York, England, United Kingdom

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Department of Biology
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Department of Health Sciences
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Department of Chemistry
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  • [Show abstract] [Hide abstract]
    ABSTRACT: Monte Carlo Tree Search (MCTS) has produced many breakthroughs in search-based decision-making in games and other domains. There exist many general-purpose enhancements for MCTS, which improve its efficiency and effectiveness by learning information from one part of the search space and using it to guide the search in other parts. We introduce the Information Capture And ReUse Strategy (ICARUS) framework for describing and combining such enhancements. We demonstrate the ICARUS framework's usefulness as a frame of reference for understanding existing enhancements, combining them, and designing new ones. We also use ICARUS to adapt some well-known MCTS enhancements (originally designed for games of perfect information) to handle information asymmetry between players and randomness, features which can make decision-making much more difficult. We also introduce a new enhancement designed within the ICARUS framework, EPisodic Information Capture and reuse (EPIC), designed to exploit the episodic nature of many games. Empirically we demonstrate that EPIC is stronger and more robust than existing enhancements in a variety of game domains, thus validating ICARUS as a powerful tool for enhancement design within MCTS.
    Artificial Intelligence 12/2014;
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    ABSTRACT: Community-based fisheries management (CBFM) strategies have been adopted in a variety of small-scale fisheries around the world. Within these management structures, leaders are increasingly regarded as essential for viable CBFM yet systematic analysis into the intricate mechanisms of leadership are limited. This paper aims to identify key knowledge gaps of leadership in CBFM by strategically reviewing research from fisheries and natural resource management, and from other sectors. The focus is on the interaction between leaders, their connections with and beyond their communities, and the context within which leaders function. Insights from over 30 case studies suggest previous work on leaders and leadership generally focused on relatively coarse-scale characteristics of leadership and the functions that leaders perform. Ecological and social context influence leaders׳ ability to help deliver successful CBFM. The personal and professional attributes of leaders themselves may be beneficial or inhibitory for CBFM depending on that context. It is therefore essential that future research builds on current insight in order to decipher the implications of contextual influences on local leadership and, by extension, the level of CBFM success.
    Marine Policy 12/2014; 50:261–269.
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    ABSTRACT: Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition. One of the problems encountered in the analysis of such data is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean dissimilarities. Such non-Euclidean dissimilarities are often corrected or a consistent Euclidean geometry is imposed on them via embedding. This paper commences by reviewing the available algorithms for analysing non-Euclidean dissimilarity data. The novel contribution is to show how the Ricci flow can be used to embed and rectify non-Euclidean dissimilarity data. According to our representation, the data is distributed over a manifold consisting of patches. Each patch has a locally uniform curvature, and this curvature is iteratively modified by the Ricci flow. The raw dissimilarities are the geodesic distances on the manifold. Rectified Euclidean dissimilarities are obtained using the Ricci flow to flatten the curved manifold by modifying the individual patch curvatures. We use two algorithmic components to implement this idea. Firstly, we apply the Ricci flow independently to a set of surface patches that cover the manifold. Second, we use curvature regularisation to impose consistency on the curvatures of the arrangement of different surface patches. We perform experiments on three real world datasets, and use these to determine the importance of the different algorithmic components, i.e. Ricci flow and curvature regularisation. We conclude that curvature regularisation is an essential step needed to control the stability of the piecewise arrangement of patches under the Ricci flow.
    Pattern Recognition 11/2014; 47(11):3709–3725.

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Journal of Educational Psychology 07/2010; 102(3):741-756.
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