Domain knowledge and the design process

McDermott, John

doi:10.1016/0142-694x(82)90077-1

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…In the context of design‐thinking and innovation, AI is already changing, but could actually transform, innovative idea generation (Verganti et al, 2020). In design, AI may be particularly helpful as design problems are often ill‐structured and require search over a large space (McDermott, 1982). Enhancing AI’s potential today is the observational data available from mobile and wearable devices.…”

Section: A Conceptual Model Of Innovative Idea Generation In Problem ...mentioning

confidence: 99%

“…On the other hand, mental models have their drawbacks and can impede innovative idea generation. Since problems in design and innovation are often novel and unique, knowledge acquired in the past frequently is not applicable to the present problem at hand and must be adapted to fit an ill‐structured present problem (McDermott, 1982; Muller & Pasman, 1996). Having to escape the confines of prior knowledge and mine new knowledge from new sources or other individuals complicates matters further.…”

Section: A Conceptual Model Of Innovative Idea Generation In Problem ...mentioning

confidence: 99%

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Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Garbuio

Lin

2021

J of Product Innov Manag

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This article addresses gaps about abductive reasoning—widely considered key to design‐thinking but rarely detailed in design‐thinking and innovation literatures—by examining two types of abduction; identifying impediments to it; and proposing the promise of Artificial Intelligence (AI) to mitigate those impediments. Contrasting with the deductive and inductive approaches that dominant problem‐solving, we distinguish and elucidate explanatory abduction and innovative abduction in problem finding, where the problem to solve is itself uncertain. We argue these are appropriate for generating innovative problem‐finding ideas. Focusing thenceforth on problem finding alone, the heart of the article proposes a comprehensive conceptual model of innovative idea generation in that more ambiguous, complex, under‐researched but exciting problem space. The model details three chief stages: (1) problem search frame, combining leadership’s vision and innovators’ knowledge; (2) generating abductive hypotheses from often‐surprising observations and their synthesis into insights; and (3) evaluating abductive hypotheses, against the novel quality criteria of plausibility and relevance. Among cognitive impediments we show how the downsides of mental model, limited cognitive load and exemplifying heuristics and cognitive biases, such as confirmation bias, can hinder each stage. Conversely, we examine how support from AI can help human innovators improve the quantity, speed, and quality of their innovative idea generation.

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Section: A Conceptual Model Of Innovative Idea Generation In Problem ...mentioning

confidence: 99%

Section: A Conceptual Model Of Innovative Idea Generation In Problem ...mentioning

confidence: 99%

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Garbuio

Lin

2021

J of Product Innov Manag

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“…17-28). As McDermott (1982) put it, given the understanding that design is an ill-structured activity, and that the set of constraints applicable to specific design problems is often substantial, one can hope to surmount these problems only when significant volumes of domain specific knowledge can be combined and fused together at every stage of the problem solving process (p. 36). In this context, ill-defined design problems necessitate nothing but a large base of appropriate prior knowledge for the formation of their solutions.…”

Section: Cognitive Use Of Prior Knowledge In Architectural Designmentioning

confidence: 99%

The Cognitive Use of Prior Knowledge in Design Cognition: The Role of Types and Precedents in Architectural Design

Ürey

2019

JCUA

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This paper examines the cognitive use of prior knowledge in design and evaluates the role of types and precedents in architectural design and education from a cognitive perspective. Previous research on design cognition shows that the amount of prior knowledge possessed by the designer plays a fundamental role in the production and quality of the creative outcome. Prior knowledge is thought to be held by way of specific cognitive structures that are called cognitive schemas and, the role of our cognitive schemas (be it personal or cultural schemas) is portrayed as indispensable for the formation of our creative productions. Although significant efforts were made in the way of studying the use of prior knowledge in design, the correlation of types and cultural schemas has yet to be explored. This paper examines this correlation between cultural schemas, a markedly cognitive concept, and types, an architectural one, culminating in an investigation of the cognitive role of types and precedents within architectural design and education in the light of the cognitive literature. Building on that attempt, the study endeavors to conduct an interdisciplinary theoretical inquiry that respectively studies the role of prior knowledge in design cognition, the concept of cognitive-cultural schemas, the concept of type and its relationship with cultural schemas, and finally, the cognitive role of types and precedents in architectural design and education. In conclusion, this study proposes that, in terms of function, types are virtually identical to cultural schemas at the cognitive level, and types and precedents have a generative value for architectural design, by virtue of the fact that they exist as the initial cognitive schemas that are employed at the beginning of the design process.

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“…Any planning system is embedded in a real environment, that is, a myriad of "non-system" tools, objects, people, and processes with which it must interact. It is necessary to have a detailed model of this domain environment and it is important that this model be developed independently of the planning system (McDermott 1981). In itSIMPLE, the modeling phase is composed of dual layered sections: one concerned with the modeling of the environment where the plan is to be executed and the other dealing with a specific planning instance to be solved.…”

Section: Requirements Elicitation and Modeling With Umlmentioning

confidence: 99%

itSIMPLE: towards an integrated design system for real planning applications

Vaquero

Silva

Tonidandel

et al. 2013

The Knowledge Engineering Review

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Since the end of the 1990s, there has been an increasing interest in the application of artificial intelligence (AI) planning techniques to solve real-life problems. In addition to characteristics of academic problems, such as the need to reason about actions, real-life problems require detailed knowledge elicitation, engineering, and management. A systematic design process in which Knowledge and Requirements Engineering tools play a fundamental role is necessary in such applications. One of the main challenges in such design process, and consequently in the study of Knowledge Engineering in AI planning, has been the analysis of requirements and their subsequent transformation into an input-ready model for planners. itSIMPLE is a research project dedicated to the study of a project process to support the design phases of real-life planning models. In this paper, we give an overview of itSIMPLE focusing on the main translation processes among a minimal set of representations: from requirements represented in Unified Modeling Language (UML) to Petri Nets and from UML models to planning domain definition language for problem solving.

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Domain knowledge and the design process

Cited by 16 publications

References 5 publications

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

The Cognitive Use of Prior Knowledge in Design Cognition: The Role of Types and Precedents in Architectural Design

itSIMPLE: towards an integrated design system for real planning applications

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