Design-to-time real-time scheduling

Garvey, Alan; Lesser, Victor

doi:10.1109/21.257749

Cited by 126 publications

(61 citation statements)

References 12 publications

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“…Dean, Kaelbling, Kirman, and Nicholson (1993) also adapt the technique for scheduling deliberation and execution when planning in the face of uncertainty. Garvey and Lesser (1993) present design-to-time methods that advocate using all available time to find the best possible solution. Unlike anytime approaches that can be interrupted at any time, the design-to-time method requires the time deadline to be given upfront.…”

Section: Related Workmentioning

confidence: 99%

Heuristic Search When Time Matters

Burns¹,

Ruml²,

B.³

2013

jair

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In many applications of shortest-path algorithms, it is impractical to find a provably optimal solution; one can only hope to achieve an appropriate balance between search time and solution cost that respects the user's preferences. Preferences come in many forms; we consider utility functions that linearly trade-off search time and solution cost. Many natural utility functions can be expressed in this form. For example, when solution cost represents the makespan of a plan, equally weighting search time and plan makespan minimizes the time from the arrival of a goal until it is achieved. Current state-of-theart approaches to optimizing utility functions rely on anytime algorithms, and the use of extensive training data to compute a termination policy. We propose a more direct approach, called Bugsy, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. We describe a new method based on off-line parameter tuning and a novel benchmark domain for planning under time pressure based on platform-style video games. We then present what we believe to be the first empirical study of applying anytime monitoring to heuristic search, and we compare it with our proposals. Our results suggest that the parameter tuning technique can give the best performance if a representative set of training instances is available. If not, then Bugsy is the algorithm of choice, as it performs well and does not require any off-line training. This work extends the tradition of research on metareasoning for search by illustrating the benefits of embedding lightweight reasoning about time into the search algorithm itself.

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Section: Related Workmentioning

confidence: 99%

Heuristic Search When Time Matters

Burns¹,

Ruml²,

B.³

2013

jair

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“…Notable examples are: anytime algorithms that trade reasoning time for solution quality [Boddy and Dean, 1989], design-to-time scheduling algorithms for maximizing the use of available resources while meeting deadlines on critical tasks [Garvey and Lesser, 1993], reactive systems that provide bounded response times for specified events [Rosenschein and Kaelbling, 1986] or flexible adaptation to unanticipated event orderings [Agre and Chapman, 1987;Nilsson, 1989], approximate processing techniques that provide acceptably degraded responses when resources are short [Decker et al, 1990]. We view these approaches as useful capabilities that we would strive to integrate within our architecture.…”

Section: Other Related Workmentioning

confidence: 99%

An Architecture for Adaptive Intelligent Systems.

Hayes-Roth¹

1993

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We identify a class of niches to be occupied by 'adaptive intelligent sy^ms (AISs)'. In contrast with niches occupied by typ ical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. We have designed and implemented an agent architecture that supports all of different kinds o adaptation by exploiting a single underlying t heoretical concept: An agent dynamicallA co n structs explicit control plans to guide its choices among situation-triggered behaviors. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring. AbstractOur goal is to understand and build comprehensive agents that function effectively in challenging niches. In particular, we identify a class of niches to be occupied by "adaptive intelligent systems (AISs)." In contrast with niches occupied by typical Al

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“…The advantages of flexible times frameworks have been demonstrated in various centralized planning and scheduling contexts (e.g., [12,8,9,10,11]). However their use in distributed problem solving settings has been quite sparse ( [7] is one exception), and prior approaches to multi-agent scheduling (e.g., [6,13,5]) have generally operated with fixed-times representations of agent schedules.…”

Section: Introductionmentioning

confidence: 99%

Distributed management of flexible times schedules

Smith

Gallagher

Zimmerman

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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We consider the problem of managing schedules in an uncertain, distributed environment. We assume a team of collaborative agents, each responsible for executing a portion of a globally pre-established schedule, but none possessing a global view of either the problem or solution. The goal is to maximize the joint quality obtained from the activities executed by all agents, given that, during execution, unexpected events will force changes to some prescribed activities and reduce the utility of executing others. We describe an agent architecture for solving this problem that couples two basic mechanisms: (1) a "flexible times" representation of the agent's schedule (using a Simple Temporal Network) and (2) an incremental rescheduling procedure. The former hedges against temporal uncertainty by allowing execution to proceed from a set of feasible solutions, and the latter acts to revise the agent's schedule when execution is forced outside of this set of solutions or when execution events reduce the expected value of this feasible solution set. Basic coordination with other agents is achieved simply by communicating schedule changes to those agents with inter-dependent activities. Then, as time permits, the core local problem solving infra-structure is used to drive an inter-agent option generation and query process, aimed at identifying opportunities for solution improvement through joint change. Using a simulator to model the environment, we compare the performance of our multi-agent system with that of an expected optimal (but non-scalable) centralized MDP solver.

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Design-to-time real-time scheduling

Cited by 126 publications

References 12 publications

Heuristic Search When Time Matters

Heuristic Search When Time Matters

An Architecture for Adaptive Intelligent Systems.

Distributed management of flexible times schedules

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