Extracting general task structures to accelerate the learning of new tasks

2016 ◽  
Author(s):  
Ilya Dianov ◽  
Karinne Ramirez-Amaro ◽  
Pablo Lanillos ◽  
Emmanuel Dean-Leon ◽  
Florian Bergner ◽  
...  
Keyword(s):  
Task Structures

Agent Support for Design of Aircraft Parts

1998 ◽  
Author(s):  
Krishna N. Jha ◽  
Andrea Morris ◽  
Ed Mytych ◽  
Judith Spering
Keyword(s):  
Preliminary Design ◽  
Formal Representation ◽  
Web Based ◽  
Agent Based ◽  
Multi Agent ◽  
High Level ◽  
Legacy Applications ◽  
The Cost ◽  
And Control ◽  
Task Structures

Abstract Designing aircraft parts requires extensive coordination among multiple distributed design groups. Achieving such a coordination is time-consuming and expensive, but the cost of ignoring or minimizing it is much higher in terms of delayed and inferior quality products. We have built a multi-agent-based system to provide the desired coordination among the design groups, the legacy applications, and other resources during the preliminary design (PD) process. A variety of agents are used to model the various design and control functionalities. The agent-representation includes a formal representation of the task-structures. A web-based user-interface provides high-level interface to the users. The agents collaborate to achieve the design goals.

Buddy Up for Exergames

2016 ◽  
pp. 222-244
Author(s):  
Alison Ede ◽  
Samuel Thomas Forlenza ◽  
Deborah L. Feltz
Keyword(s):  
Physical Activity ◽  
Chronic Disease ◽  
Health Outcomes ◽  
Group Dynamics ◽  
Health And Human Services ◽  
Department Of Health ◽  
Adults And Children ◽  
Task Structures ◽  
Motivation Gain ◽  
The U.S

Many adults and children in the U.S. are not active enough to meet the U.S. Department of Health and Human Services guidelines for physical activity to maintain health and reduce the risk of chronic disease. Exergames (exercise video games) have the potential to promote physical activity, and researchers have examined ways for improving motivation to exercise for longer and at higher intensities with these types of games. This chapter considers group dynamics principles as one way to influence motivation within exergames to help realize better health outcomes. We illustrate how group dynamics principles can be applied to exergames and how different task structures within groups (e.g., conjunctive, additive, and coactive tasks) can influence motivation. One group dynamic principle, the Köhler motivation gain effect, has been the basis of a series of research studies that we have conducted within exergames. We summarize this research, discuss the issues, controversies, and problems with using group dynamics in exergames, and provide possible solutions and recommendations.

Exploring the Challenges of Supporting Collaborative Mobile Learning

2011 ◽  
Vol 3 (4) ◽  
pp. 54-69 ◽  
Author(s):  
Jalal Nouri ◽  
Teresa Cerratto-Pargman ◽  
Johan Eliasson ◽  
Robert Ramberg
Keyword(s):  
Collaborative Learning ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Collaborative Problem Solving ◽  
Learning Activity ◽  
Task Structure ◽  
Related Information ◽  
Collaborative Skills ◽  
Task Structures ◽  
Asymmetrically Distributed

Mobile technology opens up opportunities for collaborative learning in otherwise remote contexts outside the classroom. A successful realization of these opportunities relies, however, on mobile learning activities providing adequate collaboration structures. This article presents an empirical study aimed at examining the role played by mobile devices, teachers and task structures as a means for collaborative learning in geometry. The study focused on the analysis of the nature of collaboration that unfolded when students measured areas outdoors in the field. The analysis of the mobile learning activity was conducted from an Activity theory perspective. The findings obtained indicate that the collaboration observed may be impaired if: 1) the functionalities needed for collaborative problem-solving are asymmetrically distributed on a number of mobile devices; 2) task-related information is not accessible to all learners; 3) the task structure is not sufficiently complex; 4) teacher scaffolding is too readily available; and 5) necessary collaborative skills are not developed.

scholarly journals Automation and the Welfare State: Technological Change as a Determinant of Redistribution Preferences

2017 ◽  
Vol 52 (2) ◽  
pp. 171-208 ◽  
Author(s):  
Stefan Thewissen ◽  
David Rueda
Keyword(s):  
Technological Change ◽  
Theoretical Framework ◽  
Income Loss ◽  
Manual Task ◽  
Occupational Risks ◽  
Routine Work ◽  
Increased Risk ◽  
Future Income ◽  
Task Structures

Technological change is widely considered to be a key driver of the economic and occupational structure of affluent countries. Current advances in information technology have led to a significant substitution of routine work by capital, while occupations with abstract or interpersonal manual task structures are complemented or unaffected. We develop a simple theoretical framework for the reasons why individuals in routine task-intensive occupations would prefer public insurance against the increased risk of future income loss resulting from automation. Moreover, we contend that this relation will be stronger for richer individuals who have more to lose from automation. We focus on the role of occupational elements of risk exposure and challenge some general interpretations of the determinants of redistribution preferences. We test the implications of our theoretical framework with survey data for 17 European countries between 2002 and 2012. While up to now the political economy literature has emphasized other occupational risks, we find vulnerability to automation to be an important determinant of the demand for redistribution that should not be ignored.

scholarly journals Task-structures, knowledge acquisition and learning

1989 ◽  
Vol 4 (3-4) ◽  
pp. 339-345 ◽  
Author(s):  
B. Chandrasekaran
Keyword(s):  
Knowledge Acquisition ◽  
Task Structures

Signposting: An AI Approach to Supporting Human Decision Making in Design

2000 ◽  
Author(s):  
Martin Stacey ◽  
P. John Clarkson ◽  
Claudia Eckert
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Problem Solving ◽  
Engineering Problem ◽  
Cognitive Engineering ◽  
Design Parameters ◽  
Management Tools ◽  
Design Activities ◽  
Control Knowledge ◽  
Task Structures

Abstract Artificial intelligence provides powerful techniques for formalising the art of engineering problem solving: for modelling products, describing task structures, and representing problem solving expertise as inference knowledge and control knowledge. Signposting systems extend the scope of these methods beyond automatic design by using them to provide both information and guidance for decision-making by human designers. This paper outlines the application of AI methods according to cognitive engineering considerations, to the development of knowledge management tools for engineering design. These tools go beyond conventional knowledge management and decision support approaches by supplying both inference knowledge and strategic problem solving knowledge to the user, as well as information about the state of the design. By focusing on tasks and on the dependencies between design parameters, signposting systems support contingent and flexible organisation of activities. Such tools can support product modelling, design process planning and capturing expert design knowledge, in a form that can be used directly to guide the organisation of design activities and the performance of individual tasks. A key element of this approach is the incremental acquisition of product models, task structures and problem solving knowledge by defining variant cases.

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