484 research outputs found
Reasoning and querying bounds on differences with layered preferences
Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and
calories. Recently, some approaches have extended the
BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction,
considering probabilities or preferences. While previous
approaches have mainly aimed at providing an
optimal solution to the set of constraints, we propose
an innovative class of approaches in which constraint
propagation algorithms aim at identifying the \u201cspace of
solutions\u201d (i.e., the minimal network) with their preferences,
and query answering mechanisms are provided
to explore the space of solutions as required, for
example, in decision support tasks. Aiming at generality,
we propose a class of approaches parametrized
over user\u2010defined scales of qualitative preferences (e.g.,
Low, Medium, High, and Very High), utilizing the resume
and extension operations to combine preferences,
and considering different formalisms to associate preferences
with BoDs. We consider both \u201cgeneral\u201d preferences
and a form of layered preferences that we call
\u201cpyramid\u201d preferences. The properties of the class of
approaches are also analyzed. In particular, we show
that, when the resume and extension operations are
defined such that they constitute a closed semiring, a
more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation
of the constraint propagation algorithms
Beyond C<i>max</i>: an optimization-oriented framework for constraint-based scheduling
This paper presents a framework taking advantage of both the flexibility of constraint programming and the efficiency of operations research algorithms for solving scheduling problems under various objectives and constraints. Built upon a constraint programming engine, the framework allows the use of scheduling global constraints, and it offers, in addition, a modular and simplified way to perform optimality reasoning based on well-known scheduling relaxations. We present a first instantiation on the single machine problem with release dates and lateness minimization. Beyond the simplicity of use, the ptimizationoriented framework appears to be, from the experiments, effective for dealing with such a pure problem even without any ad-hoc heuristics
Beyond C<i>max</i>: an optimization-oriented framework for constraint-based scheduling
This paper presents a framework taking advantage of both the flexibility of constraint programming and the efficiency of operations research algorithms for solving scheduling problems under various objectives and constraints. Built upon a constraint programming engine, the framework allows the use of scheduling global constraints, and it offers, in addition, a modular and simplified way to perform optimality reasoning based on well-known scheduling relaxations. We present a first instantiation on the single machine problem with release dates and lateness minimization. Beyond the simplicity of use, the ptimizationoriented framework appears to be, from the experiments, effective for dealing with such a pure problem even without any ad-hoc heuristics
Using a Temporal Constraint Network for Business Process Execution
Business process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, the technology continues to face challenges in coping with dynamic business environments where requirements and goals are constantly changing. In this paper, we present a modelling framework for business processes that is conducive to dynamic change and the need for flexibility in execution. This framework is based on the notion of process constraints. Process constraints may be specified for any aspect of the process, such as task selection, control flow, resource allocation, etc. Our focus in this paper is on a set of scheduling constraints that are specified through a temporal constraint network. We will demonstrate how this specification can lead to increased flexibility in process execution, while maintaining a desired level of control. A key feature and strength of the approach is to use the power of constraints, while still preserving the intuition and visual appeal of graphical languages for process modelling
Constraint-based Temporal Reasoning with Preferences
Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples
Simple Stochastic Temporal Constraint Networks
Many artificial intelligence tasks (e.g., planning, situation assessment, scheduling) require reasoning about events in time. Temporal constraint networks offer an elegant and often computationally efficient framework for such temporal reasoning tasks. Temporal data and knowledge available in some domains is necessarily imprecise - e.g., as a result of measurement errors associated with sensors. This paper introduces stochastic temporal constraint networks thereby extending constraint-based approaches to temporal reasoning with precise temporal knowledge to handle stochastic imprecision. The paper proposes an algorithm for inference of implicit stochastic temporal constraints from a given set of explicit constraints. It also introduces a stochastic version of the temporal constraint network consistency problem and describes techniques for solving it under certain simplifying assumptions
A general framework integrating techniques for scheduling under uncertainty
Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof
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