Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real-World Problem-Solving Task

1993 ◽  
Author(s):  
Ashwin Ram ◽  
S. Narayanan ◽  
Michael T. Cox
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Multistrategy Learning ◽  
Real World Problem ◽  
Problem Solving Task

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals Preparing Biomedical Engineers For Real World Problem Solving

2020 ◽  
Author(s):  
David Kelso ◽  
John D. Enderle ◽  
Kristina Ropella
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Real World Problem

Task Ontology-Based Human-Computer Interaction

2009 ◽  
pp. 950-960
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Task Ontology-Based Human-Computer Interaction

2006 ◽  
pp. 588-596
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

The Effects of Elaboration in Creativity Tests as it Pertains to Overall Scores and How it Might Prevent a Person From Thinking of Creative Ideas During the Early Stages of Brainstorming and Idea Generation

2015 ◽  
Author(s):  
Caitlin Dippo ◽  
Barry Kudrowitz
Keyword(s):  
Problem Solving ◽  
Negative Correlation ◽  
Real World ◽  
Idea Generation ◽  
Occurrence Rate ◽  
The Novel ◽  
Strong Negative Correlation ◽  
Relative Occurrence ◽  
Real World Problem ◽  
Creative Solutions

Previous studies have found that the first few ideas we think of for a given prompt are likely to be less original than the later ideas. In this study, 460 participants were given the Alternative Uses Test (AUT) where they were asked to list alternative uses for a paperclip, creating a database of 235 unique answers, each having a relative occurrence rate in that pool. It was found that later responses were significantly more novel than early responses and on average the originality of responses exponentially increased with quantity. A closer look at this data reveals that a person is likely to have a lower overall originality score if he or she has more elaborate responses. 89 of these participants were also given the Abbreviated Torrance Test For Adults (ATTA) and the data from both tests was used to study relationships between elaboration, fluency, and originality. The data from the AUT reveals a strong negative correlation between an individual’s average number of words per response and his or her average originality score. It is hypothesized that people who spend more time writing multiple-word responses have less time to generate many different ideas thus hindering their ability to reach the novel ideas. Similarly, the ATTA reveals that after two extraneous details, elaboration on a drawing will negatively impact fluency and originality scores. This is not to say that elaborate ideas cannot be original, but rather that in time-limited situations, elaboration may hinder the production of original ideas. In applying this to real world problem solving and idea generation, it is suggested that people may prevent themselves from finding creative solutions if too much time is spent on discussing the first few suggested ideas from a brainstorming session. It is suggested that a more effective brainstorming session will delay discussion until a significant number of ideas are generated.

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