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Academic literature on the topic '170203 Knowledge Representation and Machine Learning'

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Published: 10 December 2022
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Journal articles on the topic "170203 Knowledge Representation and Machine Learning"

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Twine, S. "Knowledge representation and organization in machine learning." Information and Software Technology 32, no. 7 (September 1990): 510–11. http://dx.doi.org/10.1016/0950-5849(90)90171-m.

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Maher, Mary Lou, and Heng Li. "Learning design concepts using machine learning techniques." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 95–111. http://dx.doi.org/10.1017/s0890060400000706.

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AbstractThe use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.
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Ma, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (September 30, 2021): 1–28. http://dx.doi.org/10.1145/3467982.

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Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.
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Maher, Mary Lou, David C. Brown, and Alex Duffy. "Special Issue: Machine Learning in Design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 81–82. http://dx.doi.org/10.1017/s0890060400000688.

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The linking of research in machine learning with research in knowledge-based design is such that each of the two areas benefit from the consideration of the other. The use of machine learning in design addresses the perceived need to support the capture and representation of design knowledge, because handcrafting a representation is a difficult and time-consuming task. In addition, design provides a task with which to investigate the usefulness of existing machine learning techniques, and, perhaps, to discover new ones.
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Littman, David, and Maarten van Someren. "International Workshop on Knowledge Representation and Organization in Machine Learning." AI Communications 1, no. 1 (1988): 44–45. http://dx.doi.org/10.3233/aic-1988-1108.

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Robinson, Peter N., and Melissa A. Haendel. "Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions." Yearbook of Medical Informatics 29, no. 01 (August 2020): 159–62. http://dx.doi.org/10.1055/s-0040-1701991.

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Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.
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Moreno, Marcio, Vítor Lourenço, Sandro Rama Fiorini, Polyana Costa, Rafael Brandão, Daniel Civitarese, and Renato Cerqueira. "Managing Machine Learning Workflow Components." International Journal of Semantic Computing 14, no. 02 (June 2020): 295–309. http://dx.doi.org/10.1142/s1793351x20400115.

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Machine Learning Workflows (MLWfs) have become an essential and disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complex, time-consuming, and error-prone. To handle this problem, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. We introduce our approach to structure MLWfs’ components and metadata in order to aid component retrieval and reuse of new MLWfs. We also consider the execution of these components within a tool. A hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM’s aspects. To validate our approach, we show a practical use case in the Oil & Gas industry. In addition, to evaluate the feasibility of the proposed technique, we create a dataset of MLWfs executions and discuss the MLWfM’s performance in loading and querying this dataset.
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Ullah, AMM Sharif. "Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering." Education Sciences 9, no. 3 (August 29, 2019): 228. http://dx.doi.org/10.3390/educsci9030228.

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This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.
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GRANER, NICOLAS, SUNIL SHARMA, D. SLEEMAN, MICHALIS RISSAKIS, SUSAN CRAW, and CHRIS MOORE. "THE MACHINE LEARNING TOOLBOX CONSULTANT." International Journal on Artificial Intelligence Tools 02, no. 03 (September 1993): 307–28. http://dx.doi.org/10.1142/s0218213093000163.

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The Machine Learning Toolbox contains a set of ten Machine Learning algorithms, integrated with a common interface and common knowledge representation language. An essential component of the Toolbox is the Consultant, a knowledge-based system that advises novice users about which algorithm they could use for a particular application. We show how the Consultant’s architecture evolved, through its successive implementations, from a rigid rule-based expert system to a flexible information browsing system supporting user experimentation. In particular, we show how a task description can be elicited from the user in three different modes and exploited by several functions to provide advice and explanations at various levels of detail. The system’s output also increased in sophistication: initially limited to the recommendation of a suitable algorithm, it now includes detailed information about the algorithm and its usage, and will be extended to help the user interpret and improve the results of learning.
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Kocabas, S. "A review of learning." Knowledge Engineering Review 6, no. 3 (September 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.

