Constraint logic programming for fault-tolerant distributed systems

This paper presents key notions of Constraint Logic Programming (CLP), which is a young programming paradigm oriented toward solving difficult discrete highly combinatorial problems by making active use of constraints on the basis of mechanisms of Logic Programming. Being the subject of intensive research all over the world, CLP has already been used successfully in a large variety of application areas. As one of the important applications where CLP demonstrates its potential, we propose CLP-based procedures of solving the problems of optimal resource and task allocation at the stages of design and operation of Fault-Tolerant Distributed Technical Systems.Peer Reviewe

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

E-GENET: a stochastic constraint solver.

by Won, Hon Wing Stephen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 95-101).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Constraint Satisfaction Problem --- p.1Chapter 1.2 --- CSP Solving Techniques --- p.2Chapter 1.3 --- Motivation of the Dissertation --- p.4Chapter 1.4 --- Overview of the Dissertation --- p.6Chapter 2 --- Related Work --- p.8Chapter 2.1 --- Heuristic Repair Method --- p.8Chapter 2.2 --- GSAT --- p.8Chapter 2.3 --- GENET --- p.9Chapter 2.4 --- Simulated Annealing --- p.9Chapter 2.5 --- Genetic Algorithms --- p.10Chapter 3 --- Overview of GENET --- p.11Chapter 3.1 --- Network Architecture --- p.11Chapter 3.2 --- Convergence Procedure --- p.12Chapter 3.3 --- The illegal and atmost Constraints --- p.13Chapter 3.3.1 --- The illegal Constraint --- p.14Chapter 3.3.2 --- The atmost Constraint --- p.14Chapter 3.4 --- General Non-Binary Constraints --- p.15Chapter 3.4.1 --- Constraint Transformation --- p.15Chapter 3.4.2 --- Using the illegal Constraints --- p.17Chapter 3.4.3 --- Problem Transformation --- p.17Chapter 4 --- An Extended GENET --- p.20Chapter 4.1 --- Network Architecture --- p.20Chapter 4.2 --- Convergence Procedure --- p.22Chapter 4.3 --- E-GENET as a Generalization of GENET --- p.24Chapter 4.3.1 --- Constraints --- p.30Chapter 4.3.2 --- Network Architecture --- p.31Chapter 4.4 --- Properties of E-GENET --- p.32Chapter 4.4.1 --- Incompleteness of E-GENET --- p.33Chapter 4.4.2 --- Making E-GENET Complete --- p.36Chapter 4.5 --- Storage Scheme --- p.38Chapter 4.5.1 --- The illegal and atmost Constraint --- p.39Chapter 4.5.2 --- The Disequality Constraint --- p.39Chapter 4.5.3 --- General Constraints --- p.40Chapter 4.6 --- Benchmarking Results --- p.40Chapter 4.6.1 --- The Graph-Coloring Problem --- p.41Chapter 4.6.2 --- The N-queens Problem --- p.42Chapter 4.6.3 --- The Car-Sequencing Problem --- p.43Chapter 4.6.4 --- The Cryptarithmetic Problem --- p.44Chapter 4.6.5 --- The Hamiltonian Path Problem --- p.45Chapter 5 --- Optimizations to E-GENET --- p.47Chapter 5.1 --- Inadequacies of E-GENET --- p.47Chapter 5.1.1 --- Cumbrous Constraint Node --- p.48Chapter 5.1.2 --- Inefficiency of the min-conflicts heuristic --- p.48Chapter 5.2 --- Optimizations --- p.50Chapter 5.2.1 --- Intermediate Node --- p.50Chapter 5.2.2 --- New Assignment Scheme of Initial Penalty Values --- p.55Chapter 5.2.3 --- Concept of Contribution --- p.57Chapter 5.2.4 --- Learning Heuristic --- p.62Chapter 6 --- A Comprehensive Constraint Library --- p.63Chapter 6.1 --- Elementary Constraints --- p.64Chapter 6.1.1 --- Linear Arithmetic Constraints --- p.64Chapter 6.1.2 --- The atmost Constraint --- p.66Chapter 6.1.3 --- Disjunctive Constraints --- p.69Chapter 6.2 --- Global Constraints --- p.71Chapter 6.2.1 --- The cumulative Constraint --- p.72Chapter 6.2.2 --- The among Constraint --- p.77Chapter 6.2.3 --- The diffn Constraint --- p.82Chapter 6.2.4 --- The cycle Constraint --- p.85Chapter 7 --- Conclusion --- p.89Chapter 7.1 --- Contributions --- p.89Chapter 7.2 --- Discussions --- p.90Chapter 7.3 --- Future Work --- p.94Bibliography --- p.9

