1,721,108 research outputs found
Enhanced n-gram extraction using relevance feature discovery
No full textGuaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio
Improving Confidence in the Estimation of Values and Norms
No full textAutonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent's behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive IntelligenceInformation and Communication TechnologyEthics & Philosophy of Technolog
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Opal's Conversation Manager (CM)
No full textMulti-agent systems consist of multiple agents to accomplish tasks or goals that require coordination and collaboration through interactions, such as agent conversation. This thesis provides description of the technologies we have used and discusses the design and implementation of the proposed conversation manager based on Coloured Petri nets to handle FIPA-compliant technologies. The proposed conversation manager is built on the existing multi-agent system Opal. The conversation manager acts as an optional module inside Opal to provide a more friendly development environment and visualisation support to agent designers for handling agent conversation
Teaching Novices Programming: A Programming Process Using Goals & Plans with a Visual Programming Environment
Full text linkIt is easy to get novices to understand individual statements of a computer programming language, but it is hard to teach them how to put these statements together into a valid program. This research focuses on the one of the important matters: how to teach novices to construct code. A key constraint is that it aims to develop a new approach for teaching novice programming which is both easy to introduce and effective in improving novices’ learning.
The approach of this study combines three key ideas: using a visual programming environment (VPE); using strategies, specifically the concept of “goal” and “plan”; and having a well-defined programming process. In this study, a visual notation of programming goals, plans and the data-flow relations has been developed and used to represent “hand solution” of programming design. A data-flow framework has also been developed and applied to support implementation of the programming design. A detailed programming process is provided to guide novices programming by using goals and plans in a VPE in order to combine the relevant programming statements into a valid program. Moreover, the data-flow framework provides immediate feedbacks to motivate and engage novices, not only from the unmerged plans, but also from all the rest of intermediate level phases in the programming process till the final program code.
Based on the cognitive load theory, the integration of the above developments has been built up on a visual goal-plan teaching approach. This approach has been evaluated experimentally in a real teaching setting. The evaluation results indicated that the approach has potential to significantly improve the teaching of novices programming
Opal's Conversation Manager (CM)
No full textMulti-agent systems consist of multiple agents to accomplish tasks or goals that require coordination and collaboration through interactions, such as agent conversation. This thesis provides description of the technologies we have used and discusses the design and implementation of the proposed conversation manager based on Coloured Petri nets to handle FIPA-compliant technologies. The proposed conversation manager is built on the existing multi-agent system Opal. The conversation manager acts as an optional module inside Opal to provide a more friendly development environment and visualisation support to agent designers for handling agent conversation
Ensemble learning through cooperative evolutionary computation
No full textBuilding ensembles of classifiers is an active area of research for machine learning, with the fundamental goal of combining the predictions of multiple classifiers to improve prediction accuracy over an individual classifier. In theory, combining classifiers in an ensemble can improve the prediction results by compensating for individual classifier weaknesses in certain areas by benefiting from better accuracy of the other individuals in the same area. Typical ensemble learning approaches require extensive amounts of computation to train and combine multiple models into a single solution. A key question in ensemble learning is: given the total computational effort roughly equivalent to a single monolithic solution, can an ensemble learner achieve comparable or better performance?
In this thesis, a comparison is made between a single complex monolithic agent and an ensemble of many simpler agents that is evolved using equivalent computational effort. To do this, a framework is constructed that enables the comparison of a monolithic approach using complex agents, and an ensemble approach made up of simple agents. This is then applied to the application of buying and selling stocks on a simulated stock market and comparing the results of the two approaches to classify stock data on when to buy and sell. The framework involves creating a population of agents. These are “decision making agents” (DMAs), which evaluate a data source and decide at each time step whether to trade or hold a stock.
In many learning problems, such as the stock trading example used in this thesis, the suitability of a model is measured at a macroscopic level aggregated over multiple decision actions. These problems are not well-suited to traditional learning methods, so evolutionary computation (EC) is frequently used to build machine learning models in these situations. Historically, most EC approaches use a single population to evolve a single solution. A more recent branch of EC research emphasises the use of cooperative coevolution, where the required solution is decomposed into several sub-components, and multiple populations are used in parallel to simultaneously evolve these. There are strong analogies between the divide and conquer strategies of cooperative co-evolution and the building of ensembles in traditional machine learning.
In this thesis, a cooperative co-evolution approach using genetic programming to evolve individual populations and combined them as an ensemble is used to evolve a solution. The agents in the individual populations are evolved with a standard genetic programming approach, where our DMAs are decision trees made up of logic operators (function primitives) and stock indicators (terminal primitives). DMAs are used for both monolithic and ensemble algorithms, but the size of the DMAs varies and the way they are evaluated is different. With the monolithic approach only a single population is used, but the agents in the population evolve to have increased complexity compared to the agents in the ensemble approach. With the cooperative co-evolution ensemble approach, n populations are created and evolved independently, but they are evaluated together using majority voting. The agents used in the ensemble approach are only allowed 1/n of the nodes that the monolithic agent can have, reducing the ensemble’s total complexity to a similar level to that of the monolithic approach.
With this framework, this thesis suggests that an ensemble of simple agents using variance reduction performs as well, and in most cases better than, a complex monolithic agent. The variance reduction process is like that of bagging, with majority voting within the ensemble damping down the behaviour of over-active, risky models to reduce the error component attributable to these risky actions. This variance reduction behaviour was not by design, but was an emergent property. The robustness of these findings is examined under multiple conditions, which include key parameters pertaining to ensemble learning. These include population size and ensemble size, which are examined in this work to gain insights into an optimal set of parameter values.
To ensure that the insights into ensemble learning generalise beyond that of the examined stock trading problem, an alternative unrelated problem, suitable for a cooperative approach is then tested in a similar way. This is the Tartarus problem, in which agents use finite state machines (FSM) for internal states. Previous work in using cooperative co-evolutionary methods on the Tartarus problem focused on decomposition of a single FSM and met with limited success. The co-evolutionary approach used here builds an ensemble of smaller FSMs, each voting on the best action to take. This configuration reduces the computational effort in the mutation operator, therefore allowing an ensemble with more total states to be used for the same overall computational effort. In this context, this approach improves on previous cooperative research and shows that some findings are transferable between applications when using the ensemble approach shown in this research
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