Problem simplification is of continuing interest in the field of constraint satisfaction. In this paper we examine properties associated with the basic idea of value substitutability and show how certain forms of substitutability can be organised into a strict hierarchy. One of these properties, here called replaceability, has been identified by other authors as being of special interest. We confirm these claims by showing that replaceability is the most general property in this hierarchy that allows local to global inferences. We introduce new algorithms for establishing local (neighbourhood) replaceability that are superior to other types of algorithms designed for this task and demonstrate their effectiveness (values removed) for a variety of constraint types. We show that it is sometimes possible to infer replaceability, leading to appreciable reductions in time required to reduce a problem to irreplaceable domain sets. We also describe a new kind of complexity peak that occurs with neighbourhood replaceability algorithms and analyse this effect.

Many problems, from Sudoku to factory scheduling, can be regarded as constraint satisfaction problems. A key component of real world problem solving is a conversation between a constraint programming expert and a problem domain expert to specify the problem to be solved. This presentation argues that the time is ripe for progress in automating the constraint programmer side of this conversation and suggests promising avenues for this pursuit.

As AI becomes more ubiquitous there is increasing interest in computers being able to provide explanations for their conclusions. This paper proposes exploring the relationship between the structure of a problem and its explanation. The nature of this challenge is introduced through a series of simple constraint satisfaction problems.

Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundation for human-awareness should enable ourselves and our "AIs" to "explain ourselves" naturally to each other. Constraint reasoning offers particular opportunities and challenges in this regard. This paper takes note of the history of work in this area and encourages increased attention, laying out a rough research agenda.

Data uncertainty in real-life problems is a current challenge in many areas, including Operations Research (OR) and Constraint Programming (CP). This is especially true given the continual and accelerating increase in the amount of data associated with real-life problems, to which Large Scale Combinatorial Optimization (LSCO) techniques may be applied. Although data uncertainty has been studied extensively in the literature, many approaches do not take into account the partial or complete lack of information about uncertainty in real-life settings. To meet this challenge, in this paper we present a strategy for extrapolating data from limited uncertain information to ensure a certain level of robustness in the solutions obtained. Our approach is motivated by real-world applications of supply of timber from forests to saw-mills.

In evolutionary computation, incremental evolution refers to the process of employing an evolutionary environment that becomes increasingly complex over time. We present an implementation of this approach to develop randomised local search heuristics for constraint satisfaction problems, combining research on incremental evolution with local search heuristics evolution. A population of local search heuristics is evolved using a genetic programming framework on a simple problem for a short period and is then allowed to evolve on a more complex problem. Experiments compare the performance of this population with that of a randomly initialised population evolving directly on the more complex problem. The results obtained show that incremental evolution can represent a significant improvement in terms of optimisation speed, solution quality and solution structure.

Many knowledge-based applications require knowledge maintenance to keep the application functional throughout its lifecycle. In this paper we present iCAM, a constraintbased knowledge maintenance system that operates in a hospital’s material management domain. iCAM uses consistency algorithms to assist users in placing orders and making order corrections, and to ensure that maintenance activities are consistent with the department’s ordering environment. This approach allows iCAM to interact with the user to revise orders and/or to update the knowledge base. For the user, there is not much distinction between these two tasks. This is one of iCAM’s greatest strengths; order corrections and knowledge maintenance are carried out in a similar manner, since both are based on inconsistencies with respect to the existing knowledge base. iCAM has various constraint types that support physical and policy restrictions. It also allows maintenance to be done by a number of users while maintaining the integrity of the knowledge base by a system of role restrictions.

This paper describes the Adaptive Constraint Engine (ACE), an ambitious ongoing research project to support constraint programmers, both human and machine. The program begins with substantial knowledge about constraint satisfaction. The program harnesses a cognitively-oriented architecture (FORR) to manage search heuristics and to learn new ones. ACE can transfer what it learns on simple problems to solve more difficult ones, and can readily export its knowledge to ordinary constraint solvers. It currently serves both as a learner and as a test bed for the constraint community.

The partial constraint satisfaction paradigm focuses on solving relaxations of problems that either do not admit solutions, or that are either impractical or impossible to solve completely. The semiring-based framework for soft constraints is a unifying model for a variety of extensions of the constraint satisfaction formalism. For example, the semiring-based framework can represent weighted, fuzzy, probabilistic and set-based constraint satisfaction problems. We discuss how the semiring-based framework for soft constraints can be used to model partial constraint satisfaction problems. We show how the semiring framework can be used to capture a notion of distance between a solution and a problem based on the known distance metrics used in the partial constraint satisfaction literature. These solution-problem distance metrics can be seen as providing lower-bounds on the distance between a problem and its relaxation.