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AbstractLearning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering are described as knowledge level learning methods. Learning in classifiers, genetic algorithms and classifier systems are described as symbol level learning, and neural networks are described as device level systems. In accordance with this classification, methods of learning are described in terms of inputs, learning algorithms or devices, and outputs. Then there follows a discussion on the relationships between knowledge representation and learning, and a discussion on the limits of learning in knowledge systems. The paper concludes with a summary of the results drawn from this review.
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Dissertations / Theses on the topic "170203 Knowledge Representation and Machine Learning"

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Leitner, Jürgen. "From vision to actions: Towards adaptive and autonomous humanoid robots." Thesis, Università della Svizzera Italiana, 2014. https://eprints.qut.edu.au/90178/2/2014INFO020.pdf.

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Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.
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Alirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

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The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name. In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase. Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities. The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set. The software that has been implemented and used in this project has been implemented in C.
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Tuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.

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Abstract Knowledge discovery (KD) technology is used to extract knowledge from large quantities of digital data in an automated fashion. The established process model represents the KD process in a linear and technology-centered manner, as a sequence of transformations that refine raw data into more and more abstract and distilled representations. Any actual KD process, however, has aspects that are not adequately covered by this model. In particular, some of the most important actors in the process are not technological but human, and the operations associated with these actors are interactive rather than sequential in nature. This thesis proposes an augmentation of the established model that addresses this neglected dimension of the KD process. The proposed process model is composed of three sub-models: a data model, a workflow model, and an architectural model. Each sub-model views the KD process from a different angle: the data model examines the process from the perspective of different states of data and transformations that convert data from one state to another, the workflow model describes the actors of the process and the interactions between them, and the architectural model guides the design of software for the execution of the process. For each of the sub-models, the thesis first defines a set of requirements, then presents the solution designed to satisfy the requirements, and finally, re-examines the requirements to show how they are accounted for by the solution. The principal contribution of the thesis is a broader perspective on the KD process than what is currently the mainstream view. The augmented KD process model proposed by the thesis makes use of the established model, but expands it by gathering data management and knowledge representation, KD workflow and software architecture under a single unified model. Furthermore, the proposed model considers issues that are usually either overlooked or treated as separate from the KD process, such as the philosophical aspect of KD. The thesis also discusses a number of technical solutions to individual sub-problems of the KD process, including two software frameworks and four case-study applications that serve as concrete implementations and illustrations of several key features of the proposed process model
Tiivistelmä Tiedonlouhintateknologialla etsitään automoidusti tietoa suurista määristä digitaalista dataa. Vakiintunut prosessimalli kuvaa tiedonlouhintaprosessia lineaarisesti ja teknologiakeskeisesti sarjana muunnoksia, jotka jalostavat raakadataa yhä abstraktimpiin ja tiivistetympiin esitysmuotoihin. Todellisissa tiedonlouhintaprosesseissa on kuitenkin aina osa-alueita, joita tällainen malli ei kata riittävän hyvin. Erityisesti on huomattava, että eräät prosessin tärkeimmistä toimijoista ovat ihmisiä, eivät teknologiaa, ja että heidän toimintansa prosessissa on luonteeltaan vuorovaikutteista eikä sarjallista. Tässä väitöskirjassa ehdotetaan vakiintuneen mallin täydentämistä siten, että tämä tiedonlouhintaprosessin laiminlyöty ulottuvuus otetaan huomioon. Ehdotettu prosessimalli koostuu kolmesta osamallista, jotka ovat tietomalli, työnkulkumalli ja arkkitehtuurimalli. Kukin osamalli tarkastelee tiedonlouhintaprosessia eri näkökulmasta: tietomallin näkökulma käsittää tiedon eri olomuodot sekä muunnokset olomuotojen välillä, työnkulkumalli kuvaa prosessin toimijat sekä niiden väliset vuorovaikutukset, ja arkkitehtuurimalli ohjaa prosessin suorittamista tukevien ohjelmistojen suunnittelua. Väitöskirjassa määritellään aluksi kullekin osamallille joukko vaatimuksia, minkä jälkeen esitetään vaatimusten täyttämiseksi suunniteltu ratkaisu. Lopuksi palataan tarkastelemaan vaatimuksia ja osoitetaan, kuinka ne on otettu ratkaisussa huomioon. Väitöskirjan pääasiallinen kontribuutio on se, että se avaa tiedonlouhintaprosessiin valtavirran käsityksiä laajemman tarkastelukulman. Väitöskirjan sisältämä täydennetty prosessimalli hyödyntää vakiintunutta mallia, mutta laajentaa sitä kokoamalla tiedonhallinnan ja tietämyksen esittämisen, tiedon louhinnan työnkulun sekä ohjelmistoarkkitehtuurin osatekijöiksi yhdistettyyn malliin. Lisäksi malli kattaa aiheita, joita tavallisesti ei oteta huomioon tai joiden ei katsota kuuluvan osaksi tiedonlouhintaprosessia; tällaisia ovat esimerkiksi tiedon louhintaan liittyvät filosofiset kysymykset. Väitöskirjassa käsitellään myös kahta ohjelmistokehystä ja neljää tapaustutkimuksena esiteltävää sovellusta, jotka edustavat teknisiä ratkaisuja eräisiin yksittäisiin tiedonlouhintaprosessin osaongelmiin. Kehykset ja sovellukset toteuttavat ja havainnollistavat useita ehdotetun prosessimallin merkittävimpiä ominaisuuksia
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Duminy, Willem H. "A learning framework for zero-knowledge game playing agents." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-10172007-153836.

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Oramas, Martín Sergio. "Knowledge extraction and representation learning for music recommendation and classification." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457709.

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In this thesis, we address the problems of classifying and recommending music present in large collections. We focus on the semantic enrichment of descriptions associated to musical items (e.g., artists biographies, album reviews, metadata), and the exploitation of multimodal data (e.g., text, audio, images). To this end, we first focus on the problem of linking music-related texts with online knowledge repositories and on the automated construction of music knowledge bases. Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation. Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification, combining audio, text, and images. We show how the semantic enrichment of texts and the combination of learned data representations improve the performance on both tasks.
En esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados.
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Choi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.

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Recent progress on deep neural networks has shown remarkable action recognition performance from videos. The remarkable performance is often achieved by transfer learning: training a model on a large-scale labeled dataset (source) and then fine-tuning the model on the small-scale labeled datasets (targets). However, existing action recognition models do not always generalize well on new tasks or datasets because of the following two reasons. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor generalization performance. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small- scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. For the first problem, I propose to learn scene-invariant action representations to mitigate the scene bias in action recognition models. Specifically, I augment the standard cross-entropy loss for action classification with 1) an adversarial loss for the scene types and 2) a human mask confusion loss for videos where the human actors are invisible. These two losses encourage learning representations unsuitable for predicting 1) the correct scene types and 2) the correct action types when there is no evidence. I validate the efficacy of the proposed method by transfer learning experiments. I trans- fer the pre-trained model to three different tasks, including action classification, temporal action localization, and spatio-temporal action detection. The results show consistent improvement over the baselines for every task and dataset. I formulate human action recognition as an unsupervised domain adaptation (UDA) problem to handle the second problem. In the UDA setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already exist- ing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene, to learn domain-invariant action representations. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Then I explore the semi-supervised video action recognition, where we have a lot of labeled videos as source data and sparsely labeled videos as target data. The semi-supervised setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject photometric, geometric, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
Doctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
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Sudre, Gustavo. "Characterizing the Spatiotemporal Neural Representation of Concrete Nouns Across Paradigms." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/315.

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Most of the work investigating the representation of concrete nouns in the brain has focused on the locations that code the information. We present a model to study the contributions of perceptual and semantic features to the neural code representing concepts over time and space. The model is evaluated using magnetoencephalography data from different paradigms and not only corroborates previous findings regarding a distributed code, but provides further details about how the encoding of different subcomponents varies in the space-time spectrum. The model also successfully generalizes to novel concepts that it has never seen during training, which argues for the combination of specific properties in forming the meaning of concrete nouns in the brain. The results across paradigms are in agreement when the main differences among the experiments (namely, the number of repetitions of the stimulus, the task the subjects performed, and the type of stimulus provided) were taken into consideration. More specifically, these results suggest that features specific to the physical properties of the stimuli, such as word length and right-diagonalness, are encoded in posterior regions of the brain in the first hundreds of milliseconds after stimulus onset. Then, properties inherent to the nouns, such as is it alive? and can you pick it up?, are represented in the signal starting at about 250 ms, focusing on more anterior parts of the cortex. The code for these different features was found to be distributed over time and space, and it was common for several regions to simultaneously code for a particular property. Moreover, most anterior regions were found to code for multiple features, and a complex temporal profile could be observed for the majority of properties. For example, some features inherent to the nouns were encoded earlier than others, and the extent of time in which these properties could be decoded varied greatly among them. These findings complement much of the work previously described in the literature, and offer new insights about the temporal aspects of the neural encoding of concrete nouns. This model provides a spatiotemporal signature of the representation of objects in the brain. Paired with data from carefully-designed paradigms, the model is an important tool with which to analyze the commonalities of the neural code across stimulus modalities and tasks performed by the subjects.
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Duminy, Willem Harklaas. "A learning framework for zero-knowledge game playing agents." Diss., University of Pretoria, 2007. http://hdl.handle.net/2263/28767.

