Knowledge Representations in Technical Systems -- A Taxonomy

• URL: http://arxiv.org/abs/2001.04835v2
• Date: Wed, 15 Jan 2020 07:07:10 GMT
• Title: Knowledge Representations in Technical Systems -- A Taxonomy
• Authors: Kristina Scharei, Florian Heidecker, Maarten Bieshaar
• Abstract summary: An accurate representation of knowledge is essential for the system to work as expected. This article mainly gives insight into different knowledge representation techniques and their categorization into various problem domains in artificial intelligence.
• Score: 4.807347156077899
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent usage of technical systems in human-centric environments leads to the question, how to teach technical systems, e.g., robots, to understand, learn, and perform tasks desired by the human. Therefore, an accurate representation of knowledge is essential for the system to work as expected. This article mainly gives insight into different knowledge representation techniques and their categorization into various problem domains in artificial intelligence. Additionally, applications of presented knowledge representations are introduced in everyday robotics tasks. By means of the provided taxonomy, the search for a proper knowledge representation technique regarding a specific problem should be facilitated.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Knowledge-enhanced Neural Machine Reasoning: A Review [67.51157900655207]
  We introduce a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We elucidate the current application domains and provide insight into promising prospects for future research.
  arXiv  Detail & Related papers  (2023-02-04T04:54:30Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Machine learning and deep learning [0.0]
  Machine learning describes the capacity of systems to learn from problem-specific training data. Deep learning is a machine learning concept based on artificial neural networks.
  arXiv  Detail & Related papers  (2021-04-12T09:54:12Z)
• Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence [6.218613353519724]
  Methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Projection is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations.
  arXiv  Detail & Related papers  (2021-03-24T22:33:51Z)
• Continual Learning of Knowledge Graph Embeddings [17.686595033265558]
  In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications. Our work relaxes the static assumptions of these representations to tackle the incremental knowledge graph embedding problem. We provide insights about trade-offs for practitioners to match a semantics-driven robotics application to a suitable continual knowledge graph embedding method.
  arXiv  Detail & Related papers  (2021-01-14T19:59:57Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Foundations of Explainable Knowledge-Enabled Systems [3.7250420821969827]
  We present a historical overview of explainable artificial intelligence systems. We focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. We propose new definitions for explanations and explainable knowledge-enabled systems.
  arXiv  Detail & Related papers  (2020-03-17T04:18:48Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)
• A Survey of Knowledge Representation in Service Robotics [10.220366465518262]
  We focus on knowledge representations and how knowledge is typically gathered, represented, and reproduced to solve problems. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models. We discuss key principles that should be considered when designing an effective knowledge representation.
  arXiv  Detail & Related papers  (2018-07-05T22:18:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.