A component-based parallel constraint solver

As a case study that illustrates our view on coordination and component-based software engineering, we present the design and implementation of a parallel constraint solver. The parallel solver coordinates autonomous instances of a sequential constraint solver, which is used as a software component. The component solvers achieve load balancing of tree search through a time-out mechanism. Experiments show that the purely exogenous mode of coordination employed here yields a viable parallel solver that effectively reduces turn-around time for constraint solving on a broad range of hardware platforms

Combining search strategies for distributed constraint satisfaction.

Many real-life problems such as distributed meeting scheduling, mobile frequency allocation and resource allocation can be solved using multi-agent paradigms. Distributed constraint satisfaction problems (DisCSPs) is a framework for describing such problems in terms of related subproblems, called a complex local problem (CLP), which are dispersed over a number of locations, each with its own constraints on the values their variables can take. An agent knows the variables in its CLP plus the variables (and their current value) which are directly related to one of its own variables and the constraints relating them. It knows little about the rest of the problem. Thus, each CLP is solved by an agent which cooperates with other agents to solve the overall problem. Algorithms for solving DisCSPs can be classified as either systematic or local search with the former being complete and the latter incomplete. The algorithms generally assume that each agent has only one variable as they can solve DisCSP with CLPs using virtual agents. However, in large DisCSPs where it is appropriate to trade completeness off against timeliness, systematic search algorithms can be expensive when compared to local search algorithms which generally converge quicker to a solution (if a solution is found) when compared to systematic algorithms. A major drawback of local search algorithms is getting stuck at local optima. Significant researches have focused on heuristics which can be used in an attempt to either escape or avoid local optima. This thesis makes significant contributions to local search algorithms for DisCSPs. Firstly, we present a novel combination of heuristics in DynAPP (Dynamic Agent Prioritisation with Penalties), which is a distributed synchronous local search algorithm for solving DisCSPs having one variable per agent. DynAPP combines penalties on values and dynamic agent prioritisation heuristics to escape local optima. Secondly, we develop a divide and conquer approach that handles DisCSP with CLPs by exploiting the structure of the problem. The divide and conquer approach prioritises the finding of variable instantiations which satisfy the constraints between agents which are often more expensive to satisfy when compared to constraints within an agent. The approach also exploits concurrency and combines the following search strategies: (i) both systematic and local searches; (ii) both centralised and distributed searches; and (iii) a modified compilation strategy. We also present an algorithm that implements the divide and conquer approach in Multi-DCA (Divide and Conquer Algorithm for Agents with CLPs). DynAPP and Multi-DCA were evaluated on several benchmark problems and compared to the leading algorithms for DisCSPs and DisCSPs with CLPs respectively. The results show that at the region of difficult problems, combining search heuristics and exploiting problem structure in distributed constraint satisfaction achieve significant benefits (i.e. generally used less computational time and communication costs) over existing competing methods

Towards 40 years of constraint reasoning

Research on constraints started in the early 1970s. We are approaching 40 years since the beginning of this successful field, and it is an opportunity to revise what has been reached. This paper is a personal view of the accomplishments in this field. We summarize the main achievements along three dimensions: constraint solving, modelling and programming. We devote special attention to constraint solving, covering popular topics such as search, inference (especially arc consistency), combination of search and inference, symmetry exploitation, global constraints and extensions to the classical model. For space reasons, several topics have been deliberately omitted.Partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya grant 2009-SGR-1434.Peer Reviewe

Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques

A rolling horizon approach for the locomotive routing problem at the Canadian National Railway Company

