Constraint Satisfaction Algorithm Research Articles

Constraint satisfaction algorithms: edition of timetables in the license-master-doctorate system

In this paper, we studied some algorithms for solving constraint satisfaction problem (CSP) and then applied them to solve the problem of generating schedules in a university setting. In other words, we studied the genetic algorithm, the simulated annealing, the hill climbing, a hybridization of the genetic algorithm and the simulated annealing as well as a hybridization of the genetic algorithm and the hill climbing. These algorithms have been tested on the problem of scheduling in a university environment. The hybrid uses hill climbing or simulated annealing to improve each individual in the starting population to a certain stopping point. These individuals are then sent to the genetic algorithm. Our results show that the hybridization of the genetic algorithm with a metaheuristic gives better execution time and performs better as the problem size increases compared to the classical genetic algorithm.

Constraint satisfaction algorithms: edition of timetables in the license-master-doctorate system

In this paper, we studied some algorithms for solving constraint satisfaction problem (CSP) and then applied them to solve the problem of generating schedules in a university setting. In other words, we studied the genetic algorithm, the simulated annealing, the hill climbing, a hybridization of the genetic algorithm and the simulated annealing as well as a hybridization of the genetic algorithm and the hill climbing. These algorithms have been tested on the problem of scheduling in a university environment. The hybrid uses hill climbing or simulated annealing to improve each individual in the starting population to a certain stopping point. These individuals are then sent to the genetic algorithm. Our results show that the hybridization of the genetic algorithm with a metaheuristic gives better execution time and performs better as the problem size increases compared to the classical genetic algorithm.

Packing spheres in high dimensions with moderate computational effort.

We generate nonlattice packings of spheres in up to 22 dimensions using the geometrical constraint satisfaction algorithm RRR. Our aggregated data suggest that it is easy to double the density of Ball's lower bound and, more tentatively, that the exponential decay rate of the density can be improved relative to Minkowski's longstanding 1/2.

A Learning-Based Method for Computing Control Barrier Functions of Nonlinear Systems With Control Constraints

Verifying the safety of states and designing a safety controller are very important for safety critical systems (for example, robotic and automotive systems). Since the reachable set of a state is hard to calculate online, it is difficult to determine whether the current state will enter the unsafe set in the future. Thus, the safe set of a system is usually obtained by calculating the forward invariant set offline. For control affine systems, the control barrier function provides a linear safety constraint on the control, which helps us obtain safety control online. However, the computation of control barrier functions is recognized as difficult, and existing methods make over-approximations or are only applicable to problems without control constraints. In this paper, we propose a learning-based method to compute the control barrier function for nonlinear control affine systems with control constraints. The method consists of a constraint satisfaction (CS) algorithm and a set expansion (SE) algorithm. The CS algorithm computes the control barrier function that satisfies the constraints, and the SE algorithm expands the volume of the safe set. The numerical results demonstrate that our algorithm outperforms existing methods and can improve existing control barrier functions.

Teaching Aspects of Constraint Satisfaction Algorithms Via a Game


 
 
 In an Artificial Intelligence course, a basic concept is Constraint Satisfaction (CS), which is acknowledged as a hard domain for teachers to teach and student to understand. In this paper, we present a game based learning approach to assist students in learning CS algorithms, such as arc consistency and search algorithms, for problem solving in an easy, interactive and motivating way. Preliminary valuation has showed promising results.
 
 

Information Set Generation in Partially Observable Games

We address the problem of making single-point decisions in large partially observable games, where players interleave observation, deliberation, and action. We present information set generation as a key operation needed to reason about games in this way. We show how this operation can be used to implement an existing decision-making algorithm. We develop a constraint satisfaction algorithm for performing information set generation and show that it scales better than the existing depth-first search approach on multiple non-trivial games.

Flexible Process Model Mapping using Relaxation Labeling

Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed.

Agent-Based Distributed Computing for Power System State Estimation

Centralized grid operations are now facing data storage and computation complexities more than ever. These are further compounded by communication saturation and related issues. These have instigated the transition towards distributed computing paradigm. This paper proposes a novel agent-based distributed (node-node) state estimation that overcomes the complexities of centralized operations. The proposed methodology sketches distributed constraint satisfaction algorithm that enables an agent to estimate its local representative bus’s state only by local computation and asynchronous message exchange with connected neighboring agents. The case studies demonstrate the performance, efficacy and solution accuracy of the proposed methodology by comparing it with centralized and distributed algorithms.

Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line

In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This study has been prompted by a practical problem in an international steel company in Iran. In this study, we present a new mixed integer programming model and develop a heuristic algorithm, as the commercial solvers would have difficulty in solving the problem. In our heuristic algorithm, initial solutions are obtained by a greedy constraint satisfaction algorithm, and then a local search method is developed to improve the initial solution. The experimental results tested on the data collected from the steel company show the efficiency of the proposed heuristic algorithm by solving a large-sized instance in a reasonable computation time. The average deviation between the manual method and the heuristic algorithm is 30%. Also, in all the components of the objective function, the algorithm performs better compared to the manual method. The improved values are greater than 15. In addition, we develop a commercial decision support system for the implementation of the proposed algorithm in the steel company.

Using Atomic Confining Potentials for Geometry Optimization and Vibrational Frequency Calculations in Quantum-Chemical Models of Enzyme Active Sites.

Quantum-chemical studies of enzymatic reaction mechanisms sometimes use truncated active-site models as simplified alternatives to mixed quantum mechanics molecular mechanics (QM/MM) procedures. Eliminating the MM degrees of freedom reduces the complexity of the sampling problem, but the trade-off is the need to introduce geometric constraints in order to prevent structural collapse of the model system during geometry optimizations that do not contain a full protein backbone. These constraints may impair the efficiency of the optimization, and care must be taken to avoid artifacts such as imaginary vibrational frequencies. We introduce a simple alternative in which terminal atoms of the model system are placed in soft harmonic confining potentials rather than being rigidly constrained. This modification is simple to implement and straightforward to use in vibrational frequency calculations, unlike iterative constraint-satisfaction algorithms, and allows the optimization to proceed without constraint even though the practical result is to fix the anchor atoms in space. The new approach is more efficient for optimizing minima and transition states, as compared to the use of fixed-atom constraints, and also more robust against unwanted imaginary frequencies. We illustrate the method by application to several enzymatic reaction pathways where entropy makes a significant contribution to the relevant reaction barriers. The use of confining potentials correctly describes reaction paths and facilitates calculation of both vibrational zero-point and finite-temperature entropic corrections to barrier heights.

A constraint-based, efficiency optimisation approach to network-level pavement maintenance management

Network-level pavement maintenance programming is characterised by its high computational complexity. In this article, a novel modelling to solve this problem efficiently is proposed. In this modelling, careful care has been taken to reduce the search space and formulate the original problem as one of the well-known problems of the literature on mathematical optimisation. Efficient algorithms can therefore be used to find a solution. According to this approach, the maintenance programming problem is divided into two sub-problems: (i) the first performs a reduction of the search space by filtering road section maintenance alternatives based on technical criteria; (ii) the second computes a road network-level maintenance program by optimising efficiency subject to budget constraints. The section-level filtering of alternatives is modelled as a constraint satisfaction problem and solved using appropriate constraint satisfaction algorithms. The network-level maintenance programming is modelled as a generalised assignment problem, whose resolution is well documented in the literature on optimisation. This approach has shown to be very effective, with strong computational performances for different road network sizes.

A New Perspective on the Complexity of On-board Primary Power Systems

The applicability of different selection methods for power systems on board ships is highly dependent on the number of options possible. With the introduction of new technologies in the maritime sector, this number is increasing. However, its exact value, or even its order of magnitude is still unknown. This article shows the complexity of modern on-board power system by quantifying the number of possible system configurations currently available. A constraint satisfaction algorithm generates different configurations and the results are supported by combinatorics principles. The set of constraints used led to a significant reduction in the design space, and the influence of each of the constraints is investigated independently. Traditional mechanical propulsion is present in less than 1% of the generated configurations, whereas electric and hybrid propulsion are dominant. In particular, the impact of energy storage on the size of the design space is made evident. The order of magnitude of the total number of configurations obtained supports the use of modern selection methods instead of ones based on expert intuition and past experience. The presented work offers a tool to determine and restrict the size of the design space. It can therefore be integrated into existing selection methods and is a starting point for the development of new ones. 1. Introduction Ship design has undergone drastic changes in recent years as computers are used more and more to manage the large number of design choices. Until recently, the configuration of the on-board power system has been a relatively straight forward decision because of the predominance of mechanical propulsion. However, several new technologies have been developed in recent years, most of which aim to improve the energy systems on board ships. Several more are currently under development. Modern on-board power systems may have an alternative energy source through different fuels, more complex configurations sometimes including energy storage, exhaust-gas treatment or a combination of these.