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The subjects of perfect information games, machine learning and computational intelligence combine in an experiment that investigates a method to build the skill of a game-playing agent from zero game knowledge. The skill of a playing agent is determined by two aspects, the first is the quantity and quality of the knowledge it uses and the second aspect is its search capacity. This thesis introduces a novel representation language that combines symbols and numeric elements to capture game knowledge. Insofar search is concerned; an extension to an existing knowledge-based search method is developed. Empirical tests show an improvement over alpha-beta, especially in learning conditions where the knowledge may be weak. Current machine learning techniques as applied to game agents is reviewed. From these techniques a learning framework is established. The data-mining algorithm, ID3, and the computational intelligence technique, Particle Swarm Optimisation (PSO), form the key learning components of this framework. The classification trees produced by ID3 are subjected to new post-pruning processes specifically defined for the mentioned representation language. Different combinations of these pruning processes are tested and a dominant combination is chosen for use in the learning framework. As an extension to PSO, tournaments are introduced as a relative fitness function. A variety of alternative tournament methods are described and some experiments are conducted to evaluate these. The final design decisions are incorporated into the learning frame-work configuration, and learning experiments are conducted on Checkers and some variations of Checkers. These experiments show that learning has occurred, but also highlights the need for further development and experimentation. Some ideas in this regard conclude the thesis.
Dissertation (MSc)--University of Pretoria, 2007.
Computer Science
MSc
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Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.

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In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reasoning processes. The core issue my dissertation addresses is: what kind of metaknowledge (knowledge about knowledge) may enable the agent to diagnose faults in its domain knowledge? In providing a solution to this issue, the central contribution of this research is a theory of the kind of metaknowledge that enables a system to reason about and adapt its conceptual knowledge. For this purpose, I propose a representation that explicitly encodes metaknowledge in the form of procedures called Empirical Verification Procedures (EVPs). In the proposed knowledge representation, an EVP is associated with each concept within the agent's domain knowledge. Each EVP explicitly semantically grounds the associated concept in the agent's perception, and can thus be used as a test to determine the validity of knowledge of that concept during diagnosis. I present the formal and empirical evaluation of a system, Augur, that makes use of EVP metaknowledge to adapt its own domain knowledge in the context of a particular subclass of classification problem that I call compositional classification, in which the overall classification task can be broken into a hierarchically organized set of subtasks. I hypothesize that EVP metaknowledge will enable a system to automatically adapt its knowledge in two ways: first, by adjusting the ways that inputs are categorized by a concept, in accordance with semantics fixed by an associated EVP; and second, by adjusting the semantics of concepts themselves when they fail to contribute appropriately to system goals. The latter adaptation is realized by altering the EVP associated with the concept in question. I further hypothesize that the semantic grounding of domain concepts in perception through the use of EVPs will increase the generalization power of a learner that operates over those concepts, and thus make learning more efficient. Beyond the support of these hypotheses, I also present results pertinent to the understanding of learning in compositional classification settings using structured knowledge representations.
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Bulgarov, Florin Adrian. "Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404562/.

