173,952 research outputs found
Knowledge Acquisition Analytical Games: games for cognitive systems design
Knowledge discovery from data and knowledge acquisition from experts are steps of paramount importance when designing cognitive systems. The literature discusses extensively on the issues related to current knowledge acquisition techniques. In this doctoral work we explore the use of gaming approaches as a knowledge acquisition tools, capitalising on aspects such as engagement, ease of use and ability to access tacit knowledge. More specifically, we explore the use of analytical games for this purpose. Analytical game for decision making is not a new class of games, but rather a set of platform independent simulation games, designed not for entertainment, whose main purpose is research on decision-making, either in its complete dynamic cycle or a portion of it (i.e. Situational Awareness). Moreover, the work focuses on the use of analytical games as knowledge acquisition tools. To this end, the Knowledge Acquisition Analytical Game (K2AG) method is introduced. K2AG is an innovative game framework for supporting the knowledge acquisition task. The framework introduced in this doctoral work was born as a generalisation of the Reliability Game, which on turn was inspired by the Risk Game. More specifically, K2AGs aim at collecting information and knowledge to be used in the design of cognitive systems and their algorithms. The two main aspects that characterise those games are the use of knowledge cards to render information and meta-information to the players and the use of an innovative data gathering method that takes advantage of geometrical features of simple shapes (e.g. a triangle) to easily collect players\u2019 beliefs. These beliefs can be mapped to subjective probabilities or masses (in evidence theory framework) and used for algorithm design purposes. However, K2AGs might use also different means of conveying information to the players and to collect data. Part of the work has been devoted to a detailed articulation of the design cycle of K2AGs. More specifically, van der Zee\u2019s simulation gaming design framework has been extended in order to account for the fact that the design cycle steps should be modified to include the different kinds of models that characterise the design of simulation games and simulations in general, namely a conceptual model (platform independent), a design model (platform independent) and one or more implementation models (platform dependent). In addition, the processes that lead from one model to the other have been mapped to design phases of analytical wargaming. Aspects of game validation and player experience evaluation have been addressed in this work. Therefore, based on the literature a set of validation criteria for K2AG has been proposed and a player experience questionnaire for K2AGs has been developed. This questionnaire extends work proposed in the literature, but a validation has not been possible at the time of writing. Finally, two instantiations of the K2AG framework, namely the Reliability Game and the MARISA Game, have been designed and analysed in details to validate the approach and show its potentialities
Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
Cooking typically involves a plethora of decisions about ingredients and
tools that need to be chosen in order to write a good cooking recipe. Cooking
can be modelled in an optimization framework, as it involves a search space of
ingredients, kitchen tools, cooking times or temperatures. If we model as an
objective function the quality of the recipe, several problems arise. No
analytical expression can model all the recipes, so no gradients are available.
The objective function is subjective, in other words, it contains noise.
Moreover, evaluations are expensive both in time and human resources. Bayesian
Optimization (BO) emerges as an ideal methodology to tackle problems with these
characteristics. In this paper, we propose a methodology to suggest recipe
recommendations based on a Machine Learning (ML) model that fits real and
simulated data and BO. We provide empirical evidence with two experiments that
support the adequacy of the methodology
A tool-mediated cognitive apprenticeship approach for a computer engineering course
Teaching database engineers involves a variety of learning activities. A strong focus is on practical problems that go beyond the acquisition of knowledge. Skills and experience are equally important. We propose a virtual apprenticeship model for the knowledge- and skillsoriented Web-based education of database students. We adapt the classical cognitive apprenticeship theory to the Web context utilising scaffolding and activity theory. The choice of educational media and the forms of student interaction with the media are central success criteria
A conceptual architecture for interactive educational multimedia
Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching.
A conceptual architecture for interactive educational multimedia can support the development of such multimedia systems. Such an architecture needs to embed multimedia technology into a coherent educational context. A framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, to describe them in the human-computer context, and to integrate them with mechanisms and principles of multimedia interaction
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
Market information acquisition: a prerequisite for successful strategic entrepreneurship
AbstractThis paper investigates on the types of information used by managers and entrepreneurs, so as to conduct market research and to evaluate market potential.The authors examine five major sets of variables to understand their impact on firmsâ information market search effort. Empirical results based on a survey of Greek enterprises provide support for these factors in predicting firmsâ market information acquisition. Findings on structural and administrative characteristics of the firms support the notion that companies engaged in greater market information search and evaluation of market potential tend to develop and implement complex penetration and development market strategies, in order to maximize their business performance in the examined market
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