Constraint satisfaction is becoming the paradigm of choice for solving many real-world problems. To date, most approaches to constraint satisfaction have focused on solving a problem using some form of backtrack search. Furthermore, the typical view is that a constraint satisfaction problem will be solved only once. However, in many real-world contexts, problems are solved repeatedly over time. Also such problems often exhibit some structure. This motivates the application of some form of learning to improve the performance of search from previously discovered solutions. We present an approach that uses knowledge about known solutions to a problem to improve search. The approach we present is based on a combination of decision tree learning and constraint satisfaction. We demonstrate that significant improvements, almost an order-of-magnitude, in search effort can be achieved using this hybrid approach over traditional search. We also show that the space complexity using this approach is almost negligible. This work is of interest in domains such as product configuration, and interactive constraint solving in general where the system takes the initiative by asking questions.

In this paper, we consider multi-agent constraint systems with preferences, modeled as soft constraint systems in which variables and constraints are distributed among multiple autonomous agents. We assume that each agent can set some preferences over its local data, and we consider two different criteria for finding optimal global solutions: fuzzy and Pareto optimality. We propose a general graph-based framework to describe the problem to be solved in its generic form. As a case study, we consider a distributed meeting scheduling problem where each agent has a pre-existing schedule and the agents must decide on a common meeting that satisfies a given optimality condition. For this scenario we consider the topics of solution quality, search efficiency, and privacy loss, where the latter pertains to information about an agent's pre-existing meetings and available time-slots. We also develop and test strategies that trade efficiency for solution quality and strategies that minimize information exchange, including some that do not require inter-agent comparisons of utilities. Our experimental results demonstrate some of the relations among solution quality, efficiency, and privacy loss, and provide useful hints on how to reach a tradeoff among these three factors. In this work, we show how soft constraint formalisms can be used to incorporate preferences into multi-agent problem solving along with other facets of the problem, such as time and distance constraints. This work also shows that the notion of privacy loss can be made concrete so that it can be treated as a distinct, manipulable factor in the context of distributed decision making.

The digital networked world is enabling and requiring a new emphasis on personalized document creation. The new, more dynamic digital environment demands tools that can reproduce both the contents and the layout automatically, tailored to personal needs and transformed for the presentation device, and can enable novices to easily create such documents. In order to achieve such automated document assembly and transformation, we have formalized custom document creation as a multiobjective optimization problem, and use a genetic algorithm to assemble and transform compound personalized documents. While we have found that such an automated process for document creation opens new possibilities and new workflows, we have also found several areas where further research would enable the approach to be more broadly and practically applied. This paper reviews the current system and outlines several areas where future research will broaden its current capabilities.

There is no standard model at the service layer. However, fault management to distributed services and applications needs to construct and utilize complex models of the participating objects and their interdependencies. Thus, model-based fault management tools can predict the correct behavior of diagnosed systems and use the resulting predictions to identify faults. When used on-line in real systems, diagnostic tools based on such models should be able to provide prompt response and accurate, comprehensive explanations of the root causes of faults. In this paper we propose to address these requirements: modeling, proactive diagnosis, and explanation. We apply a recent extension to the constraint satisfaction paradigm, called composite constraint satisfaction, to facilitate modeling of complex systems, and we use constraint propagation techniques to support proactive diagnosis and explanation. We demonstrate the applicability of our approach on an example of a basic groupware service, namely, distributed database replication.

Constraint satisfaction problems involve finding values for problem variables that satisfy constraints on what combinations of values are permitted. They have applications in many areas of artifical intelligence, from planning to natural language understanding. Constraint satisfaction can be very difficult. A variety of tools are available for promoting successful satisfaction.

A basic problem is to optimize the tradeoff between effort required to establish a local consistency and that required for search. An approach is presented to this problem which is termed selective relaxation. The idea is to perform consistency checking at places where it is likely to be effective, basing this judgment on local criteria. To this end, the authors introduce two forms of bounded relaxation, one in which consistency testing propagates for a limited distance from a point of change, and one in which it stops when the amount of change, or response, falls below threshold. Experiments show that these procedures can outperform well-known preprocessing or hybrid algorithms on many problems.>

Backtrack search is often used to solve constraint satisfaction problems. A relationship involving the structure of the constraints is described that provides a bound on the backtracking required to advance deeper into the backtrack tree. This analysis leads to upper bounds on the effort required for solution of a class of constraint satisfaction problems. The solutions involve a combination of relaxation preprocessing and backtrack search. The bounds are expressed in terms of the structure of the constraint connections. Specifically, the effort is shown to have a bound exponential in the size of the largest biconnected component of the constraint graph, as opposed to the size of the graph as a whole.

A constraint satisfaction problem revolves finding values for a set of variables subject to a set of constraints (relations) on those variables Backtrack search is often used to solve such problems.A relationship involving the structure of the constraints is described which characterizes to some degree the extreme case of mimmum backtracking (none) The relationship involves a concept called "width," which may provide some guidance in the representation of constraint satisfaction problems and the order m which they are searched The width concept is studied and applied, in particular, to constraints which form tree structures.