Distributed Method Selection and Dispatching of Contingent, Temporally Flexible Plans

SM thesisMany applications of autonomous agents require groups to work in tight coordination. To be dependable, these groups must plan, carry out and adapt their activities in a way that is robust to failure and to uncertainty. Previous work developed contingent, temporally flexible plans. These plans provide robustness to uncertain activity durations, through flexible timing constraints, and robustness to plan failure, through alternate approaches to achieving a task. Robust execution of contingent, temporally flexible plans consists of two phases. First, in the plan extraction phase, the executive chooses between the functionally redundant methods in the plan to select an execution sequence that satisfies the temporal bounds in the plan. Second, in the plan execution phase, the executive dispatches the plan, using the temporal flexibility to schedule activities dynamically.Previous contingent plan execution systems use a centralized architecture in which a single agent conducts planning for the entire group. This can result in a communication bottleneck at the time when plan activities are passed to the other agents for execution, and state information is returned. Likewise, a computation bottleneck may also occur because a single agent conducts all processing.This thesis introduces a robust, distributed executive for temporally flexible plans, called Distributed-Kirk, or D-Kirk. To execute a plan, D-Kirk first distributes the plan between the participating agents, by creating a hierarchical ad-hoc network and by mapping the plan onto this hierarchy. Second, the plan is reformulated using a distributed, parallel algorithm into a form amenable to fast dispatching. Finally, the plan is dispatched in a distributed fashion.We then extend the D-Kirk distributed executive to handle contingent plans. Contingent plans are encoded as Temporal Plan Networks (TPNs), which use a non-deterministic choice operator to compose temporally flexible plan fragments into a nested hierarchy of contingencies. A temporally consistent plan is extracted from the TPN using a distributed, parallel algorithm that exploits the structure of the TPN.At all stages of D-Kirk, the communication load is spread over all agents, thus eliminating the communication bottleneck. In particular, D-Kirk reduces the peak communication complexity of the plan execution phase by a factor of O(A/e'), where e' is the number of edges per node in the dispatchable plan, determined by the branching factor of the input plan, and A is the number of agents involved in executing the plan.In addition, the distributed algorithms employed by D-Kirk reduce the computational load on each agent and provide opportunities for parallel processing, thus increasing efficiency. In particular, D-Kirk reduces the average computational complexity of plan dispatching from O(eN^3) in the centralized case, to typical values of O(eN^2) per node and O(eN^3/A) per agent in the distributed case, where N is the number of nodes in the plan and e is the number of edges per node in the input plan.Both of the above results were confirmed empirically using a C++ implementation of D-Kirk on a set of parameterized input plans. The D-Kirk implementation was also tested in a realistic application where it was used to control a pair of robotic manipulators involved in a cooperative assembly task

Distributed method selection and dispatching of contingent, temporally flexible plans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 175-178).Many applications of autonomous agents require groups to work in tight coordination. To be dependable, these groups must plan, carry out and adapt their activities in a way that is robust to failure and to uncertainty. Previous work developed contingent, temporally flexible plans. These plans provide robustness to uncertain activity durations, through flexible timing constraints, and robustness to plan failure, through alternate approaches to achieving a task. Robust execution of contingent, temporally flexible plans consists of two phases. First, in the plan extraction phase, the executive chooses between the functionally redundant methods in the plan to select an execution sequence that satisfies the temporal bounds in the plan. Second, in the plan execution phase, the executive dispatches the plan, using the temporal flexibility to schedule activities dynamically. Previous contingent plan execution systems use a centralized architecture in which a single agent conducts planning for the entire group. This can result in a communication bottleneck at the time when plan activities are passed to the other agents for execution, and state information is returned. Likewise, a computation bottleneck may also occur because a single agent conducts all processing.(cont.) This thesis introduces a robust, distributed executive for temporally flexible plans, called Distributed-Kirk, or D-Kirk. To execute a plan, D-Kirk first distributes the plan between the participating agents, by creating a hierarchical ad-hoc network and by mapping the plan onto this hierarchy. Second, the plan is reformulated using a distributed, parallel algorithm into a form amenable to fast dispatching. Finally, the plan is dispatched in a distributed fashion. We then extend the D-Kirk distributed executive to handle contingent plans. Contingent plans are encoded as Temporal Plan Networks (TPNs), which use a non-deterministic choice operator to compose temporally flexible plan fragments into a nested hierarchy of contingencies. A temporally consistent plan is extracted from the TPN using a distributed, parallel algorithm that exploits the structure of the TPN. At all stages of D-Kirk, the communication load is spread over all agents, thus eliminating the communication bottleneck. In particular, D-Kirk reduces the peak communication complexity of the plan execution phase by a factor of O (A/e'), where e' is the number of edges per node in the dispatchable plan, determined by the branching factor of the input plan, and A is the number of agents involved in executing the plan.(cont.) In addition, the distributed algorithms employed by D-Kirk reduce the computational load on each agent and provide opportunities for parallel processing, thus increasing efficiency. In particular, D-Kirk reduces the average computational complexity of plan dispatching from O (N3e) in the centralized case, to typical values of O (N2e) per node and O (N3e/A) per agent in the distributed case, where N is the number of nodes in the plan and e is the number of edges per node in the input plan. Both of the above results were confirmed empirically using a C++ implementation of D-Kirk on a set of parameterized input plans. The D-Kirk implementation was also tested in a realistic application where it was used to control a pair of robotic manipulators involved in a cooperative assembly task.by Stephen Block.S.M

