Outcomes, Implications, and Concluding Thoughts

2017 ◽  
Author(s):  
Richard E. Ocejo
Keyword(s):  
Problem Solving ◽  
Interpersonal Communication ◽  
Culturally Relevant ◽  
New Economy ◽  
Manual Labor ◽  
Cultural Understanding ◽  
Knowledge Based ◽  
Mental Problem ◽  
Cultural Repertoires ◽  
Career Options

This concluding chapter reviews the alternative paths for how workers are dealing with conditions of the precarious new economy. They are entering common occupations in everyday workplaces that people do not normally think of as knowledge-based or culturally relevant, and transforming them into high-end, quality jobs that fuse mental and manual labor and that people with other work opportunities see as viable career options. These workers experience manual labor as meaningful and even fun through the enactment of a set of cultural repertoires that allow for physical, bodily labor, challenging mental problem-solving, cultural understanding, and interpersonal communication. The jobs also require the confident performance of each of these work practices in concert, not independently of the others.

Masters of Craft

2017 ◽  
Author(s):  
Richard E. Ocejo
Keyword(s):  
Labor Market ◽  
Young Men ◽  
New Economy ◽  
Technical Skills ◽  
Manual Labor ◽  
Work Practices ◽  
Downward Mobility ◽  
Postindustrial City ◽  
New Form ◽  
Cultural Repertoires

In today's new economy—in which “good” jobs are typically knowledge or technology based—many well-educated and culturally savvy young men are instead choosing to pursue traditionally low-status manual-labor occupations as careers. This book looks at the renaissance of four such trades: bartending, distilling, barbering, and butchering. The book takes readers into the lives and workplaces of these people to examine how they are transforming these once-undesirable jobs into “cool” and highly specialized upscale occupational niches—and in the process complicating our notions about upward and downward mobility through work. It shows how they find meaning in these jobs by enacting a set of “cultural repertoires,” which include technical skills based on a renewed sense of craft and craftsmanship and an ability to understand and communicate that knowledge to others, resulting in a new form of elite taste-making. The book describes the paths people take to these jobs, how they learn their chosen trades, how they imbue their work practices with craftsmanship, and how they teach a sense of taste to their consumers. The book provides new insights into the stratification of taste, gentrification, and the evolving labor market in today's postindustrial city.

Configuration Tree Solver: A Technology for Automated Design and Configuration

1990 ◽  
Author(s):  
Alexander Kott ◽  
Gerald Agin ◽  
Dave Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Automated Design ◽  
Automotive Component ◽  
Business Operation ◽  
Knowledge Based ◽  
Specific Configuration

Abstract Configuration is a process of generating a definitive description of a product or an order that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an enabling factor in automation of configuration tasks found in the business operation. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or-tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

Problem solving methods and knowledge systems: A personal journey to perceptual images as knowledge

2009 ◽  
Vol 23 (4) ◽  
pp. 331-338
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Mental Image ◽  
Spatial Relations ◽  
Point Of View ◽  
Design Strategies ◽  
Symbolic Form ◽  
Knowledge Based ◽  
High Level ◽  
Use Of Knowledge

AbstractI was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support theconstructionof knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.

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.

Leading Learning Communities with Creative Practice

2021 ◽  
pp. 91-103
Author(s):  
Susannah L. Brown ◽  
Jennifer Lynne Bird ◽  
Ann Musgrove ◽  
Jillian Powers
Keyword(s):  
Instructional Technology ◽  
Problem Solving ◽  
Learning Communities ◽  
Technology Education ◽  
Interpersonal Communication ◽  
Personal Growth ◽  
Professional Growth ◽  
Growth And Development ◽  
Leadership Theories ◽  
Communication Problem

Reflective leadership stories from various fields including, instructional technology, education and humanities guide the reader to reflect upon practice. Leadership theories that support personal growth, caring, interpersonal communication, problem solving, and creativity are discussed (Bass, 2008). Furthermore, the authors describe how creative leaders can use Communities of Practice (CoPs) as a mechanism to share and build knowledge, solve problems, and foster professional growth and development.

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.

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