Optimal schedules for monitoring anytime algorithms

Finkelstein, Lev; Markovitch, Shaul

doi:10.1016/s0004-3702(00)00072-2

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…This allocation may be performed prior to execution [1,11], in particular when the anytime behavior is predictable and consistent between instances, or during execution [10] by monitoring the anytime behavior of the algorithm. It is worth noting that monitoring the algorithm can possibly take a significant amount of time, in which case the monitorization needs to be scheduled accordingly [7].…”

Section: Related Workmentioning

confidence: 99%

A model of anytime algorithm performance for bi-objective optimization

2020

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Related Workmentioning

confidence: 99%

A model of anytime algorithm performance for bi-objective optimization

2020

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“…In the context of such a real-time system with many anytime algorithms cyber and physical co-design can be made more robust by providing a supervisory monitor that accounts for uncertainty in the algorithm itself, as well as the cost of monitoring the process [ 112 ]. Optimal scheduling of anytime algorithms has also garnered attention [ 113 ]. However, much of the difficulty in computational resource management stems from an inability to use such algorithms, as they lack support in many real-time operating systems (RTOSs) and because of their inherent non-determinism [ 114 ].…”

Section: Control Of Cyber-physical Vehicle Systemsmentioning

confidence: 99%

Optimization and Control of Cyber-Physical Vehicle Systems

Bradley

Atkins

2015

Sensors

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A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.

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“…A major question that arises in utilizing this class of algorithms is how to optimally decide when to stop. For instance, Finkelstein and Markovitch (2001) developed algorithms that design an optimal query schedule to detect when a given goal has been fulfilled. Their aim was to minimize the number of queries (which are time consuming) to reach the goal.…”

Section: Exploration-exploitation Problemsmentioning

confidence: 99%

Decision Making with Dynamic Uncertain Events

Kalech

Reches

2015

jair

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When to make a decision is a key question in decision making problems characterized by uncertainty. In this paper we deal with decision making in environments where information arrives dynamically. We address the tradeoff between waiting and stopping strategies. On the one hand, waiting to obtain more information reduces uncertainty, but it comes with a cost. Stopping and making a decision based on an expected utility reduces the cost of waiting, but the decision is based on uncertain information. We propose an optimal algorithm and two approximation algorithms. We prove that one approximation is optimistic -waits at least as long as the optimal algorithm, while the other is pessimistic -stops not later than the optimal algorithm. We evaluate our algorithms theoretically and empirically and show that the quality of the decision in both approximations is near-optimal and much faster than the optimal algorithm. Also, we can conclude from the experiments that the cost function is a key factor to chose the most effective algorithm.

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Optimal schedules for monitoring anytime algorithms

Cited by 7 publications

References 20 publications

A model of anytime algorithm performance for bi-objective optimization

A model of anytime algorithm performance for bi-objective optimization

Optimization and Control of Cyber-Physical Vehicle Systems

Decision Making with Dynamic Uncertain Events

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