Exploring centralized and distributed constraint propagation algorithms for solving constraint satisfaction problems

University of Technology Sydney. Faculty of Engineering and Information Technology.Constraint Satisfaction Problem (CSP) is widely used in Artificial Intelligence (AI), such as temporal planning, vehicle routing, scheduling, and spatial reasoning. Constraint propagation is central to the process of solving a CSP. It can be used to solve several large tractable classes of CSPs directly and it is also predominantly used to reduce the space of combinations that will be explored by a search algorithm. Constraint propagation, also known as local consistency enforcing, is the process of reasoning over the given explicit local constraints to discover new implicit constraints and then make them explicit. Designing efficient local consistency algorithms is a central research question in constraint processing. Another related important question is to find large tractable classes that can be solved by enforcing local consistency. This thesis extends the related works in the literature and explores centralized and distributed local consistency algorithms for solving CSPs in several dimensions. On the one hand, we explore centralized local consistency algorithms to identify more general tractable constraint subclasses and to solve tractable constraint subclasses more efficiently. On the other hand, we design more efficient distributed local consistency algorithms to solve tractable constraint classes and to filter inconsistent tuples for distributed CSP solvers

Exploring directional path-consistency for solving constraint networks

© The British Computer Society 2017. All rights reserved. Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes unnecessary. Directional PC (DPC) is a weaker notion of PC that considers a given variable ordering and can thus be enforced more efficiently than PC. This paper shows that (the DPC enforcing algorithm of Dechter and Pearl) decides the constraint satisfaction problem (CSP) of a constraint language if it is complete and has the variable elimination property (VEP). However, we also show that no complete VEP constraint language can have a domain with more than two values. We then present a simple variant of the algorithm, called, and show that the CSP of a constraint language can be decided by if it is closed under a majority operation. In fact, is sufficient for guaranteeing backtrack-free search for such constraint networks. Examples of majority-closed constraint classes include the classes of connected row-convex constraints and tree-preserving constraints, which have found applications in various domains, such as scene labeling, temporal reasoning, geometric reasoning and logical filtering. Our experimental evaluations show that significantly outperforms the stateof-the-Art algorithms for solving majority-closed constraints