Dynamic Controllability of Temporally-flexible Reactive Programs

In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs. We consider three reactive programming language constructs whose behavior depends on runtime observations; conditional execution, iteration, and exception handling. Temporally-flexible reactive programs are distinguished from temporally-flexible plans in that program execution is conditioned on the runtime state of the world. In addition, exceptions are thrown and caught at runtime in response to violated timing constraints, and handled exceptions are considered successful program executions. Dynamic controllability corresponds to a guarantee that a program will execute to completion, despite runtime constraint violations and uncertainty in runtime state. An algorithm is developed which frames the dynamic controllability problem as an AND/OR search tree over possible program executions. A key advantage of this approach is the ability to enumerate only a subset of possible program executions that guarantees dynamic controllability, framed as an AND/OR solution subtree

Optimising Flexibility of Temporal Problems with Uncertainty

Temporal networks have been applied in many autonomous systems. In real situations, we cannot ignore the uncertain factors when using those autonomous systems. Achieving robust schedules and temporal plans by optimising flexibility to tackle the uncertainty is the motivation of the thesis. This thesis focuses on the optimisation problems of temporal networks with uncertainty and controllable options in the field of Artificial Intelligence Planning and Scheduling. The goal of this thesis is to construct flexibility and robustness metrics for temporal networks under the constraints of different levels of controllability. Furthermore, optimising flexibility for temporal plans and schedules to achieve robust solutions with flexible executions. When solving temporal problems with uncertainty, postponing decisions according to the observations of uncertain events enables flexible strategies as the solutions instead of fixed schedules or plans. Among the three levels of controllability of the Simple Temporal Problem with Uncertainty (STPU), a problem is dynamically controllable if there is a successful dynamic strategy such that every decision in it is made according to the observations of past events. In the thesis, we make the following contributions. (1) We introduce an optimisation model for STPU based on the existing dynamic controllability checking algorithms. Some flexibility and robustness measures are introduced based on the model. (2) We extend the definition and verification algorithm of dynamic controllability to temporal problems with controllable discrete variables and uncertainty, which is called Controllable Conditional Temporal Problems with Uncertainty (CCTPU). An entirely dynamically controllable strategy of CCTPU consists of both temporal scheduling and variable assignments being dynamically decided, which maximize the flexibility of the execution. (3) We introduce optimisation models of CCTPU under fully dynamic controllability. The optimisation models aim to answer the questions how flexible, robust or controllable a schedule or temporal plan is. The experiments show that making decisions dynamically can achieve better objective values than doing statically. The thesis also contributes to the field of AI planning and scheduling by introducing robustness metrics of temporal networks, proposing an envelope-based algorithm that can check dynamic controllability of temporal networks with uncertainty and controllable discrete decisions, evaluating improvements from making decisions strongly controllable to temporally dynamically controllable and fully dynamically controllable and comparing the runtime of different implementations to present the scalability of dynamically controllable strategies

