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

Un cadre général intégrant les techniques d'ordonnancement sous incertitudes

La littérature sur la planification de tâches et l'ordonnancement sous incertitudes comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer en utilisant les termilologies existantes. Nous avons cependant identifié trois familles d'approches générales qui structurent la littérature en fonction de la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui intègre 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. Au-dessus de ILOG Solver et Scheduler nous avons également implémenté un prototype logiciel 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.The research literature about task planning and scheduling under uncertainty comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them with existing terminologies. However, we have identified three general families of approaches that can be used to structure the literature based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that integrates 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 have implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraint-based approach combined with simulation to solve some instances thereof.TOULOUSE-ENSEEIHT (315552331) / SudocTARBES-ENIT (654402301) / SudocSudocFranceF

Dynamic sequencing of tasks in simple temporal networks with uncertainty

Planning or scheduling systems that handle tasks with uncertain durations might use an extension of the Simple Temporal Network with a distinction between controllable and contingent variables and constraints. Temporal consistency is then redefined in terms of Dynamic Controllability, which means the ability to decide the precise timing of tasks only at execution time, depending on observations made, and still satisfying all no constraints. This property has been recently proven to be checkable in polynomial time through a simple path consistency-like algorithm. In this paper, we are interested in using such a model in scheduling applications, in which tasks may compete for the same resource, and should thus be sequenced. Such constraints make the problem NP-hard, and cannot be directly expressed in a STN. In the presence of uncertainty, one might also wish to postpone task sequencing until execution time. This paper provides the characterization of such a Dynamic Sequencing ability. Then we propose an incomplete checking method still relying on the STNU for the sake of temporal reasoning efficiency, adding further filtering techniques to account for sequencing constraints.

Using AI Planning and Late Binding for Managing Service Workflows in Intelligent Environments

IEEE International Conference on Pervasive Computing and Communications (PerCom) -- MAR 21-25, 2011 -- Seattle, WAWOS: 000299123100018In this paper, we present an approach to aggregating and using devices that support the everyday life of human users in ambient intelligence environments. These execution environments are complex and changing over time, since the devices of the environments are numerous and heterogeneous, and they may appear or disappear at any time. In order to appropriately adapt the ambient system to a user's needs, we adopt a service-oriented approach; i.e., devices provide services that reflect their capabilities. The orchestration of the devices is actually realized with the help of Artificial Intelligence planning techniques and dynamic service binding. At design time, (i) a planning problem is created that consists of the user's goal to be achieved and the services currently offered by the intelligent environment, (ii) the planning problem is then solved using Hierarchical Task Network and Partial-Order Causal-Link planning techniques, (iii) and from the planning decisions taken to find solution plans, abstract service workflows are automatically generated. At run time, the abstract services are dynamically bound to devices that are actually present in the environment. Adaptation of the workflow instantiation is possible due to the late binding mechanism employed. The paper depicts the architecture of our system. It also describes the modeling and the life cycle of the workflows. We discuss the advantages and the limit of our approach with respect to related work and give specific details about implementation. We present some experimental results that validate our system in a real-world application scenario.IEEE, Natl Sci Fdn (NSF), Microsoft Res, IBM, QUALCOMMEC's 7th FP [216837]; Transregional Collaborative Research CentreMinistry of Education and Science, Spain [SFB/TRR 62]; German Research Foundation (DFG)German Research Foundation (DFG)The research leading to these results has received funding from the ECs 7th FP under grant agreement no 216837 and from the Transregional Collaborative Research Centre SFB/TRR 62 Companion-Technology for Cognitive Technical Systems funded by the German Research Foundation (DFG)

Using simulation for execution monitoring and on-line rescheduling with uncertain durations

The problem we tackle is on-line rescheduling with temporal uncertainty, activity durations are uncertain and activity end times must be observed during execution. In this paper we will assume we have a representation of the uncertainty of each activity duration in the form of probability distributions which are used in the simulation of schedule execution. We use the simulations to monitor the execution of the schedule and in particular to estimate the quality of the schedule and the end times of the activities. Given an initial schedule, the schedule starts execution and we must decide when to reschedule. We propose and explore a non-monotonic technique where each time we reschedule we can completely change the existing schedule except for those activities that have already started (or finished) execution. This paper explicitly addresses the basis on which the decision to reschedule is made by investigating three simple measures of the data provided by simulation

Abstract

There are many systems and techniques that address stochastic scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how scheduling and schedule execution are combined, and if and when knowledge about the uncertainties are taken into account. In many real-life problems, it appears that all these approaches are needed and should be combined, which to our knowledge has never been done. Hence it it first desirable to define a thorough classification of the techniques and systems, exhibiting relevant features: in this paper, we propose a tree-dimension typology that distinguishes between proactive, progressive, and revision techniques. Then a theoretical representation model integrating those three distinct approaches is defined. This model serves as a general template within which parameters can be tuned to implement a system that will fit specific application needs: we briefly introduce in this paper our first experimental prototypes which validate our model.

Geometric backtracking for combined task and path planning in robotic systems

Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift. GeRTSAUN

Geometric backtracking for combined task and path planning in robotic systems

Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift. GeRTSAUN

Using simuation for execution monitoring and on-line rescheduling with uncertain durations

The problem we tackle is on-line rescheduling with temporal uncertainty, activity durations are uncertain and activity end times must be observed during execution. In this paper, we will assume we have a representation of the uncertainty of each activity duration in the form of probability distributions which are used in the simulation of schedule execution. We use the simulations to monitor the execution of the schedule and in particular to estimate the quality of the schedule and the end times of the activities. Given an initial schedule, the schedule starts execution and we must decide when to reschedule. We propose and explore a non-monotonic technique where each time we reschedule we can completely change the existing schedule except for those activities that have already started (or finished) execution. This paper explicitly addresses the basis on which the decision to reschedule is made by investigating three simple measures of the data provided by simulation