scholarly journals Search for Answers in Domain-Specific Supported by Intelligent Agents

2018 ◽  
Author(s):  
Fernando Zacarias ◽  
Rosalba Cuapa ◽  
Guillermo De Ita ◽  
Miguel Bracamontes
Keyword(s):  
Intelligent Agents ◽  
Domain Specific

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 28-46
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

scholarly journals Search for Answers in Domain Specific Supported by Intelligent Agents

2018 ◽  
Vol 10 (4) ◽  
pp. 39-47
Author(s):  
Fernando Zacarias ◽  
Rosalba Cuapa ◽  
Guillermo De Ita ◽  
Miguel Bracamontes
Keyword(s):  
Intelligent Agents ◽  
Domain Specific

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 433-451
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

scholarly journals Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 4
Author(s):  
František Babič ◽  
Vladimír Bureš ◽  
Pavel Čech ◽  
Martina Husáková ◽  
Peter Mikulecký ◽  
...  
Keyword(s):  
Intelligent Agents ◽  
Ontology Learning ◽  
Journal Articles ◽  
Scientific Databases ◽  
Domain Specific ◽  
Layer Cake ◽  
Research Activities ◽  
Speed Up ◽  
Express Information ◽  
Research Domain

Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.

Scientific Problem Solving in a Virtual Laboratory: A Comparison Between Individuals and Pairs

2008 ◽  
Vol 67 (2) ◽  
pp. 71-83 ◽  
Author(s):  
Yolanda A. Métrailler ◽  
Ester Reijnen ◽  
Cornelia Kneser ◽  
Klaus Opwis
Keyword(s):  
Visual Search ◽  
Problem Solving ◽  
Process Data ◽  
Scientific Problem ◽  
Domain Specific ◽  
Scientific Problem Solving ◽  
Before And After ◽  
Verbal Data ◽  
Psychological Knowledge ◽  
Problem Solving Task

This study compared individuals with pairs in a scientific problem-solving task. Participants interacted with a virtual psychological laboratory called Virtue to reason about a visual search theory. To this end, they created hypotheses, designed experiments, and analyzed and interpreted the results of their experiments in order to discover which of five possible factors affected the visual search process. Before and after their interaction with Virtue, participants took a test measuring theoretical and methodological knowledge. In addition, process data reflecting participants’ experimental activities and verbal data were collected. The results showed a significant but equal increase in knowledge for both groups. We found differences between individuals and pairs in the evaluation of hypotheses in the process data, and in descriptive and explanatory statements in the verbal data. Interacting with Virtue helped all students improve their domain-specific and domain-general psychological knowledge.

Emotions and health: The impact of emotions, emotions regulation, music, and acculturation on health

2008 ◽  
Vol 16 (3) ◽  
pp. 112-115 ◽  
Author(s):  
Stephan Bongard ◽  
Volker Hodapp ◽  
Sonja Rohrmann
Keyword(s):  
Stress Responses ◽  
Physiological Stress ◽  
Anger Expression ◽  
Domain Specific ◽  
Cardiovascular Stress ◽  
Expression Behavior ◽  
Cardio Vascular ◽  
And Coping ◽  
Relationship Of ◽  
The Impact

Abstract. Our unit investigates the relationship of emotional processes (experience, expression, and coping), their physiological correlates and possible health outcomes. We study domain specific anger expression behavior and associated cardio-vascular loads and found e.g. that particularly an open anger expression at work is associated with greater blood pressure. Furthermore, we demonstrated that women may be predisposed for the development of certain mental disorders because of their higher disgust sensitivity. We also pointed out that the suppression of negative emotions leads to increased physiological stress responses which results in a higher risk for cardiovascular diseases. We could show that relaxation as well as music activity like singing in a choir causes increases in the local immune parameter immunoglobuline A. Finally, we are investigating connections between migrants’ strategy of acculturation and health and found e.g. elevated cardiovascular stress responses in migrants when they where highly adapted to the German culture.

Measurement Invariance of Divergent Thinking Across Gender, Age, and School Forms

2009 ◽  
Vol 25 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Heinz Holling
Keyword(s):  
Measurement Invariance ◽  
Divergent Thinking ◽  
Factor Model ◽  
Age Groups ◽  
Thinking Skills ◽  
Specific Model ◽  
Confirmatory Factor Analyses ◽  
Domain Specific ◽  
Gender And Age ◽  
Confirmatory Factor

The present study explores the factorial structure and the degree of measurement invariance of 12 divergent thinking tests. In a large sample of German students (N = 1328), a three-factor model representing verbal, figural, and numerical divergent thinking was supported. Multigroup confirmatory factor analyses revealed that partial strong measurement invariance was tenable across gender and age groups as well as school forms. Latent mean comparisons resulted in significantly higher divergent thinking skills for females and students in schools with higher mean IQ. Older students exhibited higher latent means on the verbal and figural factor, but not on the numerical factor. These results suggest that a domain-specific model of divergent thinking may be assumed, although further research is needed to elucidate the sources that negatively affect measurement invariance.

APA Book Talk: The Psychology of High Performance: Developing Human Potential Into Domain-Specific Talent

2020 ◽  
Author(s):  
Jamie Buck ◽  
Rena Subotnik ◽  
Frank Worrell ◽  
Paula Olszewski-Kubilius ◽  
Chi Wang
Keyword(s):  
High Performance ◽  
Human Potential ◽  
Domain Specific ◽  
Book Talk
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