Adaptive Time- and Process-Aware Information Systems

For the digitized enterprise the proper handling of the temporal aspects of its business processes is vital. Delivery times, appointments and deadlines must be met, processing times and durations be monitored, and optimization objectives shall be pursued. However, contemporary Process-Aware Information Systems (PAISs)--the go-to solution for the computer-aided support of business processes—still lack a sophisticated support of the time perspective. Hence, there is a high demand for a more profound support of temporal aspects in PAISs. Accordingly, both the specification and the operational support of temporal aspects constitute fundamental challenges for the further development and dissemination of PAISs. The aim of this thesis is to propose a framework for supporting the time perspective of business processes in PAISs. As PAISs enable the design, execution and evolution of business processes, the designated framework must support these three fundamental phases of the process life cycle. The ATAPIS framework proposed by this thesis essentially comprises three major com-ponents. First, a universal and comprehensive set of time patterns is provided. Respective time patterns represent temporal concepts commonly found in business processes and are based on empirical evidence. In particular, they provide a universal and comprehensive set of notions for describing temporal aspects in business processes. Moreover, a precise formal semantics for each of the time patterns is provided based on an in-depth analysis of a large set of real-world use cases. Respective formal semantics enable the proper integration of the time patterns into PAISs. In turn, the latter will allow for the specification of time-aware process schemas. Second, a generic framework for implementing the time patterns based on their formal semantics is developed. The framework and its techniques enable the verification of time-aware process schemas regarding their temporal consistency, i. e., their ability to be successfully executed without violating any of their temporal constraints. Subsequently, the framework is extended to consider advanced aspects like the contingent nature of activity durations and alternative execution paths as well. Moreover, an algorithm as well as techniques for executing and monitoring time-aware process instances in PAISs is provided. Based on the presented concepts, it becomes possible to ensure that a time-aware process instance may be executed without violating any of its temporal constraints. Third, a set of change operations for dynamically modifying time-aware process instances during run time is suggested. Respective change operations ensure that a modified time-aware process instance remains temporally consistent after the respective modification. Moreover, to reduce the complexity involved when applying multiple change operations a sophisticated approximation-based technique is presented. Overall, the developed change operations allow providing the flexibility required by business processes in practice. Altogether, the ATAPIS framework provides fundamental concepts, techniques and algorithms for integrating the time perspective into PAISs. As beauty of this framework the specification, execution and evolution of business processes is supported by an integrated approach

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

Optimal temporal planning at reactive time scales via dynamic backtracking branch and bound

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.Includes bibliographical references (p. 110-115).Autonomous robots are being considered for increasingly capable roles in our society, such as urban search and rescue, automation for assisted living, and lunar habitat construction. To fulfill these roles, teams of autonomous robots will need to cooperate together to accomplish complex mission objectives in uncertain and dynamic environments. In these environments, autonomous robots face a host of new challenges, such as responding robustly to timing uncertainties and perturbations, task and coordination failures, and equipment malfunctions. In order to address these challenges, this thesis advocates a novel planning approach, called temporally-flexible contingent planning. A temporally-flexible contingent plan is a compact encoding of methods for achieving the mission objectives which incorporates robustness through flexible task durations, redundant methods, constraints on when methods are applicable, and preferences between methods. This approach enables robots to adapt to unexpected changes on-the-fly by selecting alternative methods at runtime in order to satisfy as best possible the mission objectives. The drawback to this approach, however, is the computational overhead involved in selecting alternative methods at runtime in response to changes.(cont.) If a robot takes too long to select a new plan, it could fail to achieve its near-term mission objectives and potentially incur damage. To alleviate this problem, and extend the range of applicability of temporally-flexible contingent planning to more demanding real-time systems, this thesis proposes a temporally-flexible contingent plan executive that selects new methods quickly and optimally in response to changes in a robot's health and environment. We enable fast and optimal method selection through two complimentary approaches. First, we frame optimal method selection as a constraint satisfaction problem (CSP) variant, called an Optimal Conditional CSP (OCCSP). Second, we extend fast CSP search algorithms, such as Dynamic Backtracking and Branch-and-Bound Search, to solve OCCSPs. Experiments on an autonomous rover test-bed and on randomly generated plans show that these contributions significantly improve the speed at which robots perform optimal method selection in response to changes in their health status and environment.by Robert T. Effinger, IV.S.M

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