Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education

Artificial Intelligence (AI) and User Experience (UX) design: A systematic literature review and future research agenda

PurposeThe aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as Artificial Intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.Design/Methodology/ApproachThis article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.FindingsOur analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. We also highlight the ways in which AI is changing the UX design process through illustrative examples.Originality/valueWhile there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, we show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans

Integrated design optimization research and development in an industrial environment

An overview is given of a design optimization project that is in progress at the GE Research and Development Center for the past few years. The objective of this project is to develop a methodology and a software system for design automation and optimization of structural/mechanical components and systems. The effort focuses on research and development issues and also on optimization applications that can be related to real-life industrial design problems. The overall technical approach is based on integration of numerical optimization techniques, finite element methods, CAE and software engineering, and artificial intelligence/expert systems (AI/ES) concepts. The role of each of these engineering technologies in the development of a unified design methodology is illustrated. A software system DESIGN-OPT has been developed for both size and shape optimization of structural components subjected to static as well as dynamic loadings. By integrating this software with an automatic mesh generator, a geometric modeler and an attribute specification computer code, a software module SHAPE-OPT has been developed for shape optimization. Details of these software packages together with their applications to some 2- and 3-dimensional design problems are described

Innovative Integration: Exploring AI Art Platforms in Architectural Education for Mosque Facade Design

This research aims to investigate the effectiveness of implementing the mentoring model in Mosque facade design presents a unique challenge in the field of architecture due to their importance as the mosque's front-facing feature and their role in representing Islamic identity. In this study, an AI art platform was introduced as a tool to improve the creativity and design process for architecture students and professionals. The study investigated the platform's novel application in the context of architectural learning for mosque façade design. The research methodology was divided into three parts: a description of the platforms used, identification of common use cases in mosque architectural design, and query analysis using NLP methods in Midjourney AI art platform. According to the findings of the study, the AI art platform is a valuable tool for architectural learning, allowing students and professionals to generate diverse and innovative designconcepts. The Midjourney AI art platform has flaws such as difficulty recognizing architectural styles from specific regions

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Maximizing team synergy in AI-related interdisciplinary groups: an interdisciplinary-by-design iterative methodology

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: (i) definition of the hypothesis space, (ii) building-up of a common lexicon, (iii) scenario-building, (iv) interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies

EMPIRICAL RESEARCH ON HUMAN-AI COLLABORATIVE ARCHITECTURAL DESIGN PROCESS THROUGH A DEEP LEARNING APPROACH

北九州市立大学博士(工学)The purpose of this thesis is to explore how AI technologies intervene in the architectural design process and to discuss the importance and approaches that drive the paradigm shift towards human-AI collaboration in architectural design. The research is conducted from two perspectives: theoretical and practical. At the theoretical level, how AI technologies affect architectural design through technological evolution is analyzed, as well as the advantages, disadvantages and trends of different AI networks in sustainably analyzing and optimizing different kinds of architectural designs. Further, based on this, the methodology of how to develop a reflection on the nature of technology and data is discussed. At the practical level, AI methods that are inventive and capable of performance-based design are constructed and trained. And the basic process of human-AI collaborative architectural design is presented with an empirical study. The results of this thesis not only provide a theoretical reference and methodological basis for future research on human-AI collaborative architectural design at a broader and higher level but also attempt to explore new ideas and methods for the field of architectural design during the evolution of the old and new paradigms, ultimately realizing the purpose of sustainable development of the B&C industry.doctoral thesi

Appreciative inquiry for stress management

Purpose: The purpose of this paper is to demonstrate the innovative application of an Appreciative Inquiry (AI) approach for the design and implementation of organizational stress management interventions, alongside a case study of the successful design and implementation of the approach. By utilizing the AI methodology to develop a “local stress theory” for the participating organization, the authors propose a model which can be utilized in other similar organizations. Design/methodology/approach: Stage 1: 35 participants completed up to ten daily logs by answering four positively framed questions regarding their working day. Stage 2: semi-structured interviews (n=13). The interview schedule was designed to further elaborate log findings, and begin looking into feasible organizational changes for improvement of stress. Stage 3: two focus groups (Stage 3, total 13 employees) verified interventions from logs and interviews and discuss how these can be implemented. Findings: The log phase identified two key themes for improvement: managerial/organizational support and communication. From these, interviews and focus groups led to workable proposals for simple but likely effective changes. The authors reported findings to management, emphasizing organizational change implementation, and these were subsequently implemented. Research limitations/implications: The study demonstrated the effectiveness of AI to identify and implement relatively simple but meaningful changes. The AI cycle was completed but allocating lengthy follow-up time for evaluation of outcomes was not possible, although initial responses were favorable. There are also issues of generalizability of the findings. Originality/value: This is the among first studies to utilize an AI approach for the design of stress management interventions