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Recent advancements in machine learning have started to put their mark on educational technology. Technology is evolving fast and, as people adopt it, schools and universities must also keep up (nearly 70% of primary and secondary schools in the UK are now using tablets for various purposes). As these numbers are likely going to follow the same increasing trend, it is imperative for schools to adapt and benefit from the advantages offered by technology: real-time processing of data, availability of different resources through connectivity, efficiency, and many others. To this end, this work contributes to the growth of educational technology by developing several algorithms and models that are meant to ease several tasks for the instructors, engage students in deep discussions and ultimately, increase their learning gains. First, a novel, fine-grained knowledge representation is introduced that splits phrases into their constituent propositions that are both meaningful and minimal. An automated extraction algorithm of the propositions is also introduced. Compared with other fine-grained representations, the extraction model does not require any human labor after it is trained, while the results show considerable improvement over two meaningful baselines. Second, a proposition alignment model is created that relies on even finer-grained units of text while also outperforming several alternative systems. Third, a detailed machine learning based analysis of students' unrestricted natural language responses to questions asked in classrooms is made by leveraging the proposition extraction algorithm to make computational predictions of textual assessment. Two computational approaches are introduced that use and compare manually engineered machine learning features with word embeddings input into a two-hidden layers neural network. Both methods achieve notable improvements over two alternative approaches, a recent short answer grading system and DiSAN – a recent, pre-trained, light-weight neural network that obtained state-of-the-art performance on multiple NLP tasks and corpora. Fourth, a clustering algorithm is introduced in order to bring structure to the feedback offered to instructors in classrooms. The algorithm organizes student responses based on three important aspects: propositional importance classifications, computational textual understanding of student understanding and algorithm similarity metrics between student responses. Moreover, a dynamic cluster selection algorithm is designed to decide which are the best groups of responses resulting from the cluster hierarchy. The algorithm achieves a performance that is 86.3% of the performance achieved by humans on the same task and dataset. Fifth, a deep neural network is built to predict, for each cluster, an engagement response that is meant to help generate insightful classroom discussion. This is the first ever computational model to predict how engaging student responses will be in classroom discussion. Its performance reaches 86.8% of the performance obtained by humans on the same task and dataset. Moreover, I also demonstrate the effectiveness of a dynamic algorithm that can self-improve with minimal help from the teachers, in order to reduce its relative error by up to 32%.
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Books on the topic "170203 Knowledge Representation and Machine Learning"

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Morik, Katharina, ed. Knowledge Representation and Organization in Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0017213.

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Katharina, Morik, ed. Knowledge representation and organization in machine learning. Berlin: Springer-Verlag, 1989.

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Kumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898.

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Machine learning of robot assembly plans. Boston: Kluwer Academic Publishers, 1988.

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Emde, Werner. Modellbildung, Wissensrevision und Wissensrepräsentation im Maschinellen Lernen. Berlin: Springer, 1991.

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Pacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96: Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Berlin: Springer, 1996.

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1953-, Benjamin D. Paul, ed. Change of representation and inductive bias. Boston: Kluwer Academic, 1990.

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Motta, E. Reusable components for knowledge modelling: Case studies in parametric design problem solving. Amsterdam: IOS Press, 2000.

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G, Antoniou, Ghose Aditya K, Truszczyński Mirosław, Workshop on Inducing Complex Representations (1996 : Cairns, Qld.), and Pacific Rim International Conference on Artificial Intelligence (4th : 1996 : Cairns, Qld.), eds. Learning and reasoning with complex representations: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations, Cairns, Australia, August 26-30, 1996 : selected papers. Berlin: Springer, 1998.

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International Conference on Knowledge Modeling & Expertise Transfer (1st 1991 Sophia-Antipolis, France). Knowledge modeling & expertise transfer: Proceedings of the first International Conference on Knowledge Modeling & Expertise Transfer, Sophia-Antipolis, French Riviera, France, April 22-24, 1991. Amsterdam: IOS Press, 1991.

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Book chapters on the topic "170203 Knowledge Representation and Machine Learning"

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Neri, Filippo, and Lorenza Saitta. "Knowledge representation in machine learning." In Machine Learning: ECML-94, 20–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_48.

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Sowmyayani, S. "Machine Learning." In Prediction and Analysis for Knowledge Representation and Machine Learning, 1–31. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898-1.

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Muthu Lakshmi, G., and N. Krishnammal. "Multi-View Representation Learning." In Prediction and Analysis for Knowledge Representation and Machine Learning, 175–98. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898-9.

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Ciucci, Davide, Stefania Boffa, and Andrea Campagner. "Orthopartitions in Knowledge Representation and Machine Learning." In Rough Sets, 3–18. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21244-4_1.

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Khosla, Megha, Jurek Leonhardt, Wolfgang Nejdl, and Avishek Anand. "Node Representation Learning for Directed Graphs." In Machine Learning and Knowledge Discovery in Databases, 395–411. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46150-8_24.

