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  • 1.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Biundo, Susanne
    Universität Ulm, Ulm, Germany.
    Artificial intelligence planning for ambient environments2011In: Next generation intelligent environments: ambient adaptive systems / [ed] Wolfgang Minker, Tobias Heinroth, Springer Science+Business Media B.V., 2011, 1, p. 195-225Chapter in book (Refereed)
    Abstract [en]

    In this chapter, we describe how Artificial Intelligence planning techniques are used in The Adapted and TRusted Ambient eCOlogies (ATRACO) in order to provide Sphere Adaptation. We introduce the Planning Agent (PA) which plays a central role in the realization and the structural adaptation of activity spheres. Based on particular information included in the ontology of the execution environment, the PA delivers workflows that consist of the basic activities to be executed in order to achieve a user's goals. The PA encapsulates a search engine for hybrid planning--the combination of hierarchical task network planning and partial-order causal-link planning. In this chapter, we describe a formal framework and a development platform for hybrid planning, PANDA. This platform allows for the implementation of many search strategies, and we explain how we realize the search engine of the PA by adapting and configuring PANDA specifically for addressing planning problems that are part of the ATRACO service composition. We describe how the PA interacts with the Sphere Manager and the Ontology Manager in order to create planning problems dynamically and generate workflows in the ATRACO-BPEL language. In addition, an excerpt of a planning domain for ATRACO is provided.

  • 2.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Biundo, Susanne
    Universität Ulm, Ulm, Germany.
    Heinroth, Tobias
    Universität Ulm, Ulm, Germany.
    Minker, Wolfgang
    Universität Ulm, Ulm, Germany.
    Nothdurft, Florian
    Universität Ulm, Ulm, Germany.
    Schattenberg, Bernd
    Universität Ulm, Ulm, Germany.
    Verbal plan explanations for hybrid planning2010In: MKWI / [ed] Matthias Schumann, Lutz M. Kolbe, Michael H. Breitner, and Arne Frerichs, Universitätsverlag Göttingen, 2010, p. 2309-2320Conference paper (Refereed)
  • 3.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Biundo, Susanne
    Universität Ulm, Ulm, Germany.
    Schattenberg, Bernd
    Universität Ulm, Ulm, Germany.
    Plan repair in hybrid planning2008In: KI 2008: Advances in Artificial Intelligence / [ed] Andreas R. Dengel, Karsten Berns, Thomas M. Breuel, Frank Bomarius, and Thomas R. Roth-Berghofer, Springer, 2008, p. 169-176Conference paper (Refereed)
    Abstract [en]

    We present a domain-independent approach to plan repair in a formal framework for hybrid planning. It exploits the generation process of the failed plan by retracting decisions that led to the failed plan fragments. They are selectively replaced by suitable alternatives, and the repaired plan is completed by following the previous generation process as close as possible. This way, a stable solution is obtained, i.e. a repair of the failed plan that causes minimal perturbation.

  • 4.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Goumopoulos, Christos
    DAISy Research Unit, Patras, Greece.
    Calemis, Ioannis
    DAISy Research Unit, Patras, Greece.
    Using AI planning and late binding for managing service workflows in intelligent environments2011In: 2011 IEEE International conference on pervasive computing and communications (PerCom), IEEE conference proceedings, 2011, p. 156-163Conference paper (Refereed)
    Abstract [en]

    In 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.

  • 5. Bidot, Julien
    et al.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Geometric backtracking for combined task and motion planning in robotic systems2017In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 247, p. 229-265Article in journal (Refereed)
    Abstract [en]

    Planners for real 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 to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.

  • 6.
    Bidot, Julien
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Geometric backtracking for combined task and path planning in robotic systemsManuscript (preprint) (Other academic)
    Abstract [en]

    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. 

  • 7.
    Bidot, Julien
    et al.
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Laborie, Philippe
    ILOG S.A., Gentilly, France.
    Beck, J. Christopher
    University of Toronto, Toronto, Canada.
    Vidal, Thierry
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Using constraint programming and simulation for execution monitoring and progressive scheduling2006In: Information control problems in manufacturing 2006 / [ed] Alexandre Dolgui, Gérard Morel, Carlos E. Pereira, Elsevier, 2006, p. 615-620Conference paper (Refereed)
    Abstract [en]

    The problem we tackle is progressive scheduling with temporal and resource uncertainty. Operation durations are imprecise and alternative resources may break down. Operation end times and resource breakdowns are observed during execution. In this paper, we assume we have a representation of uncertainty in the form of probability distributions which are used in the simulation of schedule execution. We generate the schedule piece by piece during execution and use simulation to monitor the execution of the partial schedule. This paper describes the basis on which the decision to select and schedule a new subset of operations is made.

