The design research pyramid: a three layer framework

To support knowledge-based design development, considerable research has been conducted from various perspectives at different levels. The research on knowledge-based design support systems, generic design artefact and design process modelling, and the inherent quality of design knowledge itself are some examples of these perspectives. The structure underneath the research is not a disparate one but ordered. This paper provides an overview of some ontologies of design knowledge and a layered research framework of knowledge-based engineering design support. Three layers of research are clarified in this pattern: knowledge ontology, design knowledge model, and application. Specifically, the paper highlights ontologies of design knowledge by giving a set of classifications of design knowledge from different points of view. Within the discussion of design knowledge content ontology, two topologies, i.e., teleology and evolutionary, are identified

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Gaming techniques and the product development process : commonalities and cross-applications

The use of computer-based tools is now firmly embedded within the product development process, providing a wide range of uses from visualisation to analysis. However, the specialisation required to make effective use of these tools has led to the compartmentalisation of expertise in design teams, resulting in communication problems between individual members. This paper therefore considers how computer gaming techniques and strategies could be used to enhance communication and group design activities throughout the product design process

Designinig Coordination among Human and Software Agents

The goal of this paper is to propose a new methodology for designing coordination between human angents and software agents and, ultimately, among software agents. The methodology is based on two key ideas. The first is that coordination should be designed in steps, according to a precise software engineering methodology, and starting from the specification of early requirements. The second is that coordination should be modeled as dependency between actors. Two actors may depend on one another because they want to achieve goals, acquire resources or execute a plan. The methodology used is based on Tropos, an agent oriented software engineering methodology presented in earlier papers. The methodology is presented with the help of a case study

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Considerations in development of expert systems for real-time space applications

Over the years, demand on space systems has increased tremendously and this trend will continue for the near future. Enhanced capabilities of space systems, however, can only be met with increased complexity and sophistication of onboard and ground systems. Artificial Intelligence and expert system techniques have great potential in space applications. Expert systems could facilitate autonomous decision making, improve in-orbit fault diagnosis and repair, enhance performance and reduce reliance on ground support. However, real-time expert systems, unlike conventional off-line consultative systems, have to satisfy certain special stringent requirements before they could be used for onboard space applications. Challenging and interesting new environments are faced while developing expert system space applications. This paper discusses the special characteristics, requirements and typical life cycle issues for onboard expert systems. Further, it also describes considerations in design, development, and implementation which are particularly important to real-time expert systems for space applications

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions