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learning domain theories using abstract background knowledge 1st

1st European Conference on Machine learning, ECML 1993

作者： Clark, Peter Matwin, Stan Knowledge Systems Laboratory National Research Council Ottawa Canada Ottawa Machine Learning Group Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9783540566021

Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain theories originate. In this paper we address the problem of constructing a domain theory from more general, abstract knowledge which may be available. The basis of our method is to first assume a structure for the target domain theory, and second to view background knowledge as constraints on components of that structure. This enables a focusing of search during learning, and also produces a domain theory which is explainable with respect to the background knowledge. We evaluate an instance of this methodology applied to the domain of economics, where background knowledge is represented as a qualitative model. © Springer-Verlag Berlin Heidelberg 1993.

关键词： learning systems

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Using Qualitative Models to Guide Inductive learning 10

Using Qualitative Models to Guide Inductive Learning

引用

10th International Conference on Machine learning, ICML 1993

作者： Clark, Peter Matwin, Stan National Research Council Knowledge Systems Laboratory M-50 Montreal Road Ottawa Canada Ottawa Machine Learning Group Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)1558603077

This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploiting domain knowledge in the learning process. Such explainabihty is essential both for practical application of inductive technology, and for integrating the results of learning back into an existing knowledge-base. We apply this method to two process control problems, a water tank network and an ore grinding process used in the mining industry. Surprisingly, in addition to achieving explainability the classificational accuracy of the induced rules is also increased. We show how the value of the qualitative models can be quantified in terms of their equivalence to additional training examples, and finally discuss possible extensions. © ICML *** rights reserved

关键词： Domain knowledge

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Patterns for technology enhanced meetings

Patterns for technology enhanced meetings

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12th European Conferenceon Pattern Languages of Programs, EuroPLoP 2007

作者： Schümmer, Till Tandler, Peter Cooperative Systems Group FernUniversität in Hagen Universitätsstraße 1 58084 Hagen Germany E-Learning and Knowledge Management Fraunhofer IGD Fraunhoferstr. 5 64283 Darmstadt Germany

ISBN: (纸本)9783879408191

Computer technology has evolved to a stage where it is available everywhere and every time - even in meetings. The pattern language presented in this paper sheds light on how to make use of technology when preparing and conducting a meeting. We collected advice both on social practices of good meetings as well as on implications for technology support in meetings. These patterns are illustrated by three different application areas, namely meetings in commercial contexts, meetings in the context of learning that are often referred to as group learning, and meetings in the context of committees in a non-governmental organizations.

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Supporting organizational e-learning

Supporting organizational e-learning

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： I. Douglas Knowledge Communities Research Group Learning Systems Institute Florida State University USA

A learning organization is one that continuously monitors key performance metrics and collaboratively identifies interventions that can lead to measurable performance improvement. This paper will report upon the experience of developing software architecture for facilitating the continuous monitoring and improvement of individual and organizational performance. In collaboration with a number of analysts from the US military, software prototypes have been developed that demonstrate different models of performance improvement support. Together the prototypes and the common underlying architecture illustrate a configurable computing approach, and a means of improving performance, problem solving, knowledge reuse and sharing

关键词： Electronic learning Collaborative software Military computing Software prototyping Measurement Collaboration Software architecture Monitoring Performance analysis Software performance

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Capturing and managing decision making rationale

Capturing and managing decision making rationale

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： I. Douglas Knowledge Communities Research Group Learning Systems Institute Florida State University Tallahassee FL USA

Rationale management is a technique for tracking decision making during the design process. Having a process for documenting the rationale for design decisions can help in quality control and in providing useful information to people who may seek to reuse solutions. A number of tools and approaches have been developed in the past to facilitate rationale management. The main approaches to rationale management are described as a prelude to examining the features of an organizational analysis prototype. The unique feature of this prototype is that it embeds rationale management into a more general analysis and solution selection tool. The tool facilitates a number of mechanisms for the automated collection of relevant data.

