6,333 research outputs found

    Efficient instance and hypothesis space revision in Meta-Interpretive Learning

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    Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

    Hunting Wildlife in the Tropics and Subtropics

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    The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core

    Coding Christianity: Negotiating Religious Dialogue in Online Participatory Spaces

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    This dissertation examines rhetorical conditions and internet-mediated communication strategies that open and close dialogue between individuals with diverse and conflicting worldviews. The author illustrates this tension through sacred-secular interactions in college composition classrooms and online environments, positing that navigating conflict between these discourses—namely those espoused by religiously committed students and public university instructors—often requires stepping outside of adversarial communication frameworks. This project makes a case for models of civic engagement that use more deliberative rhetorical approaches prioritizing empathy over defensiveness and understanding before persuasion. To develop these non-adversarial communication approaches for the composition classroom, the author looks to participatory media for insights and studies the negotiation strategies of Christian and atheist YouTube users who leverage the affordances of the video medium, internet logics, and invitational rhetorical strategies to engage ideological differences in their respective online communities. Through mixed methods research involving in-depth interviews with five YouTube vloggers, netnographic study of over 3,000 videos, and statistical analysis of 76,000+ user comments, Coding Christianity finds that perspective-taking in conflict-ridden environments can happen between netizens when content creators opt out of “flame wars” and, instead, explicitly model critical openness and charitable listening to perceived “others.” The author ultimately suggests that sacred-secular tension in both academic and digital environments be used, not diffused, to negotiate conflicting values and engage in rigorous, civil dialogues

    International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

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    The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Testing in the Professions

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    Testing in the Professions focuses on current practices in credentialing testing as a guide for practitioners. With a broad focus on the key components, issues, and concerns surrounding the test development and validation process, this book brings together a wide range of research and theory—from design and analysis of tests to security, scoring, and reporting. Written by leading experts in the field of measurement and assessment, each chapter includes authentic examples as to how various practices are implemented or current issues observed in credentialing programs. The volume begins with an exploration of the various types of credentialing programs as well as key differences in the interpretation and evaluation of test scores. The next set of chapters discusses key test development steps, including test design, content development, analysis, and evaluation. The final set of chapters addresses specific topics that span the testing process, including communication with stakeholders, security, program evaluation, and legal principles. As a response to the growing number of professions and professional designations that are tied to testing requirements, Testing in the Professions is a comprehensive source for up-to-date measurement and credentialing practices

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    Economic Sanctions in International Law and Practice

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    Providing perspectives from a range of experts, including international lawyers, political scientists, and practitioners, this book assesses current theory and practice of economic sanctions, discussing current legal and political challenges faced by the international community. It examines both the implementation of sanctions by major powers – the United States, the European Union, and Japan – as well as assessing the impact of those sanctions through case studies of Russia, Iran, Syria, and North Korea. Balancing theoretical analysis of legal considerations with national and regional level empirical analysis, it also includes coverage of sanctions issues by the UN Security Council and the EU, as well as the extraterritorial application of sanctions. A valuable reference for academics and practitioners, Economic Sanctions in International Law and Practice will be useful to those working in the fields of international law, diplomacy, and international political economy

    Great expectations: unsupervised inference of suspense, surprise and salience in storytelling

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    Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. Likewise, certain events are key to the plot and more important than others. Importance is referred to as salience. Inferring suspense, surprise and salience are highly challenging for computational systems. It is difficult because all these elements require a strong comprehension of the characters and their motivations, places, changes over time, and the cause/effect of complex interactions. Recently advances in machine learning (often called deep learning) have substantially improved in many language-related tasks, including story comprehension and story writing. Most of these systems rely on supervision; that is, huge numbers of people need to tag large quantities of data to tell the system what to teach these systems. An example would be tagging which events are suspenseful. It is highly inflexible and costly. Instead, the thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into the deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating new stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow up work, the insights could help improve computer models for tasks such as automatic story writing, assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach in many areas of interest for applying machine learning
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