Criteria-directed task scheduling

Wagner, Thomas; Garvey, Alan; Lesser, Victor

doi:10.1016/s0888-613x(98)10006-3

Cited by 54 publications

(43 citation statements)

References 4 publications

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Section: Dtc Planner / Initial Schedulermentioning

confidence: 99%

“…This criteria specifies, for example, the desired balance between plan quality and duration, or what level of uncertainty is tolerable (Wagner, Garvey, & Lesser 1997). More information covering the techniques DTC uses can be found in (Wagner, Garvey, &: Lesser 1998).Returning to our example, DTC is used to select the most appropriate set of actions from the initialization task structure. In this case, it has only one valid plan: Init, Calibrate, and Send-Message.…”

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confidence: 99%

“…This information is represented, along with other goal achievement and alternative plan information, in a T/EMS task structure (Decker & Lesser 1993;Horling et al 1999) (discussed in more detail later). A planning component, the Design-to-Criteria scheduler (DTC) (Wagner, Garvey, & Lesser 1998;, uses these T/EMS task structures, along with the quantitative knowledge of action interdependence and deadlines, to select the most appropriate plan given current environmental conditions. This plan is used by the Partial Order Scheduler process to determine when individual actions should be performed, either sequentially or in parallel, within the given precedence and runtime resource constraints.…”

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confidence: 99%

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The Soft Real-Time Agent Control Architecture

Horling

Lesser

Vincent

et al. 2005

Auton Agent Multi-Agent Syst

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Real-time control has become increasingly important as technologies are moved from the lab into real world situations. The complexity associated with these systems increases as control and autonomy are distributed, due to such issues as precedence constraints, shared resources, and the lack of a complete and consistent world view. In this paper we describe a soft real-time architecture designed to address these requirements, motivated by challenges encountered in a distributed sensor allocation environment. The system features the ability to generate schedules respecting temporal, structural and resource constraints, to merge new goals with existing ones, and to detect and handle unexpected results from activities. We will cover a suite of technologies being employed, including quantitative task representation, alternative plan selection, partial-order scheduling, schedule consolidation and conflict resolution in an uncertain environment. Technologies which facilitate on-line real-time control, including schedule caching and variable time granularities are also discussed. OverviewIn the field of multi-agent systems, much of the research and most of the discussion focuses on the dynamics and interactions between agents and agent groups. Just as *Effort sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory Air Force Materiel Command, USAF, under agreements number F30602-99-2-0525 and DOD DABT63-99-1-0004. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This material is also based upon work supported by the National Science Foundation under Grants No. IIS-9812755 and IIS-9988784. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Furthermore, the views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory or the U.S. Government. Copyright (E) 2002, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. important, however, is the design and behavior of the individual agents themselves. The efficiency of an agent's internal mechanics contribute to the foundation of the system as a whole, and the degree of flexibility these mechanics offer affect the agent's achievable level of sophistication, particularly in its interactions with other agents (Lesser 1991;1998). We believe that a general control architecture, responsible for both the planning for the achievement of temporally constrained goals of varying worth, and the sequencing of actions local to the agent that have resource requirements, can provide a robust and reusable platform on which to build high level reasoning compo...

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Section: Dtc Planner / Initial Schedulermentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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The Soft Real-Time Agent Control Architecture

Horling

Lesser

Vincent

et al. 2005

Auton Agent Multi-Agent Syst

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“…The connection between these two is necessary because the generation of suitable constraints must take into account characteristics of the current solution to the local agent optimization problem, e.g., when resources are available, when tasks will be performed, etc., and also the implications of change such as performing an unplanned task at a particular time. In our current work, the agent's local optimization expert is the Design-to-Criteria Scheduler (DTC) [67,71,73] or another module built on top of DTC [28], all of which are descended from the Design-to-Time (DTT) [20] scheduler.…”

Section: Underlying Conceptual Modelmentioning

confidence: 99%

Evolution of the GPGP/TÆMS domain-independent coordination framework

Lesser

2002

Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems Part 1 - AAMAS '02

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Abstract. The GPGP/TAEMS domain-independent coordination framework for small agent groups was first described in 1992 and then more fully detailed in an ICMAS'95 paper. In this paper, we discuss the evolution of this framework which has been motivated by its use in a number of applications, including: information gathering and management, intelligent home automation, distributed situation assessment, coordination of concurrent engineering activities, hospital scheduling, travel planning, repair service coordination and supply chain management. First, we review the basic architecture of GPGP and then present extensions to the TAEMS domain-independent representation of agent activities. We next describe extensions to GPGP that permit the representation of situation-specific coordination strategies and social laws as well as making possible the use of GPGP in large agent organizations. Additionally, we discuss a more encompassing view of commitments that takes into account uncertainty in commitments. We then present new coordination mechanisms for use in resource sharing and contracting, and more complex coordination mechanisms that use a cooperative search among agents to find appropriate commitments. We conclude with a summary of the major ideas underpinning GPGP, an analysis of the applicability of the GPGP framework including performance issues, and a discussion of future research directions.

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“…Another closely related stream of research includes the "design-to-time" (DTT) [2] and "design to criteria" (DTC) [11] efforts, which consider agent deliberation scheduling in the context of TAEMS task models. These task models capture some of the characteristics of our AMP problem (e.g., alternative atomic deliberation actions, success probabilities) and also can include more complex aspects such as task decomposition, interdependencies, and resource constraints.…”

Section: Design To Criteriamentioning

confidence: 99%

Managing Online Self-adaptation in Real-Time Environments

Goldman

Musliner

Krebsbach

2003

Self-Adaptive Software: Applications

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Abstract. This paper provides a solution to the deliberation scheduling problem for self-adaptive hard real time intelligent control using the Self-Adaptive Cooperative Intelligent Real-Time Control Architecture (SA-CIRCA). For self-adaptive software, deliberation scheduling is the problem of deciding what aspects of the artifact should be improved, what methods of improvement should be chosen, and how much time should be devoted to each of these activities. The time spent in deliberation scheduling must be carefully controlled because it is time not available for the primary self-adaptation task. We provide a Markov Decision Process (MDP) model for deliberation scheduling in SA-CIRCA. Directly solving this MDP is not feasible for even relatively modest domains. We provide a polynomial time greedy (myopic) approximation algorithm. We evaluate this approximation against a "gold-standard" provided by the dynamic programming (value iteration) algorithm for MDPs. Our experimental results show that the approximation produces competitive solutions very quickly.

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Criteria-directed task scheduling

Cited by 54 publications

References 4 publications

The Soft Real-Time Agent Control Architecture

The Soft Real-Time Agent Control Architecture

Evolution of the GPGP/TÆMS domain-independent coordination framework

Managing Online Self-adaptation in Real-Time Environments

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