ترغب بنشر مسار تعليمي؟ اضغط هنا

RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation

50   0   0.0 ( 0 )
 نشر من قبل Huma Samin
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف Huma Samin




اسأل ChatGPT حول البحث

Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e.g., avoiding costs vs. providing reliable service). To enable researchers to evaluate and compare decision-making techniques for self-adaptation, we present the RDMSim exemplar. RDMSim enables researchers to evaluate and compare techniques for decision-making under environmental uncertainty that support self-adaptation. The focus of the exemplar is on the domain problem related to Remote Data Mirroring, which gives opportunity to face the challenges described above. RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop. Specifically, the paper presents (i) RDMSim, a simulator for real-world experimentation, (ii) a set of realistic simulation scenarios that can be used for experimentation and comparison purposes, (iii) data for the sake of comparison.



قيم البحث

اقرأ أيضاً

Value-based methods for reinforcement learning lack generally applicable ways to derive behavior from a value function. Many approaches involve approximate value iteration (e.g., $Q$-learning), and acting greedily with respect to the estimates with a n arbitrary degree of entropy to ensure that the state-space is sufficiently explored. Behavior based on explicit greedification assumes that the values reflect those of textit{some} policy, over which the greedy policy will be an improvement. However, value-iteration can produce value functions that do not correspond to textit{any} policy. This is especially relevant in the function-approximation regime, when the true value function cant be perfectly represented. In this work, we explore the use of textit{inverse policy evaluation}, the process of solving for a likely policy given a value function, for deriving behavior from a value function. We provide theoretical and empirical results to show that inverse policy evaluation, combined with an approximate value iteration algorithm, is a feasible method for value-based control.
As part of a plan launched by the Ministry of Health of Brazil to increase the availability of linear accelerators for radiotherapy treatment for the whole country, for which Varian Medical Systems company has won the bidding, a technical cooperation agreement was signed inviting Brazilian Scientific and Technological Institutions to participate in a technology transfer program. As a result, jointly, the Eldorado Research Institute and the Center for Biomedical Engineering of the University of Campinas presents in this work, the concepts behind of a proposed rule engine to aid in the evaluation and decision-making in radiotherapy treatment planning. Normally, the determination of the radiation dose for a given patient is a complex and intensive procedure, which requires a lot of domain knowledge and subjective experience from the oncologists team. In order to help them in this complex task, and additionally, provide an auxiliary tool for less experienced oncologists, it is presented a project conception of a software system that will make use of a hybrid data-oriented approach. The proposed rule engine will apply both inference mechanism and expression evaluation to verify and accredit the quality of an external beam radiation treatment plan by considering, at first, the 3D-conformal radiotherapy (3DCRT) technique.
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy is not the end goal when the results are used for decision-making , for example in personalized medicine or economics. We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal, and we introduce a novel active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision. We compare our decision-making-aware active learning strategy to existing alternatives on both simulated and real data, and show improved performance in decision-making accuracy.
Ajax applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, Ajax still suffers from the limitations of the Webs request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for Ajax technology. It also shows the results of an empirical study comparing push and pull.
116 - Weixin Liang , James Zou , Zhou Yu 2020
Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limite d number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا