The best-of-n problem (Valentini et al. in Front Robot AI 4(9): 1-18, 2017) is one of the decision-making problems in which many robots (agents) select the best option among a set of n alternatives and are focused on ...
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The best-of-n problem (Valentini et al. in Front Robot AI 4(9): 1-18, 2017) is one of the decision-making problems in which many robots (agents) select the best option among a set of n alternatives and are focused on the field of Swarm Robotics. Almost all of the previous studies focused on binary decision-making scenarios (n = 2) and could not be applied without any change in the case of n > 2. It is necessary to satisfy constraints on the number of robots N, or the time required for reaching the best option is abruptly increased. Therefore, it is required to construct a method that can deal with n > 2. In this paper, we propose an algorithm (BRT model, bias and rising threshold model) in which the time and the possibility of reaching agreement are not dependent on the number of robots N even when n > 2. By computer experiments, our claims are verified within the tested parameter ranges.
The best-of-n problem (BSTn) is a collective decision-making problem in which a swarm of robots needs to make a collective decision about a set of n choices;specifically, to decide what choice offers the best alternat...
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The best-of-n problem (BSTn) is a collective decision-making problem in which a swarm of robots needs to make a collective decision about a set of n choices;specifically, to decide what choice offers the best alternative [1]. The BSTn captures the structure and logic of the discrete consensus achievement problems that appear in several swarm robotics scenarios. Although numerous algorithms have been proposed recently to deal with more than two choices, the number of choices that can be dealt with is not large. The bias and raising threshold (BRT) algorithm proposed by Phung et al. [2] enables swarms to deal with a large number of choices (n >> 2). However, the algorithm's goodness has not been evaluated in any practical problems, and it is necessary to evaluate the algorithm in a problem where a large number of choices exist. In this paper, we consider the best of proportions (BOP) problem;that is a version of BSTn in which a large number of choices can be dealt with by adjusting the values of different proportions. In previous research on swarms that needed to solve the BOP problem, there is only a study on the response threshold models for the division of labor. In the present study, we investigate a scenario of the BOP and apply the BRT algorithm to find the best proportion. In our previous work [3], a fixed proportion setting method has been adopted. Here, we adopt a stochastic proportion setting method to verify the relationship between the efficiency and the number of choices in a more general case. The results show that with a larger number of choices, the decision making becomes more efficient with high equality;that is a result that has not been found in [3].
This article explores an interplay between process crash failures and concurrency. Namely, it aims at answering the question, "Is it possible to cope with more crash failures when some number of crashes occur bef...
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This article explores an interplay between process crash failures and concurrency. Namely, it aims at answering the question, "Is it possible to cope with more crash failures when some number of crashes occur before some predefined contention point happened?". These crashes are named lambda-constrained crashes, where lambda is the predefined contention point (known by the processes). Hence, this article considers two types of process crashes: lambda-constrained crashes and classical crashes (which can occur at any time and are consequently called any-time crashes). Considering a system made up of n asynchronous processes communicating through atomic read/write registers, the article focuses on the design of two agreement-related algorithms. Assuming lambda = n - 1 and no any-time failure, the first algorithm solves the consensus problem in the presence of one X-constrained crash failure, thereby circumventing the well-known FLP impossibility result. The second algorithm considers k-set agreement for k >= 2. It is a k-set agreement algorithm such that lambda = n - l and l >= k = m + f that works in the presence of up to (2m + l - k) lambda-constrained crashes and (f - 1) any-time crashes, i.e., up to t = (2m + l - k) (f - 1) process crashes. It follows that considering the timing of failures with respect to a predefined contention point enlarges the space of executions in which k-set agreement can be solved despite the combined effect of asynchrony, concurrency, and process crashes. The paper also presents agreement-related impossibility results for consensus and k-set agreement in the context of lambda-constrained process crashes (with or without any-time crashes). (C) 2022 Elsevier B.V. All rights reserved.
Objectives: To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review. Study Design and Setting: This was a substu...