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Luo, Dijun, Feiping Nie, Chris Ding, and Heng Huang. "Multi-Subspace Representation and Discovery." In Machine Learning and Knowledge Discovery in Databases, 405–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23783-6_26.

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Papreja, Piyush, Hemanth Venkateswara, and Sethuraman Panchanathan. "Representation, Exploration and Recommendation of Playlists." In Machine Learning and Knowledge Discovery in Databases, 543–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_50.

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Luo, Peng, Jinye Peng, Ziyu Guan, and Jianping Fan. "Multi-view Semantic Learning for Data Representation." In Machine Learning and Knowledge Discovery in Databases, 367–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23528-8_23.

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van Someren, Maarten W. "Using attribute dependencies for rule learning." In Knowledge Representation and Organization in Machine Learning, 192–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0017223.

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Li, Xin, and Yuhong Guo. "Bi-directional Representation Learning for Multi-label Classification." In Machine Learning and Knowledge Discovery in Databases, 209–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44851-9_14.

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Conference papers on the topic "170203 Knowledge Representation and Machine Learning"

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López, Beatriz, Natàlia Mordvanyuk, Pablo Gay, and Albert Pla. "Knowledge representation and machine learning on wearable sensor data." In DATA '18: International Conference on Data Science, E-learning and Information Systems 2018. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3279996.3280041.

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Martínez-Rojas, Antonio, Andrés Jiménez-Ramírez, and Jose Enríquez. "Towards a Unified Model Representation of Machine Learning Knowledge." In 4th International Special Session on Advanced practices in Model-Driven Web Engineering. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008559204700476.

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Martínez-Rojas, Antonio, Andrés Jiménez-Ramírez, and Jose Enríquez. "Towards a Unified Model Representation of Machine Learning Knowledge." In 4th International Special Session on Advanced practices in Model-Driven Web Engineering. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008559200002366.

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Chen, Wenrui, Chuyao Luo, Shaokai Wang, and Yunming Ye. "Representation learning with complete semantic description of knowledge graphs." In 2017 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2017. http://dx.doi.org/10.1109/icmlc.2017.8107756.

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Ming-Hu Ha, Yan Li, Hai-Jun Li, and Peng Wang. "A new form of knowledge representation and reasoning." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527378.

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Khummongkol, Rojanee, and Masao Yokota. "Systematic representation and computation of human intuitive spatiotemporal knowledge as mental imagery." In 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2016. http://dx.doi.org/10.1109/icmlc.2016.7873002.

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Sabbatini, Federico, and Roberta Calegari. "Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/57.

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Procedures aimed at explaining outcomes and behaviour of opaque predictors are becoming more and more essential as machine learning (ML) black-box (BB) models pervade a wide variety of fields and, in particular, critical ones - e.g., medical or financial -, where it is not possible to make decisions on the basis of a blind automatic prediction. A growing number of methods designed to overcome this BB limitation is present in the literature, however some ML tasks are nearly or completely neglected-e.g., regression and clustering. Furthermore, existing techniques may be not applicable in complex real-world scenarios or they can affect the output predictions with undesired artefacts. In this paper we present the design and the implementation of GridREx, a pedagogical algorithm to extract knowledge from black-box regressors, along with PEDRO, an optimisation procedure to automate the GridREx hyper-parameter tuning phase with better results than manual tuning. We also report the results of our experiments involving the application of GridREx and PEDRO in real case scenarios, including GridREx performance assessment by using as benchmarks other similar state-of-the-art techniques. GridREx proved to be able to give more concise explanations with higher fidelity and predictive capabilities.
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Abd, Maan Tareq, Masnizah Mohd, and Mustafa Tareq Abd. "Investigation of Data Representation Methods with Machine Learning Algorithms for Biomedical Named Enttity Recognition." In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2018. http://dx.doi.org/10.1109/infrkm.2018.8464816.

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Heng Chung, Matthew Wai, Jianyu Liu, and Hegler Tissot. "Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00237.

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Land, Jr., Walker H., Mark J. Embrechts, Frances R. Anderson, Tom Smith, Lut Wong, Steve Fahlbusch, and Robert Choma. "Integrating knowledge representation/engineering, the multivariant PNN, and machine learning to improve breast cancer diagnosis." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2005. http://dx.doi.org/10.1117/12.604575.

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