AUTOMATED SCHEDULE GENERATION AND ANALYSIS FROM A CONSTRUCTION REQUIREMENT PERSPECTIVE

Ph.DDOCTOR OF PHILOSOPH

Constraint propagation in Mozart

This thesis presents constraint propagation in Mozart which is based on computational agents called propagators. The thesis designs, implements, and evaluates propagator-based propagation engines. A propagation engine is split up in generic propagation services and domain specific domain solvers which are connected by a constraint programming interface. Propagators use filters to perform constraint propagation. The interface isolates filters from propagators such that they can be shared among various systems. This thesis presents the design and implementation of a finite integer set domainsolver for Mozart which reasons over bound and cardinality approximations of sets.The solver cooperates with a finite domain solver to improve its propagation and expressiveness. This thesis promotes constraints to first-class citizens and thus, provides extra control over constraints. Novel programming techniques taking advantage of the first-class status of constraints are developed and illustrated.Diese Dissertation beschreibt Constraint-Propagierung in Mozart, die auf Berechnungsagenten, Propagierer genannt, basiert. Die Dissertation entwirft, implementiert und evaluiert Propagierer-basierte Propagierungsmaschinen. Eine Propagierungsmaschine ist aufgeteilt in generische Propagierungsdienste und domänenspezifische Domänenlöser, die durch eine Schnittstelle zur Constraint-Programmierung miteinander verbunden sind. Propagierer benutzen Filter, um Constraints zu propagieren. Die Schnittstelle isoliert Filter von Propagierern, so dass Programmkodes von Filtern von verschiedenen Systemen genutzt werden können. Diese Dissertation präsentiert den Entwurf und die Implementierung eines Domänenlösers über endliche Mengen von ganzen Zahlen für Mozart, die über Mengen- und Kardinalitätsschranken approximiert werden. Dieser kooperiert mit einem Löser über endlichen Bereichen, um die Propagierung und die Ausdrucksfähigkeit zu verbessern. Diese Dissertation erhebt Constraints zu emanzipierten Datenstrukturen und stellt auf dieseWeise zusätzliche Steuerungsmöglichkeiten über Constraints zur Verfügung. Des Weiteren werden neuartige Programmiertechniken für emanzipierte Constraints entwickelt und demonstriert

Constraint propagation in Mozart

This thesis presents constraint propagation in Mozart which is based on computational agents called propagators. The thesis designs, implements, and evaluates propagator-based propagation engines. A propagation engine is split up in generic propagation services and domain specific domain solvers which are connected by a constraint programming interface. Propagators use filters to perform constraint propagation. The interface isolates filters from propagators such that they can be shared among various systems. This thesis presents the design and implementation of a finite integer set domainsolver for Mozart which reasons over bound and cardinality approximations of sets.The solver cooperates with a finite domain solver to improve its propagation and expressiveness. This thesis promotes constraints to first-class citizens and thus, provides extra control over constraints. Novel programming techniques taking advantage of the first-class status of constraints are developed and illustrated.Diese Dissertation beschreibt Constraint-Propagierung in Mozart, die auf Berechnungsagenten, Propagierer genannt, basiert. Die Dissertation entwirft, implementiert und evaluiert Propagierer-basierte Propagierungsmaschinen. Eine Propagierungsmaschine ist aufgeteilt in generische Propagierungsdienste und domänenspezifische Domänenlöser, die durch eine Schnittstelle zur Constraint-Programmierung miteinander verbunden sind. Propagierer benutzen Filter, um Constraints zu propagieren. Die Schnittstelle isoliert Filter von Propagierern, so dass Programmkodes von Filtern von verschiedenen Systemen genutzt werden können. Diese Dissertation präsentiert den Entwurf und die Implementierung eines Domänenlösers über endliche Mengen von ganzen Zahlen für Mozart, die über Mengen- und Kardinalitätsschranken approximiert werden. Dieser kooperiert mit einem Löser über endlichen Bereichen, um die Propagierung und die Ausdrucksfähigkeit zu verbessern. Diese Dissertation erhebt Constraints zu emanzipierten Datenstrukturen und stellt auf dieseWeise zusätzliche Steuerungsmöglichkeiten über Constraints zur Verfügung. Des Weiteren werden neuartige Programmiertechniken für emanzipierte Constraints entwickelt und demonstriert

Solving Real-Life Hydroinformatics Problems with Operations Research and Artificial Intelligence