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Objectives: To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review. Study Design and Setting: This was a substudy to a larger randomized study;the main study sought to assess the performance of single screening search results versus dual screening. This substudy assessed the performance in identifying relevant randomized controlled trials (RCTs) for a published Cochrane review of a modified version of Cochrane's Screen4Me workflow which uses crowdsourcing and machine learning. We included participants who had signed up for the main study but who were not eligible to be randomized to the two main arms of that study. The records were put through the modified workflow where a machine learning classifier divided the data set into "Not RCTs"and "Possible RCTs."The records deemed "Possible RCTs"were then loaded into a task created on the Cochrane Crowd platform, and participants classified those records as either "Potentially relevant"or "Not relevant"to the review. Using a prespecified agreement algorithm, we calculated the performance of the crowd in correctly identifying the studies that were included in the review (sensitivity) and correctly rejecting those that were not included (specificity). Results: The RCT machine learning classifier did not reject any of the included studies. In terms of the crowd, 112 participants were included in this substudy. Of these, 81 completed the training module and went on to screen records in the live task. Applying the Cochrane Crowd agreement algorithm, the crowd achieved 100% sensitivity and 80.71% specificity. Conclusions: Using a crowd to screen search results for systematic reviews can be an accurate method as long as the agreement algorithm in place is robust. Trial registration: Open Science Framework: https://***/3jyqt. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-
We clarify the relation between the model and the convergence results of Jadbabaie et al. to those of Bertsekas et al. We show that the update equations in Jadbabaie et al. are a special case of those in Bertsekas et ...
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We clarify the relation between the model and the convergence results of Jadbabaie et al. to those of Bertsekas et al. We show that the update equations in Jadbabaie et al. are a special case of those in Bertsekas et al. Furthermore, the main convergence results in Sections II and III of Jadbabaie et al. are essentially the same as those derived earlier in Bertsekas et al.
A new notion of process failure explicitly related to contention has recently been introduced by one of the authors (NETYS 2018). More precisely, given a predefined contention threshold lambda, this notion considers t...
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ISBN:
(数字)9783030032326
ISBN:
(纸本)9783030032326;9783030032319
A new notion of process failure explicitly related to contention has recently been introduced by one of the authors (NETYS 2018). More precisely, given a predefined contention threshold lambda, this notion considers the executions in which process crashes are restricted to occur only when process contention is smaller than or equal to lambda. If crashes occur after contention bypassed lambda, there are no correctness guarantees (e.g., termination is not guaranteed). It was shown that, when lambda = n-1, consensus can be solved in an n-process asynchronous read/write system despite the crash of one process, thereby circumventing the well-known FLP impossibility result. Furthermore, it was shown that when lambda = n-k and k >= 2, k-set agreement can be solved despite the crash of 2k-2 processes. This paper considers two types of process crash failures: "lambda-constrained" crash failures (as previously defined), and classical crash failures (that we call "any time" failures). It presents two algorithms suited to these types of failures. The first algorithm solves k-set agreement, where k = m + f, in the presence of t = 2m f - 1 crash failures, 2m of them being (n - k)-constrained failures, and (f -1) being any time failures. The second algorithm solves (n + f)-renaming in the presence of t = m + f crash failures, m of them being (n - t - 1)-constrained failures, and f being any time failures. It follows that the differentiation between lambda-constrained crash failures and any time crash failures enlarges the space of executions in which the impossibility of k-set agreement and renaming in the presence of asynchrony and process crashes can be circumvented. In addition to its behavioral properties, both algorithms have a noteworthy first class property, namely, their simplicity.
agreement algorithms allow individual agents in a population to estimate a global quantity by sharing information. A common example is computing the global mean of a sensor measurement from each agent. We present a pr...
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ISBN:
(纸本)9781424466757
agreement algorithms allow individual agents in a population to estimate a global quantity by sharing information. A common example is computing the global mean of a sensor measurement from each agent. We present a practical agreement algorithm, input-based consensus (IBC), that produces bounded error and recovery in the face of significant communications failures in a stochastic distributed system. We compare our algorithm to linear average consensus (LAC), which produces an exact result under ideal conditions, but is not robust to message loss. For both algorithms, we measure performance with respect to a varying percentage of dropped messages. The algorithms are examined analytically, simulated using the Stochastic Simulation algorithm, and demonstrated experimentally on a testbed of 20 robots. In all cases, the IBC algorithm produced reasonable values, even when tested with up to 90% message loss.
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