Cette thèse étudie le problème du routage des locomotives qui se pose à la Compagnie des chemins de fer nationaux du Canada (CN) - le plus grand chemin de fer au Canada en termes de revenus et de taille physique de son réseau ferroviaire. Le problème vise à déterminer la séquence des activités de chaque locomotive sur un horizon de planification donné. Dans ce contexte, il faut prendre des décisions liées à l'affectation de locomotives aux trains planifiés en tenant compte des besoins d'entretien des locomotives. D’autres décisions traitant l'envoi de locomotives aux gares par mouvements à vide, les déplacements légers (sans tirer des wagons) et la location de locomotives tierces doivent également être prises en compte. Sur la base d'une formulation de programmation en nombres entiers et d'un réseau espace-temps présentés dans la littérature, nous introduisons une approche par horizon roulant pour trouver des solutions sous-optimales de ce problème dans un temps de calcul acceptable. Une formulation mathématique et un réseau espace-temps issus de la littérature sont adaptés à notre problème. Nous introduisons un nouveau type d'arcs pour le réseau et de nouvelles contraintes pour le modèle pour faire face aux problèmes qui se posent lors de la division de l'horizon de planification en plus petits morceaux. Les expériences numériques sur des instances réelles montrent les avantages et les inconvénients de notre algorithme par rapport à une approche exacte.This thesis addresses the locomotive routing problem arising at the Canadian National Railway Company (CN) - the largest railway in Canada in terms of both revenue and the physical size of its rail network. The problem aims to determine the sequence of activities for each locomotive over the planning horizon. Besides assigning locomotives to scheduled trains and considering scheduled locomotive maintenance requirements, the problem also includes other decisions, such as sending locomotives to stations by deadheading, light traveling, and leasing of third-party locomotives. Based on an Integer Programming formulation and a Time-Expanded Network presented in the literature, we introduce a Rolling Horizon Approach (RHA) as a method to find near-optimal solutions of this problem in acceptable computing time. We adapt a mathematical formulation and a space-time network from the literature. We introduce a new type of arcs for the network and new constraints for the model to cope with issues arising when dividing the planning horizon into smaller ones. Computational experiments on real-life instances show the pros and cons of our algorithm when compared to an exact solution approach

Capturing functional and non-functional connector

The CONNECT Integrated Project aims to develop a novel networking infrastructure that will support composition of networked systems with on-the-fly connector synthesis. The role of this work package is to investigate the foundations and verification methods for composable connectors. In this deliverable, we set the scene for the formulation of the modelling framework by surveying existing connector modelling formalisms. We covered not only classical connector algebra formalisms, but also, where appropriate, their corresponding quantitative extensions. All formalisms have been evaluated against a set of key dimensions of interest agreed upon in the CONNECT project. Based on these investigations, we concluded that none of the modelling formalisms available at present satisfy our eight dimensions. We will use the outcome of the survey to guide the formulation of a compositional modelling formalism tailored to the specific requirements of the CONNECT project. Furthermore, we considered the range of non-functional properties that are of interest to CONNECT, and reviewed existing specification formalisms for capturing them, together with the corresponding modelchecking algorithms and tool support. Consequently, we described the scientific advances concerning model-checking algorithms and tools, which are partial contribution towards future deliverables: an approach for online verification (part of D2.2), automated abstraction-refinement for probabilistic realtime systems (part of D2.2 and D2.4), and compositional probabilistic verification within PRISM, to serve as a foundation of future research on quantitative assume-guarantee compositional reasoning (part of D2.2 and D2.4)

Distributed constraint satisfaction for coordinating and integrating a large-scale, heterogeneous enterprise

