4,515 research outputs found
Conditional constraint satisfaction and configuration: A win-win proposition
Over the years, a whole sector of AI dealing with configuration problems has emerged, and since 1996, an annual configuration workshop has been held in affiliation with a major AI conference. This installment of Trends & Controversies presents essays from the configuration workshop held in August 2006 as part of ECAI in Riva del Garda, Italy
Intelligent search strategies based on adaptive Constraint Handling Rules
The most advanced implementation of adaptive constraint processing with
Constraint Handling Rules (CHR) allows the application of intelligent search
strategies to solve Constraint Satisfaction Problems (CSP). This presentation
compares an improved version of conflict-directed backjumping and two variants
of dynamic backtracking with respect to chronological backtracking on some of
the AIM instances which are a benchmark set of random 3-SAT problems. A CHR
implementation of a Boolean constraint solver combined with these different
search strategies in Java is thus being compared with a CHR implementation of
the same Boolean constraint solver combined with chronological backtracking in
SICStus Prolog. This comparison shows that the addition of ``intelligence'' to
the search process may reduce the number of search steps dramatically.
Furthermore, the runtime of their Java implementations is in most cases faster
than the implementations of chronological backtracking. More specifically,
conflict-directed backjumping is even faster than the SICStus Prolog
implementation of chronological backtracking, although our Java implementation
of CHR lacks the optimisations made in the SICStus Prolog system. To appear in
Theory and Practice of Logic Programming (TPLP).Comment: Number of pages: 27 Number of figures: 14 Number of Tables:
A constraint-based framework for configuration
The research presented here aims at providing a comprehensive framework for solving configuration problems, based on the Constraint Satisfaction paradigm. This thesis is addressing the two main issues raised by a configuration task: modeling the problem and solving it efficiently. Our approach subsumes previous approaches, incorporating both Simplification and further extension, offering increased representational power and efficiency.
Modeling. We advance the idea of local, context independent models for the types of objects in the application domain, and show how the model of an artifact can be built as a composition of local models of the constituent parts. Our modeling technique integrates two mechanisms for dealing with complexity, namely composition and abstraction. Using concepts such as locality, aggregation and inheritance, it offers support and guidance as to the appropriate content and organization of the domain knowledge, thus making knowledge specification and representation less error prone, and knowledge maintenance much easier.
There are two specific aspects which make modeling configuration problems challenging: the complexity and heterogeneity of relations that must be expressed, manipulated and maintained, and the dynamic nature of the configuration process. We address these issues by introducing Composite Constraint Satisfaction Problems, a new, nonstandard class of problems which extends the classic Constraint Satisfaction paradigm.
Efficiency. For the purpose of the work presented here, we are only interested in providing a guaranteed optimal solution to a configuration problem. To achieve this goal, our research focused on two complementary directions.
The first one led to a powerful search algorithm called Maintaining Arc Consistency Extended (MACE). By maintaining arc consistency and taking advantage of the problem structure, MACE turned out to be one of the best general purpose CSP search algorithms to date.
The second research direction aimed at reducing the search effort involved in proving the optimality of the proposed solution by making use of information which is specific to individual configuration problems. By adding redundant specialized constraints, the algorithm improves dramatically the lower bound computation. Using abstraction through focusing only on relevant features allows the algorithm to take advantage of context-dependent interchangeability between component instances and discard equivalent solutions, involving the same cost as solutions that have already been explored
Solving Set Constraint Satisfaction Problems using ROBDDs
In this paper we present a new approach to modeling finite set domain
constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We
show that it is possible to construct an efficient set domain propagator which
compactly represents many set domains and set constraints using ROBDDs. We
demonstrate that the ROBDD-based approach provides unprecedented flexibility in
modeling constraint satisfaction problems, leading to performance improvements.
We also show that the ROBDD-based modeling approach can be extended to the
modeling of integer and multiset constraint problems in a straightforward
manner. Since domain propagation is not always practical, we also show how to
incorporate less strict consistency notions into the ROBDD framework, such as
set bounds, cardinality bounds and lexicographic bounds consistency. Finally,
we present experimental results that demonstrate the ROBDD-based solver
performs better than various more conventional constraint solvers on several
standard set constraint problems
Towards more efficient solution of conditional constraint satisfaction problems
The focus of the thesis is on improving solving constraint satisfaction problems (CSPs) that change with certain conditions. This special class of problems, which we call conditional CSPs, has proved very useful in modeling important applications, such product configuration and design, and distributed software diagnosis and network management. The problem conditions model choices customers make to configure a product, or they are installation settings or actual observations of a running system that is monitored for diagnosis purpose.
The key, novel contribution of this thesis are two approaches for improving solving methods and the use of random conditional CSPs to evaluate the performance of these methods. With the first approach we propose new algorithms for solving conditional CSPs. These algorithms propagate problem constraints and conditions. The second approach explores the feasibility of reformulating the problem into a standard CSP and introduces new reformulation algorithms.
The implementation results have been evaluated experimentally. The experimental design has extensive test suites of randomly generated standard and conditional CSPs for which general problem parameters, such as density and satisfiability, were varied, as well as specialized parameters that characterize the representation of problem conditions.
The significance of the work lies in the advance of problem resolution for the class of conditional CSPs and the experimental analysis for the proposed new algorithms. The limited solving developments known in the literature of the class of conditional CSPs, a backtrack search algorithm tested on a handful of small problem examples, have been taken an important step further and aligned with efforts reported for standard and other special classes of CSPs
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