  • 8.
    Bidot, Julien
    et al.
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Laborie, Philippe
    ILOG S.A., Gentilly, France.
    Beck, J. Christopher
    University College Cork, Cork, Ireland.
    Vidal, Thierry
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Using simuation for execution monitoring and on-line rescheduling with uncertain durations2003Conference paper (Refereed)
    Abstract [en]

    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.

  • 9.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Vidal, Thierry
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Laborie, Philippe
    ILOG S.A., Gentilly, France.
    Beck, J. Christopher
    University of Toronto, Toronto, Canada.
    A general framework for scheduling in a stochastic environment2007In: / [ed] Manuela M. Veloso, 2007, p. 56-61Conference paper (Refereed)
    Abstract [en]

    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 three-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.

  • 10.
    Bidot, Julien
    et al.
    Universität Ulm, Ulm, Germany.
    Vidal, Thierry
    IRISA-INRIA, Rennes, France.
    Laborie, Philippe
    ILOG S.A., Gentilly, France.
    Beck, J. Christopher
    University of Toronto, Toronto, Canada.
    A theoretic and practical framework for scheduling in a stochastic environment2009In: Journal of Scheduling, ISSN 1094-6136, E-ISSN 1099-1425, Vol. 12, no 3, p. 315-344Article in journal (Refereed)
    Abstract [en]

    There are many systems and techniques that address stochastic planning and scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how generation and execution of the plan, or the schedule, are combined, and if and when knowledge about the uncertainties is taken into account. In many real-life problems, it appears that many of these approaches are needed and should be combined, which to our knowledge has never been done. In this paper, we propose a typology that distinguishes between proactive, progressive, and revision approaches. Then, focusing on scheduling and schedule execution, a theoretic model integrating those three approaches is defined. This model serves as a general template to implement a system that will fit specific application needs: we introduce and discuss our experimental prototypes which validate our model in part, and suggest how this framework could be extended to more general planning systems.

  • 11.
    Biundo, Susanne
    et al.
    Universität Ulm, Ulm, Germany.
    Bidot, Julien
    Universität Ulm, Ulm, Germany.
    Schattenberg, Bernd
    Universität Ulm, Ulm, Germany.
    Planning in the real world2011In: Informatik-Spektrum, ISSN 0170-6012, E-ISSN 1432-122X, Vol. 34, no 5, p. 443-454Article in journal (Refereed)
    Abstract [en]

    In this article, we describe how real world planning problems can be solved by employing Artificial Intelligence planning techniques. We introduce the paradigm of hybrid planning, which is particularly suited for applications where plans are not intended to be automatically executed by systems, but are made for humans. Hybrid planning combines hierarchical planning – the stepwise refinement of complex tasks – with explicit reasoning about causal dependencies between actions, thereby reflecting exactly the kinds of reasoning humans perform when developing plans. We show how plans are generated and how failed plans are repaired in a way that guarantees stability. Our illustrating examples are taken from a domain model for disaster relief missions enforced upon extensive floods. Finally, we present a tool to support the challenging task of constructing planning domain models.

    The article ends with an overview of a wide varity of actual planning applications and outlines further such in the area of cognitive technical systems.

  • 12.
    Karlsson, Lars
    et al.
    Örebro University, School of Science and Technology.
    Bidot, Julien
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Hillenbrand, Ulrich
    Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Oberpfaffenhofen, Germany.
    Schmidt, Florian
    Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Oberpfaffenhofen, Germany.
    Combining task and path planning for a humanoid two-arm robotic system2012In: TAMPRA 2012: Proceedings of the Workshop on Combining Task and Motion Planning for Real-World Applications / [ed] Marcello Cirillo, Brian Gerkey, Federico Pecora, Mike Stilman, 2012, p. 13-20Conference paper (Refereed)
  • 13.
    Karlsson, Lars
    et al.
    Örebro University, School of Science and Technology.
    Bidot, Julien
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Hillenbrand, Ulrich
    Deutschen Zentrums für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany.
    Schmidt, Florian
    Deutschen Zentrums für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany.
    Progress and challenges in planning for a two-arm robot2012Conference paper (Refereed)
  • 14.
    Lagriffoul, Fabien
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Bidot, Julien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Efficiently combining task and motion planning using geometric constraints2014In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 33, no 14, p. 1726-1747Article in journal (Refereed)
    Abstract [en]

    We propose a constraint-based approach to address a class of problems encountered in combined task and motion planning (CTAMP), which we call kinematically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to as geometric backtrack search. In kinematically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.