Market forces are continuously driving public and private organisations towards higher productivity, shorter process and production times, and fewer labour hours. To cope with these changes, organisations are adopting new organisational models of coordination and cooperation that increase their flexibility, consistency, efficiency, productivity and profit margins. In this thesis an organisational model of coordination and cooperation is examined using a real life example; the technical integration of a distributed large-scale project of an international physics collaboration. The distributed resource constraint project scheduling problem is modelled and solved with the methods of distributed constraint satisfaction. A distributed local search method, the distributed breakout algorithm (DisBO), is used as the basis for the coordination scheme. The efficiency of the local search method is improved by extending it with an incremental problem solving scheme with variable ordering. The scheme is implemented as central algorithm, incremental breakout algorithm (IncBO), and as distributed algorithm, distributed incremental breakout algorithm (DisIncBO). In both cases, strong performance gains are observed for solving underconstrained problems. Distributed local search algorithms are incomplete and lack a termination guarantee. When problems contain hard or unsolvable subproblems and are tightly or overconstrained, local search falls into infinite cycles without explanation. A scheme is developed that identifies hard or unsolvable subproblems and orders these to size. This scheme is based on the constraint weight information generated by the breakout algorithm during search. This information, combined with the graph structure, is used to derive a fail first variable order. Empirical results show that the derived variable order is 'perfect'. When it guides simple backtracking, exceptionally hard problems do not occur, and, when problems are unsolvable, the fail depth is always the shortest. Two hybrid algorithms, BOBT and BOBT-SUSP are developed. When the problem is unsolvable, BOBT returns the minimal subproblem within the search scope and BOBT-SUSP returns the smallest unsolvable subproblem using a powerful weight sum constraint. A distributed hybrid algorithm (DisBOBT) is developed that combines DisBO with DisBT. The distributed hybrid algorithm first attempts to solve the problem with DisBO. If no solution is available after a bounded number of breakouts, DisBO is terminated, and DisBT solves the problem. DisBT is guided by a distributed variable order that is derived from the constraint weight information and the graph structure. The variable order is incrementally established, every time the partial solution needs to be extended, the next variable within the order is identified. Empirical results show strong performance gains, especially when problems are overconstrained and contain small unsolvable subproblems

Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Planning and Decision Making

Left-turning at unsignalized intersection is one of the most challenging tasks for urban automated driving, due to the various shapes of different intersections, and rapidly changing nature of the driving scenarios. Many algorithms including rule-based approach, graph-based approach, optimization-based approach, etc. have been developed to overcome the problems. However, most algorithms implemented were difficult to guarantee the safety at intersection scenarios in real time due to the large uncertainty of the intersections. Other algorithms that aim to always keep a safe distance in all cases often become overly conservative, which might also be dangerous and inefficient. This thesis addresses this challenge by proposing a generalized critical turning point (CTP) based hierarchical decision making and planning method, which enables safe and efficient planning and decision making of autonomous vehicles. The high-level candidate-paths planner takes the road map information and generates CTPs using a parameterized CTP extraction model which is proposed and verified by naturalistic driving data. CTP is a novel concept and the corresponding CTP model is used to generate behavior-oriented paths that adapt to various intersections. These modifications help to assure the high searching efficiency of the planning process, and in the meanwhile, enable human-like driving behavior of the autonomous vehicle. The low-level planner formulates the decision-making task to a POMDP problem which considers the uncertainties of the agent in the intersections. The POMDP problem is then solved with a Monte Carlo tree search (MCTS)-based framework to select proper candidate paths and decide the actions on that path. The proposed framework that uses CTPs is tested in several critical scenarios and has out-performed the methods of not using CTPs. The framework has shown the ability to adapt to various shapes of intersections with different numbers of road lanes and different width of median strips, and finishes the left turns while keeping proper safety distances. The uses of the CTP concept which is proposed through human-driving left-turning behaviors, enables the framework to perform human-like behaviors that is easier to be speculated by the other agents of the intersection, which improves the safety of the ego vehicle too. The framework is also capable of personalized modification of the desired real-time performance and the corresponding stability. The use of the POMDP model which considers the unknown intentions of the surrounding vehicles has also enabled the framework to provide commute-efficient two-dimensional planning and decision-making. In all, the proposed framework enables the ego vehicle to perform less conservative and human-like actions while considering the potential of crashes in real-time, which not only improves the commute-efficiency, but also enables urban driving autonomous vehicles to naturally integrate into scenarios with human-driven vehicles in a friendly manne