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches.

Efficient exploration of the High Entropy Alloy composition-phase space

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.

The Complexity of Bit Retrieval

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.

Story Albums: Creating Fictional Stories From Personal Photograph Sets

AbstractWe present a method for the automatic creation of fictional storybooks based on personal photographs. Unlike previous attempts that summarize such collections by picking salient or diverse photos, or creating personal literal narratives, we focus on the creation of fictional stories. This provides new value to users, as well as an engaging way for people (especially children) to experience their own photographs. We use a graph model to represent an artist‐generated story, where each node is a ‘frame’, akin to frames in comics or storyboards. A node is described by story elements, comprising actors, location, supporting objects and time. The edges in the graph encode connections between these elements and provide the discourse of the story. Based on this construction, we develop a constraint satisfaction algorithm for one‐to‐one assignment of nodes to photographs. Once each node is assigned to a photograph, a visual depiction of the story can be generated in different styles using various templates. We show results of several fictional visual stories created from different personal photo sets and in different styles.

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

Researchers have used the (calculation of phase diagram) CALPHAD method to solve the forward phase stability problem of mapping from specific thermodynamic conditions (material composition, temperature, pressure, etc.) to the associated phase constitution. Recently, optimization has been used to solve the inverse problem: mapping specific phase constitutions to the thermodynamic conditions that give rise to them. These pointwise results, however, are of limited value since they do not provide information about the forces driving the point to equilibrium. In this paper, we investigate the problem of mapping a desirable region in the phase constitution space to corresponding regions in the space of thermodynamic conditions. We term this problem the generalized inverse phase stability problem (GIPSP) and model the problem as a continuous constraint satisfaction problem (CCSP). In this paper, we propose a new CCSP algorithm tailored for the GIPSP. We investigate the performance of the algorithm on Fe–Ti binary alloy system using ThermoCalc with the TCFE7 database against a related algorithm. The algorithm is able to generate solutions for this problem with high performance.

Interval Branch-and-Bound algorithms for optimization and constraint satisfaction: a survey and prospects

Interval Branch and Bound algorithms are used to solve rigorously continuous constraint satisfaction and constrained global optimization problems. In this paper, we explain the basic principles behind interval Branch and Bound algorithms. We detail the main components and describe issues that should be considered to improve the efficiency of the algorithms.

On the subgraph epimorphism problem

In this paper we study the problem of deciding the existence of a subgraph epimorphism between two graphs. Our interest in this variant of graph matching problem stems from the study of model reductions in systems biology, where large systems of biochemical reactions can be naturally represented by bipartite digraphs of species and reactions. In this setting, model reduction can be formalized as the existence of a sequence of vertex deletion and merge operations that transforms a first reaction graph into a second graph. This problem is in turn equivalent to the existence of a subgraph (corresponding to delete operations) epimorphism (i.e. surjective homomorphism, corresponding to merge operations) from the first graph to the second. In this paper, we study theoretical properties of subgraph epimorphisms in general directed graphs. We first characterize subgraph epimorphisms (SEPI), subgraph isomorphisms (SISO) and graph epimorphisms (EPI) in terms of graph transformation operations. Then we study the graph distance measures induced by these transformations. We show that they define metrics on graphs and compare them. On the algorithmic side, we show that the SEPIexistence problem is NP-complete by reduction of SAT and present a constraint satisfaction algorithm that has been successfully used to solve practical SEPIproblems on a large benchmark of reaction graphs from systems biology.

Benefits of Distribution-Level Power Electronics for Supporting Distributed Generation Growth

It is expected that distribution networks will be required to accommodate large amounts of distributed generation (DG). Keeping power flows and voltages within their limits will require either traditional infrastructure upgrades or active compensation. The form of active compensation (e.g., series, shunt, back to back, multiterminal), quantity, and rating of the compensator should be chosen to realize the best cost-benefit ratio. Distributed-generator and compensator placement algorithms are used with a power-flow and constraint satisfaction algorithm to analyze a large number of case studies (using real U.K. network data). From these cases, assessments of compensator performance are made and summarized statistically. When considering incremental deployment across all networks, with the site of greatest benefit chosen at each increment, it is found that static synchronous compensators provide the most favorable cost-benefit ratio. In contrast, multiterminal voltage-source converters tend to provide the greatest flexibility when considering uniform deployment across all networks. It is also observed that traditional reinforcement enhances the benefits provided by active compensation.