68T05 Learning and adaptive systems [See also 68Q32, 91E40]
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Assistenzfunktionen stellen einen Meilenstein bei der Automatisierung hydraulischer Baumaschinen dar. Zur Regelung der Position des Tool Center Points von Hydraulikbaggern, den komplexesten Vertretern dieser Klasse, können inverse Modelle als Teil einer Vorsteuerung genutzt werden. In dieser Arbeit werden hierzu physikalische und datenbasierte Modellierungsansätze in Form von hybriden Modellen vereint. Die Zielanwendung stellt eine Assistenzfunktion zur automatisierten Durchführung von Planziehbewegungen dar. Es konnte gezeigt werden, dass durch hybride Modelle eine verbesserte Güte und höhere Robustheit gegenüber Schwingungen im Vergleich zu rein datenbasierten Ansätzen erzielt werden kann. Die untersuchten Ansätze lassen sich auf andere Maschinen übertragen. Die Validierung erfolgte an einem realen Versuchsträger, dem Mobilbagger JCB Hydradig. Zur Systemidentifikation generierte Daten wurden darüber hinaus mittels Methoden des unüberwachten Lernens auf Vollständigkeit untersucht. Hierbei konnte der Effekt der konzeptbedingten Datenlücken festgestellt und untersucht werden, welcher eine Genauigkeitsgrenze bezüglich der inversen Modellierung darstellt.
In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base.
Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving.
This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases.
The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research.
The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.