Many real life problems in the hydraulic engineering literature can be modelled as constrained optimisation problems. Often, they are addressed in the literature through genetic algorithms, although other techniques have been proposed. In this thesis, we address two of these real life problems through a variety of techniques taken from the Artificial Intelligence and Operations Research areas, such as mixed-integer linear programming, logic programming, genetic algorithms and path relinking, together with hybridization amongst these technologies and with hydraulic simulators. For the first time, an Answer Set Programming formulation of hydroinformatics problems is proposed. The two real life problems addressed hereby are the optimisation of the response in case of contamination events, and the optimisation of the positioning of the isolation valves. The constraints of the former describe the feasible region of the Multiple Travelling Salesman Problem, while the objective function is computed by a hydraulic simulator. A simulation–optimisation approach based on Genetic Algorithms, mathematical programming, and Path Relinking, and a thorough experimental analysis are discussed hereby. The constraints of the latter problem describe a graph partitioning enriched with a maximum flow, and it is a new variant of the common graph partitioning. A new mathematical model plus a new formalization in logic programming are discussed in this work. In particular, the technologies adopted are mixed-integer linear programming and Answer Set Programming. Addressing these two real applications in hydraulic engineering as constrained optimisation problems has allowed for i) computing applicable solutions to the real case, ii) computing better solutions than the ones proposed in the hydraulic literature, iii) exploiting graph theory for modellization and solving purposes, iv) solving the problems by well suited technologies in Operations Research and Artificial Intelligence, and v) designing new integrated and hybrid architectures for a more effective solving

Reasoning about Goal-Plan Trees in Autonomous Agents: Development of Petri net and Constraint-Based Approaches with Resulting Performance Comparisons

Multi-agent systems and autonomous agents are becoming increasingly important in current computing technology. In many applications, the agents are often asked to achieve multiple goals individually or within teams where the distribution of these goals may be negotiated among the agents. It is expected that agents should be capable of working towards achieving all its currently adopted goals concurrently. However, in doing so, the goals can interact both constructively and destructively with each other, so a rational agent must be able to reason about these interactions and any other constraints that may be imposed on them, such as the limited availability of resources that could affect their ability to achieve all adopted goals when pursuing them concurrently. Currently, agent development languages require the developer to manually identify and handle these circumstances. In this thesis, we develop two approaches for reasoning about the interactions between the goals of an individual agent. The first of these employs Petri nets to represent and reason about the goals, while the second uses constraint satisfaction techniques to find efficient ways of achieving the goals. Three types of reasoning are incorporated into these models: reasoning about consumable resources where the availability of the resources is limited; the constructive interaction of goals whereby a single plan can be used to achieve multiple goals; and the interleaving of steps for achieving different goals that could cause one or more goals to fail. Experimental evaluation of the two approaches under various different circumstances highlights the benefits of the reasoning developed here whilst also identifying areas where one approach provides better results than the other. This can then be applied to suggest the underlying technique used to implement the reasoning that the agent may want to employ based on the goals it has been assigned

Βελτιστοποίηση επιχειρησιακών διαδικασιών βασισμένη σε προγραμματισμό περιορισμών

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.During the last two decades a change occurred in the dialectics of Business Administration science about the principles that should rule the organizational structure of a company. A structural model based on business processes that run through the business entity horizontally and/or vertically, is gradually replacing the traditional strategy that stratifies the functional entity in distinct and isolated hierarchical levels. A business process can be defined as a set of coordinated activities aiming at the successful realization of a business goal and the production of surplus value. Therefore, the design and the implementation of a process can significantly affect the efficiency of an organization's operation and the quality that the consumer receives from the produced outcome. The significance of planning and managing the business processes for the survival of a company in the modern globalized economic environment, demonstrates the need for the development of computational systems that can assist the effort for the improvement or re-engineering of processes in regard to quantitative and qualitative criteria. Considering that the constraint logic programming can offer the computational means for the confrontation of complex combinatorial problems, the aim of this thesis is to design and develop a system that can compose optimized complex business processes from simple tasks based on predefined evaluation criteria. The proposed system is consisted by four collaborating software modules. The database that stores the simple tasks in the appropriate quantitative representation for the optimization procedure. The graphical user interface (GUI) that accepts the required input by the decision maker and creates the constraint logic program using the programming language Prolog. The ECLiPSe platform executes the logic program to determine the feasible processes for specific input and output resource data sets, and construct the processes with the optimum evaluation. Finally, the visualization subsystem translates the quantitative representation of the optimized business processes into a graphical form