scholarly journals Design Support to steer Creative Wicked Problem Solving Processes with Knowledge Management and Artificial Intelligence

2019 ◽  
Vol 1 (1) ◽  
pp. 21-31
Author(s):  
Langenhan C ◽  
Eisenstadt V ◽  
Eisenstadt V ◽  
Petzold F ◽  
Althoff K ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Problem Solving ◽  
Design Process ◽  
Wicked Problem ◽  
Design Support ◽  
Relevant Reference ◽  
Interdisciplinary Problems

As the complexity of building tasks and requirements increases, designers often find themselves confronted with interdisciplinary problems that go beyond the specific challenges and methods of architecture. The iterative nature of the design process results in a continuous exchange between creative, analytical and evaluative activities, through which the designer explores and identifies promising design variants. The ability to compare and evaluate relevant reference examples of already built or designed buildings helps designers to assess their own design and informs the design process.

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

Use of Knowledge Management Systems and the Impact on the Acquisition of Explicit Knowledge

2008 ◽  
Vol 22 (2) ◽  
pp. 77-101 ◽  
Author(s):  
Holli McCall ◽  
Vicky Arnold ◽  
Steve G. Sutton
Keyword(s):  
Knowledge Management ◽  
Problem Solving ◽  
Knowledge Acquisition ◽  
Reference Materials ◽  
Explicit Knowledge ◽  
Experimental Methodology ◽  
Management Systems ◽  
Knowledge Management Systems ◽  
Use Of Knowledge ◽  
The Impact

ABSTRACT: In an era where knowledge is increasingly seen as an organization's most valuable asset, many firms have implemented knowledge-management systems (KMS) in an effort to capture, store, and disseminate knowledge across the firm. Concerns have been raised, however, about the potential dependency of users on KMS and the related potential for decreases in knowledge acquisition and expertise development (Cole 1998; Alavi and Leidner 2001b; O'Leary 2002a). The purpose of this study, which is exploratory in nature, is to investigate whether using KMS embedded with explicit knowledge impacts novice decision makers' judgment performance and knowledge acquisition differently than using traditional reference materials (e.g., manuals, textbooks) to research and solve a problem. An experimental methodology is used to study the relative performance and explicit knowledge acquisition of 188 participants partitioned into two groups using either a KMS or traditional reference materials in problem solving. The study finds that KMS users outperform users of traditional reference materials when they have access to their respective systems/materials, but the users of traditional reference materials outperform KMS users when respective systems/materials are removed. While all users improve interpretive problem solving and encoding of definitions and rules, there are significant differences in knowledge acquisition between the two groups.

A model of the mechanical design process based on empirical data

1988 ◽  
Vol 2 (1) ◽  
pp. 33-52 ◽  
Author(s):  
David G. Ullman ◽  
Thomas G. Dietterich ◽  
Larry A. Stauffer
Keyword(s):  
Problem Solving ◽  
Detailed Analysis ◽  
Conceptual Design ◽  
Design Process ◽  
Empirical Data ◽  
Mechanical Design ◽  
Level Of Detail ◽  
Cad Tools ◽  
Design Alternatives ◽  
Key Features

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.

The role of artificial intelligence in knowledge management

2000 ◽  
Vol 13 (5) ◽  
pp. 235-239 ◽  
Author(s):  
E Tsui ◽  
B.J Garner ◽  
S Staab
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management

The Algorithm of Case-Searching Based on AHP Application

2014 ◽  
Vol 513-517 ◽  
pp. 2416-2419
Author(s):  
Cai Xia Wang ◽  
Ning Liu
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Case Management ◽  
Critical Case ◽  
Case Analysis ◽  
Basic Function ◽  
Knowledge Management System ◽  
Teaching Case ◽  
Trained Teachers ◽  
Case Reasoning

The knowledge management system of teaching case corpus adopts case reasoning technology in the field of artificial intelligence. The whole system includes altogether ten modules. They are case uploading, case modification, case analysis, case algorithm and critical case management. The basic function is to assist the trained teachers to get the teaching case knowledge from other teachers, so as to develop teachersspecialty. In the module of case algorithm , in the application of the algorithm of case-searching based on AHP, the case needed by the users can be sorted out in an objective and fair way.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

Toward a Reasoning Framework of Design As Synthesis

1999 ◽  
Author(s):  
Masaharu Yoshioka ◽  
Tetsuo Tomiyama
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Design Process ◽  
Process Modeling ◽  
Implementation Strategy ◽  
Multiple Model ◽  
Hypothetical Reasoning ◽  
Design Processes ◽  
Design Process Modeling ◽  
Do So

Abstract Most of the previous research efforts for design process modeling had such assumptions as “design as problem solving,” “design as decision making,” and “design by analysis,” and did not explicitly address “design as synthesis.” These views lack notion and understanding about synthesis. Compared with analysis, synthesis is less understood and clarified. This paper discusses our fundamental view on synthesis and approach toward a reasoning framework of design as synthesis. To do so, we observe the designer’s activity and formalize knowledge operations in design processes. From the observation, we propose a hypothetical reasoning framework of design based on multiple model-based reasoning. We discuss the implementation strategy for the framework.

Sign in / Sign up

Export Citation Format

Share Document