关键词： Decision making knowledge management Prototypes Voting learning systems Process design Quality control Psychology Software design Inspection

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Towards the adaptive semantic web

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1st International Workshop on Principles and Practice of Semantic Web Reasoning, PPSWR 2003

作者： Dolog, Peter Henze, Nicola Nejdl, Wolfgang Sintek, Michael Learning Lab Lower Saxony University of Hannover Expo Plaza 1 HannoverD-30539 Germany ISI-Knowledge-Based Systems University of Hannover Appelstr. 4 HannoverD-30167 Germany GmbH Knowledge Management Group Postfach 2080 KaiserslauternD-67608 Germany

ISBN: (纸本)3540205829

In this paper we show how personalization techniques from the area of adaptive hypermedia can be achieved in the semantic web. Our approach is based on rule-based reasoning enabled by semantic web technologies. The personalization techniques are formalized as reasoning rules. The rules are able to reason over distributed information resources annotated with semantic web metadata formats. This leads towards the realization of an adaptive semantic web idea which provides personalized, adaptive access to information, services, or other, distributed resources. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Semantic Web

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A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education

arXiv

引用

arXiv 2022年

作者： Wagner, Miriam Helal, Hayyan Roepke, Rene Judel, Sven Doveren, Jens Goerzen, Sergej Soudmand, Pouya Lakemeyer, Gerhard Schroeder, Ulrik van der Aalst, Wil RWTH Aachen University Germany Knowledge-Based Systems Group RWTH Aachen University Germany Learning Technologies Research Group RWTH Aachen University Germany

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers. © 2022, CC BY.

关键词： Students

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Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics

arXiv

引用

arXiv 2022年

作者： Komeno, Naoto Michael, Brendan Küchler, Katharina Anarossi, Edgar Matsubara, Takamitsu Robot Learning Lab Nara Institue of Science and Technology Japan Institute of Knowledge Based Systems Group Rwth Aachen University Germany

Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modelling (e.g., a priori geometric definitions). While machine learning can be used to learn non-linear control models in a datadriven approach, these models often lack an intuitive internal physical interpretation and representation, limiting dynamical analysis. To address this, this paper presents an approach using Koopman operator theory and deep neural networks to provide a global linear description of the non-linear control systems. Specifically, by globally linearising dynamics, the Koopman operator is analyzed using spectral decomposition to characterises important physics-based interpretations, such as functional growths and oscillations. Experiments in this paper demonstrate this approach for controlling non-linear soft robotics, and shows model outputs are interpretable in the context of spectral analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Inferring short-term volatility indicators from the Bitcoin blockchain

arXiv

引用

arXiv 2018年

作者： Antulov-Fantulin, Nino Tolic, Dijana Piskorec, Matija Ce, Zhang Vodenska, Irena Computational Social Science ETH Zurich Switzerland Laboratory for Machine Learning and Knowledge Representations Institute Ruder Bošković Croatia Systems Group ETH Zurich Switzerland Metropolitan College Boston University United States

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume. Copyright © 2018, The Authors. All rights reserved.

关键词： Blockchain

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TagLearner: A P2P Classifier learning System from Collaboratively Tagged Text Documents

TagLearner: A P2P Classifier Learning System from Collaborat...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Haimonti Dutta Xianshu Zhu Tushar Mahule Hillol Kargupta Kirk Borne Codrina Lauth Florian Holz Gerhard Heyer The Center of Computational Learning Systems Columbia University New York USA Department of CSEE University of Maryland Baltimore MD USA George Mason University Fairfax VA USA Fraunhofer Institute of Intelligent Analysis and Information Systems Knowledge Discovery Group Germany Department of CS University of Leipzig Germany

The amount of text data on the Internet is growing at a very fast rate. Online text repositories for news agencies, digital libraries and other organizations currently store giga and tera-bytes of data. Large amounts of unstructured text poses a serious challenge for data mining and knowledge extraction. End user participation coupled with distributed computation can play a crucial role in meeting these challenges. In many applications involving classification of text documents, Web users often participate in the tagging process. This collaborative tagging results in the formation of large scale peer-to-peer (P2P) systems which can function, scale and self-organize in the presence of highly transient population of nodes and do not need a central server for co-ordination. In this paper, we describe TagLearner, a P2P classifier learning system for extracting patterns from text data where the end users can participate both in the task of labeling the data and building a distributed classifier on it. We present a novel distributed linear programming based classification algorithm which is asynchronous in nature. The paper also provides extensive empirical results on text data obtained from an online repository - the NSF Abstracts Data.

关键词： learning systems Collaboration Data mining Tagging Peer to peer computing Internet Software libraries Distributed computing Large-scale systems Labeling

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