1,720,955 research outputs found
Generalized Models of Sequential Decision-Making under Uncertainty
No full textSequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable spaces. Second, we propose a unified model of reinforcement learning algorithms that employ the principle of optimism in the face of uncertainty. This model is used to explain why these methods are efficient. We use the developed theory to design novel algorithms. Depending on the user's needs, these algorithms can either automate the decision-making process completely, or provide advice in decision-support systems.We start with presenting the basic concepts from the theory of decision-making and discuss the two approaches to it: planning and reinforcement learning. We look at a few typical sequential decision-making problems of increasing difficulty. In particular, we present a game that involves grid navigation and the problems of warehouse management and wind farm operation. Next, we survey the state-of-the-art methods for solving such problems.Based on this analysis, we identify the following research opportunities. In planning, models with non-stationary and countably-infinite data remain relatively untreated because they are equivalent to infinitely-dimensional optimization problems, which are notoriously difficult to solve even approximately. In reinforcement learning, optimistic approaches lead to computational efficiency, yet the theory of optimism remains undeveloped. Moreover, while reinforcement learning shines at playing games, such as chess, shōgi, Go, and StarCraft II, its practical applications remain few.Next, we overview a mathematical framework of sequential decision-making under uncertainty known as the Markov decision process. We explain how the goal of the decision-maker can be expressed as an optimization problem and present two approaches to achieving this goal. The first—more common—approach assigns so-called values to different actions. The other approach uses so-called occupancies that tell how often the agent should choose the actions instead of evaluating how good these actions are. In fact, the two approaches are known to be dual to each other. While this duality is well studied in the finite case, the infinite case is less explored. To address this knowledge gap, we present a new dual formulation for countable problems, both finite and infinite.Afterwards, we use the dual formulation to design a new planning algorithm for infinite-horizon problems with non-stationary data. These problems are essentially infinite-dimensional optimization problems and as such are impossible to solve exactly using the standard approaches. We show that they can be solved by changing what is defined as optimal behavior: instead of seeking universally optimal policies, we consider initial-decision-optimal ones. Instead of planning all of the actions beforehand, these policies can be used to plan given the currently observed data. When the next decision is required, the process can be repeated in the same manner, leading to an optimal decision-making strategy. Our approach uses the occupancy-value duality to rule out suboptimal actions based on so-called truncations: finite-time approximations of the infinite-horizon decision-making problem.We extend the truncation approach to a more general setting of decision-making problems with countably-infinite state spaces. Instead of time-based truncations, we consider state-based ones. This allows us to limit the amount of data required to make the decisions and to design an algorithm for a class of problems that are otherwise unsolvable to optimality. This approach belongs to a family of methods called policy iteration: starting from an initial policy, it constructs a series of improvements in the decisions while ruling out choices that are provably suboptimal.After that, we turn to reinforcement learning. For a long time, the only provably efficient reinforcement-learning methods were model-based ones; recently, a family of model-free optimistic methods emerged, each of them accompanied by an analysis of how sample-efficient the method is. We, too, study optimistic reinforcement learning, but in contrast to the existing research, we seek to understand not how efficient it is, but why it is efficient. Our analysis results in a formula that explains the three factors that cause regret—the efficiency loss—in optimistic reinforcement learning: the problem size, the measure of exploration, and the estimation error caused by the mismatch between the realized transitions and their true distribution. It can be applied to all of the existing algorithms as well as new ones. We design one such new algorithm and show how our theoretical framework can facilitate the proof of its efficiency.Finally, we consider a high-impact real-world sequential decision-making problem known as active wake control. Wind turbines can negatively impact each other with their wakes. These wake-induced losses can be reduced by changing the turbine orientations. Unfortunately, the optimal control strategy is non-trivial. To address this, existing approaches use simplified wake models in combination with numerical optimization methods; instead we propose to use model-free reinforcement learning. As a first step towards this goal, we present a wind farm simulator that is suitable for reinforcement learning and better reflects the realities of wind farm operation than other existing tools. Using this simulator, we show that previous research used a suboptimal action representation in this problem; we identify two alternatives, both of which improve the learning efficiency. Additionally, we demonstrate that reinforcement learning is robust to errors in the observations, providing further evidence that it is a fitting approach to active wake control.Our contributions advance the state of the art in the theory of sequential decision-making under uncertainty and its applications. These advances hint at unexplored connections between countably-infinite planning and optimistic learning, which may lead to even more efficient algorithms for sequential decision-making under uncertainty in the future.Algorithmic
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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