a uniform fashion. We illustrate the utility of
our framework using a detailed description of a patrolling robot, while
highlighting methods of composition as well as possible techniques to employ
them.Comment: In Proceedings V2CPS-16, arXiv:1612.0402
On empirical methodology, constraints, and hierarchy in artificial grammar learning
This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling longâdistance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns
Multi-dimensional Boltzmann Sampling of Languages
This paper addresses the uniform random generation of words from a
context-free language (over an alphabet of size ), while constraining every
letter to a targeted frequency of occurrence. Our approach consists in a
multidimensional extension of Boltzmann samplers \cite{Duchon2004}. We show
that, under mostly \emph{strong-connectivity} hypotheses, our samplers return a
word of size in and exact frequency in
expected time. Moreover, if we accept tolerance
intervals of width in for the number of occurrences of each
letters, our samplers perform an approximate-size generation of words in
expected time. We illustrate these techniques on the
generation of Tetris tessellations with uniform statistics in the different
types of tetraminoes.Comment: 12p
Information and the reconstruction of quantum physics
The reconstruction of quantum physics has been connected with the interpretation of the quantum formalism, and has continued to be so with the recent deeper consideration of the relation of information to quantum states and processes. This recent form of reconstruction has mainly involved conceiving quantum theory on the basis of informational principles, providing new perspectives on physical correlations and entanglement that can be used to encode information. By contrast to the traditional, interpretational approach to the foundations of quantum mechanics, which attempts directly to establish the meaning of the elements of the theory and often touches on metaphysical issues, the newer, more purely reconstructive approach sometimes defers this task, focusing instead on the mathematical derivation of the theoretical apparatus from simple principles or axioms. In its most pure form, this sort of theory reconstruction is fundamentally the mathematical derivation of the elements of theory from explicitly presented, often operational principles involving a minimum of extraâmathematical content. Here, a representative series of specifically informationâbased treatmentsâfrom partial reconstructions that make connections with information to rigorous axiomatizations, including those involving the theories of generalized probability and abstract systemsâis reviewed.Accepted manuscrip
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