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![Progress of the active learning algorithm for learning the decision boundary for SVR region by Virginia model. As a new round starts, new boundary points are discovered in the parameter space. This is followed by running simulation to label points in the ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document} neighborhood of the boundary point. At the end of the round, the classifier is trained with the updated training set](publication/360527209/figure/fig5/AS:11431281093109624@1667099477512/Progress-of-the-active-learning-algorithm-for-learning-the-decision-boundary-for-SVR.png)
Progress of the active learning algorithm for learning the decision boundary for SVR region by Virginia model. As a new round starts, new boundary points are discovered in the parameter space. This is followed by running simulation to label points in the ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document} neighborhood of the boundary point. At the end of the round, the classifier is trained with the updated training set
Source publication
We develop a methodology for comparing agent-based models that are developed for the same domain, but may differ in the data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to...
Citations
The software for distributed intelligent systems, also known as Multi-agent Systems (MASs), must possess the capability to comply with environmental constraints, such as norms, and exhibit self-adaptive mechanisms to autonomously modify their behavior in response to contextual changes and handle adverse situations independently. However, there is a lack of comprehensive understanding of the concepts of self-adaptation and normative systems, as well as their behavior and interactions, which is often not adequately supported by software tools. This research introduces an extension to the Framework for Normative Agent Java Simulation (JSAN), enabling implement normative adaptive agents based on an architecture capable of dealing with norms through self-adaptation. The extended framework provides support for the key properties, namely self-adaptation, norms, and normative reasoning. To validate the efficacy of this approach, a virtual marketplace study case is presented, showcasing the agents' ability to adapt to norms while facilitating user transactions and product purchases.
