Continuous Double Auctions with Execution Uncertainty

Valkenhoef, Gert van; Ramchurn, Sarvapali D.; Vytelingum, Perukrishnen; Jennings, Nicholas R.; Verbrugge, Rineke

doi:10.1007/978-3-642-15117-0_16

Cited by 4 publications

(3 citation statements)

References 10 publications

(12 reference statements)

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“…Among them we can find general games (Bowling and Veloso 2004;Archibald and Shoham 2011), where agents cannot execute actions with certainty and security games (Yin et al 2011;Jiang et al 2013), where it cannot always be assumed that actions undertaken by the security agents will be executed as planned. Auction settings (Van Valkenhoef et al 2010), when goods, services, or payments may fail to occur as desired, are an example too.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Execution Skill Estimation

Archibald

Nieves-Rivera

2019

AAAI

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The performance of agents in many domains with continuous action spaces depends not only on their ability to select good actions to execute, but also on their ability to execute planned actions precisely. This ability, which has been called an agent’s execution skill, is an important characteristic of an agent which can have a significant impact on their success. In this paper, we address the problem of estimating the execution skill of an agent given observations of that agent acting in a domain. Each observation includes the executed action and a description of the state in which the action was executed and the reward received, but notably excludes the action that the agent intended to execute. We previously introduced this problem and demonstrated that estimating an agent’s execution skill is possible under certain conditions. Our previous method focused entirely on the reward that the agent received from executed actions and assumed that the agent was able to select the optimal action for each state. This paper addresses the execution skill estimation problem from an entirely different perspective, focusing instead on the action that was executed. We present a Bayesian framework for reasoning about action observations and show that it is able to outperform previous methods under the same conditions. We also show that the flexibility of this framework allows it to be applied in settings where the previous limiting assumptions are not met. The success of the proposed method is demonstrated experimentally in a toy domain as well as the domain of computational billiards.

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Section: Related Workmentioning

confidence: 99%

Bayesian Execution Skill Estimation

Archibald

Nieves-Rivera

2019

AAAI

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“…Among them, we can find general games (Bowling & Veloso, 2004;Archibald & Shoham, 2011), where agents cannot execute actions with certainty, and security games (Yin et al, 2011;Jiang et al, 2013), where it cannot always be assumed that actions undertaken by the security agents will be executed as planned. Auction settings (Van Valkenhoef et al, 2010), when goods, services, or payments may fail to occur as desired, are also an example.…”

Section: Execution Uncertaintymentioning

confidence: 99%

Estimating Agent Skill in Continuous Action Domains

Archibald,

Nieves-Rivera

2024

jair

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Actions in most real-world continuous domains cannot be executed exactly. An agent’s performance in these domains is influenced by two critical factors: the ability to select effective actions (decision-making skill), and how precisely it can execute those selected actions (execution skill). This article addresses the problem of estimating the execution and decision-making skill of an agent, given observations. Several execution skill estimation methods are presented, each of which utilize different information from the observations and make assumptions about the agent’s decision-making ability. A final novel method forgoes these assumptions about decision-making and instead estimates the execution and decision-making skills simultaneously under a single Bayesian framework. Experimental results in several domains evaluate the estimation accuracy of the estimators, especially focusing on how robust they are as agents and their decision-making methods are varied. These results demonstrate that reasoning about both types of skill together significantly improves the robustness and accuracy of execution skill estimation. A case study is presented using the proposed methods to estimate the skill of Major League Baseball pitchers, demonstrating how these methods can be applied to real-world data sources.

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“…The requests for resource under cloud environment typically exhibit strong volatility. To ensure the credibility of dynamic resources without affecting its service efficiency, researchers propose a more credible dynamic resource provision strategy [19] .…”

Section: Dynamic Resource Assignment On the Basis Of Credibilitymentioning

confidence: 99%

Survey on Resource Allocation Policy and Job Scheduling Algorithms of Cloud Computing1

Lu¹,

Chen²,

Hu³

2013

JSW

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Cloud computing is the product of the evolution of calculation. It is a new distributed computing model. As more and more people put into the research and applications on cloud computing, the technology of computing becomes more and more widely used. Cloud computing has a huge user group. It has to deal with a large number of tasks. How to make appropriate decisions when allocating hardware resources to the tasks and dispatching the computing tasks to resource pool has become the main issue in cloud computing. This paper is based on the current situation of resource allocation policy and job scheduling algorithms under cloud circumstance. It summarizes some methods to improve the performance, including dynamic resource allocation strategy based on the law of failure, dynamic resource assignment on the basis of credibility, ant colony optimization algorithm for resource allocation, dynamic scheduling algorithm based on threshold, optimized genetic algorithm with dual fitness and improved ant colony algorithm for job scheduling

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Continuous Double Auctions with Execution Uncertainty

Cited by 4 publications

References 10 publications

Bayesian Execution Skill Estimation

Bayesian Execution Skill Estimation

Estimating Agent Skill in Continuous Action Domains

Survey on Resource Allocation Policy and Job Scheduling Algorithms of Cloud Computing1

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