scholarly journals Implied Constraints for Automaton Constraints

10.29007/m767 ◽  
2018 ◽  
Author(s):  
Maria Andreina Francisco ◽  
Pierre Flener ◽  
Justin Pearson
Keyword(s):  
Constraint Programming ◽  
Specific Choice ◽  
Inference Algorithms ◽  
Line Process ◽  
Implied Constraints ◽  
High Level ◽  
Process Constraints ◽  
Automated Tool

Automata allow many constraints on sequences of variables to be specified in a high-level way for constraint programming solvers. An automaton with accumulators induces a decomposition of the specified constraint into a conjunction of constraints with existing inference algorithms, called propagators. Towards improving propagation, we design a fully automated tool that selects, in an off-line process, constraints that are implied by such a decomposition. We show that a suitable problem-specific choice among the tool-selected implied constraints can considerably improve solving time and propagation, both on a decomposition in isolation and on entire constraint problems containing the decomposition.

scholarly journals From Support Propagation to Belief Propagation in Constraint Programming

2019 ◽  
Vol 66 ◽  
Author(s):  
Gilles Pesant
Keyword(s):  
Constraint Programming ◽  
Belief Propagation ◽  
Combinatorial Problems ◽  
Global Constraints ◽  
Inference Algorithms ◽  
Shared Variables ◽  
Shared Information ◽  
Individual Variables ◽  
High Level ◽  
Search Guidance

The distinctive driving force of constraint programming to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. From that exposed structure in the form of so-called global constraints, powerful inference algorithms have shared information between constraints by propagating it through shared variables’ domains, traditionally by removing unsupported values. This paper investigates a richer propagation medium made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. It generalizes standard support propagation and aims to converge to the true marginal distributions of the solutions over individual variables. Its advantage over standard belief propagation is that the higher-level models featuring large-arity (global) constraints do not tend to create as many cycles, which are known to be problematic for convergence. The necessary architectural changes to a constraint programming solver are described and an empirical study of the proposal is conducted on its implementation. We find that it provides close approximations to the true marginals and that it significantly improves search guidance.

NENOK — A SOFTWARE ARCHITECTURE FOR GENERIC INFERENCE

2010 ◽  
Vol 19 (01) ◽  
pp. 65-99 ◽  
Author(s):  
MARC POULY
Keyword(s):  
Programming Languages ◽  
Software Library ◽  
Inference Process ◽  
Software Projects ◽  
Open Problems ◽  
Constraint Systems ◽  
Fast Prototyping ◽  
Inference Algorithms ◽  
Key Competences ◽  
High Level

Computing inference from a given knowledgebase is one of the key competences of computer science. Therefore, numerous formalisms and specialized inference routines have been introduced and implemented for this task. Typical examples are Bayesian networks, constraint systems or different kinds of logic. It is known today that these formalisms can be unified under a common algebraic roof called valuation algebra. Based on this system, generic inference algorithms for the processing of arbitrary valuation algebras can be defined. Researchers benefit from this high level of abstraction to address open problems independently of the underlying formalism. It is therefore all the more astonishing that this theory did not find its way into concrete software projects. Indeed, all modern programming languages for example provide generic sorting procedures, but generic inference algorithms are still mythical creatures. NENOK breaks a new ground and offers an extensive library of generic inference tools based on the valuation algebra framework. All methods are implemented as distributed algorithms that process local and remote knowledgebases in a transparent manner. Besides its main purpose as software library, NENOK also provides a sophisticated graphical user interface to inspect the inference process and the involved graphical structures. This can be used for educational purposes but also as a fast prototyping architecture for inference formalisms.

scholarly journals Automated monitoring system for events detection in sewer network by distribution temperature sensing data measurement

2018 ◽  
Vol 78 (7) ◽  
pp. 1499-1508
Author(s):  
Abdelhak Kessili ◽  
Jes Vollertsen ◽  
Asbjørn Haaning Nielsen
Keyword(s):  
Large Data ◽  
Temperature Sensing ◽  
Wastewater Discharge ◽  
Sewer Pipe ◽  
Temperature Changes ◽  
Set Up ◽  
Comparison Of The Results ◽  
Events Detection ◽  
High Level ◽  
Automated Tool

Abstract This study is related to distribution temperature sensing (DTS) in sewers for tracing illicit or unintended inflows to foul sewers. A DTS measurement is performed with a fiber optic cable that is installed at the invert of a sewer pipe in combination with a standalone laser/computer instrument. This set-up generates in-sewer temperature measurements with high resolutions in time (every minute) and space (every metre) along the cable over long periods of time (weeks on end). The prolonged monitoring period in combination with the high level of detail in the dataset allows the study of anomalies (i.e., unexpected temperatures and/or temperature variations at certain locations), even if these only occur very infrequently. The objective of this paper is to develop an automated tool to analyze the large data masses and identify anomalies caused by illicit or unintended inflows. In this study, an algorithm for detecting the temperature changes that are caused by both wastewater discharge and inflow of stormwater are developed. A comparison of the results of the automated procedure to the results of a manual assessment of the datasets (Elmehaven, Denmark) shows that the automated procedure performs very well.

scholarly journals Constraint Problem Specification as Compression

10.29007/7ths ◽  
2018 ◽  
Author(s):  
Steve Prestwich ◽  
S. Armagan Tarim ◽  
Roberto Rossi
Keyword(s):  
Logic Programming ◽  
Data Compression ◽  
Constraint Programming ◽  
Auxiliary Problem ◽  
Combinatorial Problems ◽  
Specification Languages ◽  
Constraint Problem ◽  
High Level ◽  
Natural Way ◽  
Comparable Size

Constraint Programming is a powerful and expressive framework for modelling and solving combinatorial problems. It is nevertheless not always easy to use, which has led to the development of high-level specification languages. We show that Constraint Logic Programming can be used as a meta-language to describe itself more compactly at a higher level of abstraction. This can produce problem descriptions of comparable size to those in existing specification languages, via techniques similar to those used in data compression. An advantage over existing specification languages is that, for a problem whose specification requires the solution of an auxiliary problem, a single specification can unify the two problems. Moreover, using a symbolic representation of domain values leads to a natural way of modelling channelling constraints.

ECLiPSe – From LP to CLP

2011 ◽  
Vol 12 (1-2) ◽  
pp. 127-156 ◽  
Author(s):  
JOACHIM SCHIMPF ◽  
KISH SHEN
Keyword(s):  
Logic Programming ◽  
Constraint Programming ◽  
Combinatorial Problem ◽  
Third Party ◽  
Programming System ◽  
Development Environment ◽  
Control Language ◽  
Extended Prolog ◽  
High Level ◽  
And Control

AbstractECLiPSe is a Prolog-based programming system, aimed at the development and deployment of constraint programming applications. It is also used for teaching most aspects of combinatorial problem solving, for example, problem modelling, constraint programming, mathematical programming and search techniques. It uses an extended Prolog as its high-level modelling and control language, complemented by several constraint solver libraries, interfaces to third-party solvers, an integrated development environment and interfaces for embedding into host environments. This paper discusses language extensions, implementation aspects, components, and tools that we consider relevant on the way from Logic Programming to Constraint Logic Programming.

scholarly journals From high-level inference algorithms to efficient code

2019 ◽  
Vol 3 (ICFP) ◽  
pp. 1-30 ◽  
Author(s):  
Rajan Walia ◽  
Praveen Narayanan ◽  
Jacques Carette ◽  
Sam Tobin-Hochstadt ◽  
Chung-chieh Shan
Keyword(s):  
Inference Algorithms ◽  
Efficient Code ◽  
High Level

scholarly journals A new constraint programming model and solving for the cyclic hoist scheduling problem

Constraints ◽  
2020 ◽  
Vol 25 (3-4) ◽  
pp. 319-337 ◽  
Author(s):  
Mark Wallace ◽  
Neil Yorke-Smith
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
State Of The Art ◽  
Scale Up ◽  
Generic Model ◽  
Scheduling Problem ◽  
Hoist Scheduling ◽  
Wide Range ◽  
Constraint Programming Model ◽  
High Level

AbstractThe cyclic hoist scheduling problem (CHSP) is a well-studied optimisation problem due to its importance in industry. Despite the wide range of solving techniques applied to the CHSP and its variants, the models have remained complicated and inflexible, or have failed to scale up with larger problem instances. This article re-examines modelling of the CHSP and proposes a new simple, flexible constraint programming formulation. We compare current state-of-the-art solving technologies on this formulation, and show that modelling in a high-level constraint language, MiniZinc, leads to both a simple, generic model and to computational results that outperform the state of the art. We further demonstrate that combining integer programming and lazy clause generation, using the multiple cores of modern processors, has potential to improve over either solving approach alone.

Constraint Lingo: towards high-level constraint programming

2004 ◽  
Vol 34 (15) ◽  
pp. 1481-1504 ◽  
Author(s):  
Raphael Finkel ◽  
Victor W. Marek ◽  
Miros?aw Truszczy?ski
Keyword(s):  
Constraint Programming ◽  
High Level

scholarly journals A New QoS Routing Northbound Interface for SDN

2017 ◽  
Vol 5 (1) ◽  
pp. 92-115
Author(s):  
Siamak Layeghy ◽  
Farzaneh Pakzad ◽  
Marius Portmann
Keyword(s):  
Constraint Programming ◽  
Traffic Engineering ◽  
Qos Routing ◽  
Modelling Language ◽  
Routing Problem ◽  
Programming Techniques ◽  
Network Topologies ◽  
High Level ◽  
Basic Constraint ◽  
High Level Abstraction

In this paper, we introduce SCOR (Software-defined Constrained Optimal Routing), a new Software Defined Networking (SDN) Northbound Interface for QoS routing and traffic engineering. SCOR is based on constraint-programming techniques and is implemented in the MiniZinc modelling language. It provides a powerful, high-level abstraction layer, consisting of 10 basic constraint-programming predicates. A key feature of SCOR is that it is declarative, where only the constraints and utility function of the routing problem need to be expressed, and the complexity of solving the problem is hidden from the user, and handled by a powerful generic solver. We show that the interface (set of predicates) of SCOR is sufficiently expressive to handle all the known and relevant QoS routing problems. We further demonstrate the practicality and scalability of the approach via a number of example scenarios, with varying network topologies, network sizes and number of flows.

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