Adaptive constraint propagation in constraint satisfaction: review and evaluation

2021 ◽  
Author(s):  
Kostas Stergiou
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Propagation

Synthesis of efficient constraint-satisfaction programs

2001 ◽  
Vol 16 (1) ◽  
pp. 69-84 ◽  
Author(s):  
STEPHEN J. WESTFOLD ◽  
DOUGLAS R. SMITH
Keyword(s):  
Constraint Satisfaction ◽  
Original Problem ◽  
Lattice Structure ◽  
Solution Space ◽  
Search Space ◽  
Constraint Propagation ◽  
Space Representation ◽  
Solution Methods ◽  
Problem Reformulation ◽  
Effective Representation

In this paper we describe the framework we have developed in KIDS (Kestrel Interactive Development System) for generating efficient constraint satisfaction programs. We have used KIDS to synthesise global search scheduling programs that have proved to be dramatically faster than other programs running the same data. We focus on the underlying ideas that lead to this efficiency. The key to the efficiency is the reduction of the size of the search space by an effective representation of sets of possible solutions (solution spaces) that allows efficient constraint propagation and pruning at the level of solution spaces. Moving to a solution space representation involves a problem reformulation. Having found a solution to the reformulated problem, an extraction phase extracts solutions to the original problem. We show how constraints from the original problem can be automatically reformulated and specialised in order to derive efficient propagation code automatically. Our solution methods exploit the semi-lattice structure of our solution spaces.

Model and Heuristic Algorithm for Steel Productive Resources Balance Problem

2012 ◽  
Vol 566 ◽  
pp. 591-596
Author(s):  
Bing Li Zhou ◽  
Qun Zhang
Keyword(s):  
Nonlinear Model ◽  
Heuristic Algorithm ◽  
Constraint Satisfaction ◽  
Constraint Propagation ◽  
Balance Problem ◽  
Variables Selection ◽  
Production Practices

A nonlinear model is proposed for balancing the steel productive resources. In the model, the sharing and the competition between products and resources are presented, and four optimization objects are introduced. According to the model and the characteristics of the problem, a heuristic algorithm based on constraint satisfaction is proposed. Variables selection is guided by the optimization objects. During the process of assigning values, constraint propagation is adopted to narrow the value domain, and back tracking is adopted to avoid the conflict with the constraints. The validity of the model and the algorithm is testified by calculating the data from production practices.

scholarly journals Early and Efficient Identification of Useless Constraint Propagation for Alldifferent Constraints

2020 ◽  
Author(s):  
Xizhe Zhang ◽  
Jian Gao ◽  
Yizhi Lv ◽  
Weixiong Zhang
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Propagation ◽  
Constraint Satisfaction Problems ◽  
Benchmark Problem ◽  
Value Assignment ◽  
Arc Consistency ◽  
A Value ◽  
Removable Edge ◽  
Problem Instances ◽  
State Of Art

Constraints propagation and backtracking are two basic techniques for solving constraint satisfaction problems (CSPs). During the search for a solution, the variable and value pairs that do not belong to any solution can be discarded by constraint propagation to ensure generalized arc consistency so as to avoid the fruitless search. However, constraint propagation is frequently invoked often with little effect on many CSPs. Much effort has been devoted to predicting when to invoke constraint propagation for solving a CSP; however, no effective approach has been developed for the alldifferent constraint. Here we present a novel theorem for identifying the edges in a value graph of alldifferent constraint whose removal can significantly reduce useless constraint propagation. We prove that if an alternating cycle exists for a prospectively removable edge that represents a variable-value assignment, the edge (and the assignment) can be discarded without constraint propagation. Based on this theorem, we developed a novel optimizing technique for early detection of useless constraint propagation which can be incorporated in any existing algorithm for alldifferent constraint. Our implementation of the new method achieved speedup by a factor of 1-5 over the state-of-art approaches on 93 benchmark problem instances in 8 domains. Furthermore, the new algorithm is scalable well and runs increasingly faster than the existing methods on larger problems.

scholarly journals A CHR-based implementation of known arc-consistency

2005 ◽  
Vol 5 (4-5) ◽  
pp. 419-440 ◽  
Author(s):  
MARCO ALBERTI ◽  
MARCO GAVANELLI ◽  
EVELINA LAMMA ◽  
PAOLA MELLO ◽  
MICHELA MILANO
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
External Environment ◽  
Computation Time ◽  
Constraint Propagation ◽  
Constraint Handling ◽  
Declarative Language ◽  
Propagation Process ◽  
Arc Consistency ◽  
High Level

In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use. For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model has been proposed (Cucchiara et al. 1999a) as an extension of the CSP model, to make it possible to start constraint propagation even when domains are not fully known, performing acquisition of domain elements only when necessary, and without the need for restarting the propagation after every acquisition. In this paper, we show how a solver for the two sorted CLP language, defined in previous work (Gavanelli et al. 2005) to express ICSPs, has been implemented in the Constraint Handling Rules (CHR) language, a declarative language particularly suitable for high level implementation of constraint solvers.

