scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

2021 ◽  
Author(s):  
Yang Hu ◽  
Yiwen Ding ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Industrial Robots ◽  
Knowledge Graph ◽  
Disassembly Process ◽  
Knowledge Based ◽  
Manufacturing Efficiency ◽  
Human Operators ◽  
Traditional Manufacturing ◽  
Human Robot Collaboration

Abstract In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

scholarly journals Examining Performance on a Process-Based Assessment of Word Learning in Relation to Vocabulary Knowledge and Learning in Vocabulary Intervention

2019 ◽  
Vol 40 (05) ◽  
pp. 344-358
Author(s):  
Elizabeth Spencer Kelley ◽  
Howard Goldstein
Keyword(s):  
Reading Comprehension ◽  
Preschool Children ◽  
Young Children ◽  
Word Learning ◽  
Exploratory Study ◽  
Semantic Knowledge ◽  
Learning Opportunities ◽  
Vocabulary Knowledge ◽  
Vocabulary Intervention ◽  
Knowledge Based

AbstractVocabulary knowledge of young children, as a well-established predictor of later reading comprehension, is an important domain for assessment and intervention. Standardized, knowledge-based measures are commonly used by speech-language pathologists (SLPs) to describe existing vocabulary knowledge and to provide comparisons to same-age peers. Process-based assessments of word learning can be helpful to provide information about how children may respond to learning opportunities and to inform treatment decisions. This article presents an exploratory study of the relation among vocabulary knowledge, word learning, and learning in vocabulary intervention in preschool children. The study examines the potential of a process-based assessment of word learning to predict response to vocabulary intervention. Participants completed a static, knowledge-based measure of vocabulary knowledge, a process-based assessment of word learning, and between 3 and 11 weeks of vocabulary intervention. Vocabulary knowledge, performance on the process-based assessment of word learning, and learning in vocabulary intervention were strongly related. SLPs might make use of the information provided by a process-based assessment of word learning to determine the appropriate intensity of intervention and to identify areas of phonological and semantic knowledge to target during intervention.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

Knowledge-Based Production Planning Within the Reference Planning Process Supporting Manufacturing Change Management

2016 ◽  
Author(s):  
Benjamin Gernhardt ◽  
Tobias Vogel ◽  
Mohammad Givehchi ◽  
Lihui Wang ◽  
Matthias Hemmje
Keyword(s):  
Knowledge Representation ◽  
Change Management ◽  
Production Planning ◽  
Planning Process ◽  
Semantic Knowledge ◽  
Collaborative Manufacturing ◽  
Knowledge Based ◽  
Process Oriented ◽  
Machine Readable ◽  
Component Production

The manufacturing of a product takes place in several partial steps and these mostly in different locations to save tax or to use the best providers. Therefore, in the era of Internet of Things (IoT) and modern Intelligent Production Environments (IPE) are going to be inevitably based on a cloud-based repository and distributed architecture to make data and information accessible everywhere as well as development processes and knowledge available for worldwide cooperation. Semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use can support Collaborative Adaptive Production Process Planning (CAPP) in a flexible, efficient, and effective way. Thus, semantic representations of such CAPP knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based knowledge repositories supporting CAPP scenarios as required for e.g., Small and Medium Enterprises (SMEs). When such contributors work together on a product component production planning, they exchange component production and manufacturing change information between different planning subsystems which require, e.g., a standardized product-feature- and production-machine feature representation. These data exchanges are mostly based on applying the already established Standard for the Exchange of Product model data (STEP) for the computer-interpretable representation and exchange of product manufacturing information. Furthermore, the planning process can be supported by so-called Function Block (FB) based knowledge representation models, serving as a high-level planning-process knowledge-resource template. Web-based and at the same time Cloud-based tool suites, which are based on process-oriented semantic knowledge-representation methodologies, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM) can satisfy the needs of representing such planning processes and their knowledge resources. In this way, WPIM can be used to support the integration and management of distributed CAPP knowledge, as well as its access and re-use in Manufacturing Change Management (MCM) including Assembly-, Logistics and Layout Planning (ALLP). Therefore, also a collaborative planning and optimization for mass production in a machine readable and integrated representation is possible. On the other hand, that knowledge can be shared within a cloud-based semantic knowledge repository. To integrate all these functionalities, this paper introduces a new method, called Knowledge-based Production Planning (KPP) and outlines the advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM). In this way, an enabling basis for achieving ALLP interoperability in Distributed Collaborative Manufacturing and Logistics will be demonstrated.

