1,720,965 research outputs found
A Game-Theoretic Framework for Generic Second-Order Traffic Flow Models Using Mean Field Games and Adversarial Inverse Reinforcement Learning
No full textA traffic system can be interpreted as a multiagent system, wherein vehicles
choose the most efficient driving approaches guided by interconnected goals or strategies.
This paper aims to develop a family of mean field games (MFG) for generic second-order
traffic flow models (GSOM), in which cars control individual velocity to optimize their
objective functions. GSOMs do not generally assume that cars optimize self-interested
objectives, so such a game-theoretic reinterpretation offers insights into the agents’ under
lying behaviors. In general, an MFG allows one to model individuals on a microscopic
level as rational utility-optimizing agents while translating rich microscopic behaviors to
macroscopic models. Building on the MFG framework, we devise a new class of second-
order traffic flow MFGs (i.e., GSOM-MFG), which control cars’ acceleration to ensure
smooth velocity change. A fixed-point algorithm with fictitious play technique is devel
oped to solve GSOM-MFG numerically. In numerical examples, different traffic patterns
are presented under different cost functions. For real-world validation, we further use an
inverse reinforcement learning approach (IRL) to uncover the underlying cost function on
the next-generation simulation (NGSIM) data set. We formulate the problem of inferring
cost functions as a min-max game and use an apprenticeship learning algorithm to solve
for cost function coefficients. The results show that our proposed GSOM-MFG is a generic
framework that can accommodate various cost functions. The Aw Rascle and Zhang
(ARZ) and Light-Whitham-Richards (LWR) fundamental diagrams in traffic flow models
belong to our GSOM-MFG when costs are specified
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Machine Learning Methods for Large Population Games with Applications in Operations Research
Get PDFIn this tutorial, we provide an introduction to machine learning methods for
finding Nash equilibria in games with large number of agents. These types of
problems are important for the operations research community because of their
applicability to real life situations such as control of epidemics, optimal
decisions in financial markets, electricity grid management, or traffic control
for self-driving cars. We start the tutorial by introducing stochastic optimal
control problems for a single agent, in discrete time and in continuous time.
Then, we present the framework of dynamic games with finite number of agents.
To tackle games with a very large number of agents, we discuss the paradigm of
mean field games, which provides an efficient way to compute approximate Nash
equilibria. Based on this approach, we discuss machine learning algorithms for
such problems. First in the context of discrete time games, we introduce fixed
point based methods and related methods based on reinforcement learning.
Second, we discuss machine learning methods that are specific to continuous
time problems, by building on optimality conditions phrased in terms of
stochastic or partial differential equations. Several examples and numerical
illustrations of problems arising in operations research are provided along the
way.Comment: 39 pages, 11 figure
A Machine Learning Method for Stackelberg Mean Field Games
Get PDFWe propose a single-level numerical approach to solve Stackelberg mean field
game (MFG) problems. In Stackelberg MFG, an infinite population of agents play
a non-cooperative game and choose their controls to optimize their individual
objectives while interacting with the principal and other agents through the
population distribution. The principal can influence the mean field Nash
equilibrium at the population level through policies, and she optimizes her own
objective, which depends on the population distribution. This leads to a
bi-level problem between the principal and mean field of agents that cannot be
solved using traditional methods for MFGs. We propose a reformulation of this
problem as a single-level mean field optimal control problem through a
penalization approach. We prove convergence of the reformulated problem to the
original problem. We propose a machine learning method based on (feed-forward
and recurrent) neural networks and illustrate it on several examples from the
literature.Comment: 47 pages, 9 figures, 4 table
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