Principles of Constraint Processing

2008 ◽  
pp. 63-106
Author(s):  
Roman Barták
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problems ◽  
Constraint Propagation ◽  
Discrete Mathematics ◽  
Full Potential ◽  
Modeling Framework ◽  
Depth First Search ◽  
Combinatorial Optimization Problems ◽  
Look Ahead ◽  
Variable Ordering

Solving combinatorial optimization problems such as planning, scheduling, design, or configuration is a non-trivial task being attacked by many solving techniques. Constraint satisfaction, that emerged from AI research and nowadays integrates techniques from areas such as operations research and discrete mathematics, provides a natural modeling framework for description of such problems supported by general solving technology. Though it is a mature area now, surprisingly many researchers outside the CSP community do not use the full potential of constraint satisfaction and frequently confuse constraint satisfaction and simple enumeration. This chapter gives an introduction to mainstream constraint satisfaction techniques available in existing constraint solvers and answers the question “How does constraint satisfaction work?”. The focus of the chapter is on techniques of constraint propagation, depth-first search, and their integration. It explains backtracking, its drawbacks, and how to remove these drawbacks by methods such as backjumping and backmarking. Then, the focus is on consistency techniques; it explains methods such as arc and path consistency and introduces consistencies of higher level. It also presents how consistency techniques are integrated with depth-first search algorithms in a look-ahead concept and what value and variable ordering heuristics are available there. Finally, techniques for optimization with constraints are presented.

Constraint Satisfaction for Planning and Scheduling

2005 ◽  
pp. 320-353 ◽  
Author(s):  
Roman Bartak
Keyword(s):  
Constraint Satisfaction ◽  
Resource Constraints ◽  
Constraint Propagation ◽  
Scheduling Problems ◽  
Specific Time ◽  
Planning And Scheduling ◽  
Search Techniques ◽  
Basic Approaches

As the current planning and scheduling technologies are coming together by assuming time and resource constraints in planning or by allowing introduction of new activities during scheduling, the role of constraint satisfaction as the bridging technology is increasing and so it is important for researchers in these areas to understand the underlying principles and techniques. The chapter introduces constraint satisfaction technology with emphasis on its applications in planning and scheduling. It gives a brief survey of constraint satisfaction in general, including a description of mainstream solving techniques, that is, constraint propagation combined with search. Then, it focuses on specific time and resource constraints and on search techniques and heuristics useful in planning and scheduling. Last but not least, the basic approaches to constraint modelling for planning and scheduling problems are presented.

scholarly journals Constraint Satisfaction for Motion Feasibility Checking

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Seokjun Lee ◽  
Incheol Kim
Keyword(s):  
Motion Planning ◽  
Constraint Satisfaction ◽  
Humanoid Robot ◽  
Sampling Method ◽  
Search Strategy ◽  
Constraint Propagation ◽  
Research Field ◽  
Robotic Manipulation ◽  
Efficient Search ◽  
Search Heuristics

Task and motion planning (TAMP) is a key research field for robotic manipulation tasks. The goal of TAMP is to generate motion-feasible task plan automatically. Existing methods for checking motion feasibility of task plan skeletons have some limitations of semantic-free pose candidate sampling, weak search heuristics, and early value commitment. In order to overcome these limitations, we propose a novel constraint satisfaction framework for checking motion feasibility of task plan skeletons. Our framework provides (1) a semantic pose candidate sampling method, (2) novel variable and constraint ordering heuristics based on intra- and inter-action dependencies in a task plan skeleton, and (3) an efficient search strategy using constraint propagation. Based upon these techniques, our framework can improve the efficiency of motion feasibility checking for TAMP. From experiments using the humanoid robot PR2, we show that the motion feasibility checking in our framework is 1.4x to 6.0x faster than previous ones.

scholarly journals Localization of a Vehicle: A Dynamic Interval Constraint Satisfaction Problem-Based Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Kangni Kueviakoe ◽  
Zhan Wang ◽  
Alain Lambert ◽  
Emmanuelle Frenoux ◽  
Philippe Tarroux
Keyword(s):  
Kalman Filter ◽  
Bayesian Methods ◽  
Constraint Satisfaction ◽  
Measurement Errors ◽  
Constraint Satisfaction Problem ◽  
Constraint Propagation ◽  
Gps Receiver ◽  
Localization Problem ◽  
Vehicle Localization ◽  
Interval Constraint

This paper introduces a new interval constraint propagation (ICP) approach dealing with the real-time vehicle localization problem. Bayesian methods like extended Kalman filter (EKF) are classically used to achieve vehicle localization. ICP is an alternative which provides guaranteed localization results rather than probabilities. Our approach assumes that all models and measurement errors are bounded within known limits without any other hypotheses on the probability distribution. The proposed algorithm uses a low-level consistency algorithm and has been validated with an outdoor vehicle equipped with a GPS receiver, a gyro, and odometers. Results have been compared to EKF and other ICP methods such as hull consistency (HC4) and 3-bound (3B) algorithms. Both consistencies of EKF and our algorithm have been experimentally studied.

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