scholarly journals Knowledge Forgetting in Answer Set Programming

2014 ◽  
Vol 50 ◽  
pp. 31-70 ◽  
Author(s):  
Y. Wang ◽  
Y. Zhang ◽  
Y. Zhou ◽  
M. Zhang
Keyword(s):  
Logic Program ◽  
Answer Set Programming ◽  
Irrelevant Information ◽  
Semantic Knowledge ◽  
Decision Problems ◽  
Stable Model ◽  
Logic Programs ◽  
Knowledge Based ◽  
Substitution Principle ◽  
Answer Set

The ability of discarding or hiding irrelevant information has been recognized as an important feature for knowledge based systems, including answer set programming. The notion of strong equivalence in answer set programming plays an important role for different problems as it gives rise to a substitution principle and amounts to knowledge equivalence of logic programs. In this paper, we uniformly propose a semantic knowledge forgetting, called HT- and FLP-forgetting, for logic programs under stable model and FLP-stable model semantics, respectively. Our proposed knowledge forgetting discards exactly the knowledge of a logic program which is relevant to forgotten variables. Thus it preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same variables. We show that this semantic forgetting result is always expressible; and we prove a representation theorem stating that the HT- and FLP-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the HT- and FLP-model semantics, respectively. We also reveal underlying connections between the proposed forgetting and the forgetting of propositional logic, and provide complexity results for decision problems in relation to the forgetting. An application of the proposed forgetting is also considered in a conflict solving scenario.

scholarly journals Ontology-Driven Knowledge-Based Health-Care System, An Emerging Area – Challenges And Opportunities – Indian Scenario

2014 ◽  
Vol XL-8 ◽  
pp. 239-246 ◽  
Author(s):  
A. Sunitha ◽  
G. Suresh Babu
Keyword(s):  
Decision Making ◽  
Semantic Knowledge ◽  
Public Healthcare ◽  
Policy Makers ◽  
Knowledge Based ◽  
Healthcare Decision Making ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Decision Making System ◽  
Semantic Knowledge Base

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration

2021 ◽  
Author(s):  
Tadele Belay Tuli ◽  
Linus Kohl ◽  
Sisay Adugna Chala ◽  
Martin Manns ◽  
Fazel Ansari
Keyword(s):  
Digital Twin ◽  
Knowledge Based ◽  
Human Robot Collaboration

A Semantic Representation for Process-Oriented Knowledge Management Based on Functionblock Domain Models Supporting Distributed and Collaborative Production Planning

2015 ◽  
Author(s):  
Benjamin Gernhardt ◽  
Franz Miltner ◽  
Tobias Vogel ◽  
Holger Brocks ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Production Planning ◽  
Semantic Knowledge ◽  
Semantic Integration ◽  
Product Knowledge ◽  
Simulation And Optimization ◽  
Knowledge Based ◽  
Process Oriented ◽  
Machine Readable

Semantic knowledge representation, management, sharing, access, and re-use approaches can support collaborative production planning in a flexible and efficient as well as an effective way. Therefore, semantic-technology based representations of Collaborative Production Process Planning (CAPP) knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based semantic-enabled knowledge repositories supporting CAPP scenarios as required in the CAPP4SMES project [1]. Beyond that, Small and Medium Enterprises (SMEs) as represented in CAPP4SMES request for a standardized CAPP-oriented product-knowledge- and production-feature representation that can be achieved by applying function-block based knowledge representation models. Semantic Web- and at the same time Cloud-based technologies, tool suites and application solutions which are based on process-oriented semantic knowledge representation methodologies such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozesess-orientiertes Innovationsmanagement, WPIM) [2] can satisfy these needs, supporting the semantic integration, management, access and re-use in a machine readable and integrated representation of distributed CAPP knowledge that is shared within a cloud-based centralized semantic-enabled knowledge repository. Furthermore semantic knowledge representation and querying add value to knowledge-based and computer-aided re-use of such knowledge within CAPP activities and, finally, pave the way towards further automating planning, simulation and optimization support in a semantic web for CAPP.

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