1,720,993 research outputs found
Reinforcement learning through global stochastic search in N-MDPs
No full textReinforcement Learning (RL) in either fully or partially observable domains usually poses a requirement on the knowledge representation in order to be sound: the underlying stochastic process must be Markovian. In many applications, including those involving interactions between multiple agents (e.g., humans and robots), sources of uncertainty affect rewards and transition dynamics in such a way that a Markovian representation would be computationally very expensive. An alternative formulation of the decision problem involves partially specified behaviors with choice points. While this reduces the complexity of the policy space that must be explored - something that is crucial for realistic autonomous agents that must bound search time - it does render the domain Non-Markovian. In this paper, we present a novel algorithm for reinforcement learning in Non-Markovian domains. Our algorithm, Stochastic Search Monte Carlo, performs a global stochastic search in policy space, shaping the distribution from which the next policy is selected by estimating an upper bound on the value of each action. We experimentally show how, in challenging domains for RL, high-level decisions in Non-Markovian processes can lead to a behavior that is at least as good as the one learned by traditional algorithms, and can be achieved with significantly fewer samples. © 2011 Springer-Verlag
LearnPNP: A tool for learning agent behaviors
No full textHigh-level programming of robotic agents requires the use of a representation formalism able to cope with several sources of complexity (e.g. parallel execution, partial observability, exogenous events, etc.) and the ability of the designer to model the domain in a precise way. Reinforcement Learning has proved promising in improving the performance, adaptability and robustness of plans in under-specified domains, although it does not scale well with the complexity of common robotic applications. In this paper we propose to combine an extremely expressive plan representation formalism (Petri Net Plans), with Reinforcement Learning over a stochastic process derived directly from such a plan. The derived process has a significantly reduced search space and thus the framework scales well with the complexity of the domain and allows for actually improving the performance of complex behaviors from experience. To prove the effectiveness of the system, we show how to model and learn the behavior of the robotic agents in the context of Keepaway Soccer (a widely accepted benchmark for RL) and the RoboCup Standard Platform League. © 2011 Springer-Verlag
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
Skill acquisition as a function of age, hand and task difficulty : Interactions between cognition and action
Get PDFFunding: Rachael Raw was initially funded by a Medical Research Council (MRC) PhD studentship and The Magstim Company Ltd. Analysis and publication was further supported by an MRC Centenary Early Career Post-Doctoral Fellowship awarded to Rachael Raw. The production of this manuscript was also supplemented by an MRC-ESRC grant awarded to Mark Mon-Williams (MR/N024397/1) and an Engineering and Physical Sciences Research Council (EPSRC) grant awarded to Mark Mon-Williams and Matteo Leonetti (EP/R031193/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe
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
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