  • 15.
    Lagriffoul, Fabien
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology. INRIA Rhône-Alpes, France.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Bidot, Julien
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Using Geometric Constraints for Efficiently Combining Task and Motion PlanningIn: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176Article in journal (Refereed)
    Abstract [en]

    We propose a constraint-based approach to address a class of problems encountered in Combined Task and Motion Planning (CTAMP), which we call geometrically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to asgeometric backtrack search. In geometrically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task-level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.

  • 16.
    Lagriffoul, Fabien
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Bidot, Julien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Combining Task and Motion Planning is Not Always a Good Idea2013Conference paper (Refereed)
    Abstract [en]

    Combining task and motion planning requires tointerleave causal and geometric reasoning, in order to guaranteethe plan to be executable in the real world. The resulting searchspace, which is the cross product of the symbolic search spaceand the geometric search space, is huge. Systematically calling ageometric reasoner while evaluating symbolic actions is costly. Onthe other hand, geometric reasoning can prune out large parts ofthis search space if geometrically infeasible actions are detectedearly. Hence, we hypothesized the existence of a search depthlevel, until which geometric reasoning can be interleaved withsymbolic reasoning with tractable combinatorial explosion, whilekeeping the benefits of this pruning. In this paper, we propose asimple model that proves the existence of such search depth level,and validate it empirically through experiments in simulation

  • 17.
    Schattenberg, Bernd
    et al.
    Universität Ulm, Ulm, Germany.
    Bidot, Julien
    Universität Ulm, Ulm, Germany.
    Biundo, Susanne
    Universität Ulm, Ulm, Germany.
    On the construction and evaluation of flexible plan-refinement strategies2007In: KI 2007: advances in artificial intelligence / [ed] Joachim Hertzberg, Michael Beetz, Roman Englert, 2007, p. 367-381Conference paper (Refereed)
    Abstract [en]

    This paper describes a system for the systematic construction and evaluation of planning strategies. It is based on a proper formal account of refinement planning and allows us to decouple plan-deficiency detection, refinement computation, and search control. In adopting this methodology, planning strategies can be explicitly described and easily deployed in various system configurations.

    We introduce novel domain-independent planning strategies that are applicable to a wide range of planning capabilities and methods. These so-called HotSpot strategies are guided by information about current plan defects and solution options. The results of a first empirical performance evaluation are presented in the context of hybrid planning.

  • 18.
    Schattenberg, Bernd
    et al.
    Universität Ulm, Ulm, Germany.
    Bidot, Julien
    Universität Ulm, Ulm, Germany.
    Geßler, Sascha
    Universität Ulm, Ulm, Germany.
    Biundo, Susanne
    Universität Ulm, Ulm, Germany.
    A framework for interactive hybrid planning2009In: KI 2009: advances in artificial intelligence / [ed] Bärbel Mertsching, Marcus Hund, Zaheer Aziz, Springer, 2009, p. 17-24Conference paper (Refereed)
    Abstract [en]

    Hybrid planning provides a powerful mechanism to solve real-world planning problems. We present a domain-independent, mixed-initiative approach to plan generation that is based on a formal concept of hybrid planning. It allows any interaction modalities and models of initiative while preserving the soundness of the planning process. Adequately involving the decision competences of end-users in this way will improve the application potential as well as the acceptance of the technology.

  • 19.
    Vidal, Thierry
    et al.
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Bidot, Julien
    Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France.
    Dynamic sequencing of tasks insSimple temporal networks with uncertainty2001Conference paper (Refereed)
    Abstract [en]

    Planning or scheduling systems that handle tasks with uncertain durations mightuse an extension of the Simple Temporal Network (STN) 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 an 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.

1 - 19 of 19
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