The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.

New methods of configuration analysis have recently emerged that are based on development trends characteristic of many technical systems. It has been found that though the development of any system aims to increase a combination of the performance, reliability and economy, actual design changes are frequently kept to a minimum to reduce the risk of failure. However, a strategy of risk reduction commits the designer to an existing configuration and an approved set of components and materials. Therefore, it is important to analyze the configurations, components, and materials of past designs so that good aspects may be reused and poor ones changed. A good configuration produces the required performance and reliability with maximum economy. These three evaluation criteria form the core of a configuration optimization tool called KATE, where known configurations are optimized producing a set of ranked trial solutions. The authors suggest that this solution set contains valuable design knowledge that can be reused. This paper briefly introduces a generic method of configuration evaluation and then describes the use of a self-organizing neural network, the Kohonen Feature Map, to analyze solution sets by performing an initial data reduction step, producing archetype solutions, and supporting qualitative clustering.

Previous research on qualitative reasoning about shape and fit laid the foundations to determine whether two objects fit together. Continued investigation has refined the theory and has produced a functioning implementation. This paper describes extensions to the theory and the details of the implementation.

The reasoning process has been divided into five layers: grouping, topology, orientation, matching, and confirmation. The grouping layer clusters features such as cubes or cylinders into groups for each surface of an object. The topology layer recognizes patterns formed by the groups on each surface, and describes the pattern in terms of topological structures. The orientation layer selects promising surfaces from the two objects and attempts to align the two surfaces. If the orientation layer aligns the topological structures on the two surfaces, the matching layer tries to pair the features within the topological structures. The confirmation layer inspects paired features to determine whether the surfaces are compatible. If the surfaces are compatible, then the two objects qualitatively fit together.

This paper presents an approach for the case adaptation, especially case repairing, in a case-based process planning system: PROCASE, for machining of rotational parts. In PROCASE, a new process plan is generated by adapting an existing similar process plan from its case library. Case adaptation is a crucial issue in achieving an automated case-based process planning system. This is because, usually, an existing process plan cannot necessarily produce an exact identical part as of the desired part. Adaptation is essential to tailor this existing plan to generate a new process plan for the new part. The case adaptation in this paper comprises case modification, case simulation, and case repairing. The modifier uses the knowledge extracted from case library to edit the retrieved similar plan. The simulator plays an important role in verifying the adapted plan as well as in directing the plan repairing. The repairing rules are indexed by the error messages obtained from the simulation. With the proposed case adaptation, the system will have the capability to repair the erroneous plans to achieve an automated and intelligent process planning system. This paper will first briefly introduce the case representation and case retrieval in PROCASE. Then the rest of the paper is dedicated to the case adaptation.

Learning is an intrinsic product of case-based reasoning. Acquiring new cases is one possible way of learning in a case-based system. These cases comprise mainly success knowledge. The successful cases are essentially used to generate new design solutions. But a case-based system also can make use of failure knowledge. In this paper we present how a case-based system can acquire failure cases for verification of the solution created by success cases. We describe IM-RECIDE, a system that uses case-based reasoning for solving design problems that are imperfectly described and explained. The learning aspect is focused and some of the machine learning dimensions in design are criticized. Experimental results in the domain of room configuration also are presented.

Nowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

Modern design embraces digital augmentation, especially in the interplay of digital media content and the physical dispersion and handling of information. Based on the observation that small paper memos with sticky backs (such as Post-Its™) are a powerful and frequently used design tool, we have created Post-Bits, a new interface device with a physical embodiment that can be handled as naturally as paper sticky notes by designers, yet add digital information affordances as well. A Post-Bit is a design prototype of a small electronic paper device for handling multimedia content, with interaction control and display in one thin flexible sheet. Tangible properties of paper such as flipping, flexing, scattering, and rubbing are mapped to controlling aspects of the multimedia content such as scrubbing, sorting, or up- or downloading dynamic media (images, video, text). In this paper we discuss both the design process involved in building a prototype of a tangible interface using new technologies, and how the use of Post-Bits as a tangible design tool can impact two common design tasks: design ideation or brainstorming, and storyboarding for interactive systems or devices.

Previous applications of genetic programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters (e.g., geometrical parameters) to a single design objective (e.g., weight). In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands “cooperate,” simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP heuristics extraction method, is described and illustrated by means of a design case study.

The paper describes and evaluates three methods of modelling earthquake accelerograms using artificial neural networks and linear predictors. These are a radial-Gaussian neural networking system, a linear predictor, and a hybrid of these two. Two methods of using these models to predict ground acceleration are adopted. The first is based on a direct prediction of ground acceleration at some point in time in the future from a series of ground accelerations that occurred earlier in the earthquake event. The second is a recursive approach whereby a model predicts a sequence of future ground accelerations by feeding back its predictions to its inputs. The performances of the models are tested using the 1985 Mexico earthquake and aftershock. The linear predictor and hybrid models perform best at direct prediction while none of the models perform particularly well at recursive prediction. The paper concludes with an outline of some areas for future work.

This paper presents a method for automatically generating new designs from a set of existing objects of the same class using machine learning. In this particular work, we use a custom parametric chair design program to produce a large set of chairs that are tested for their physical properties using ergonomic simulations. Design schemata are found from this set of chairs and used to generate new designs by placing constraints on the generating parameters used in the program. The schemata are found by training decision trees on the chair data sets. These are automatically reverse engineered by examining the structure of the trees and creating a schema for each positive leaf. By finding a range of schemata, rather than a single solution, we maintain a diverse design space. This paper also describes how schemata for different properties can be combined to generate new designs that possess all properties required in a design brief. The method is shown to consistently produce viable designs, covering a large range of our design space, and demonstrates a significant time saving over generate and test using the same program and simulations.

Qualitative physics, a subfield of artificial intelligence, adapts intuitive and non-numerical reasoning for descriptive analysis of physical systems. The application of a set-based qualitative algebra to matrix analysis (QMA) allows for the development of a qualitative matrix stiffness methodology for linear elastic structural analysis. The unavoidable introduction of arithmetic ambiguity requires the reinforcement of physical constraints complementary to standard matrix operations. The overall analysis technique incorporates such constraints within the set-based framework with logic programming. Truss, beam, and frame structures demonstrate constraint relationships, which prune spurious solutions resulting from qualitative arithmetic relations. Though QMA is not a panacea for all structural applications, it provides greater insight into new notions of physical analysis.

Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.

The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

Can artificial systems be creative? Can they be designed to be creative on their own? What are the requirements of such creative artificial systems? To be able to support humans who are expected to deliver creative solutions, or to automate part of their tasks, this paper presents a proposal for creativity requirements that provide a basis for designing creative artificial systems.

Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine learning methods. The set of collected inputs in the manufacturing environment, including a number of products, the number of welding operations, product's surface area factor, difficulty/working environment factors, and the number of metal forming processes. The data were collected from one of the largest bus manufacturing companies in Turkey. Experimental results demonstrate that when model accuracy was measured using performance measures, k-nearest neighbors outperformed other machine learning techniques in terms of prediction accuracy. “The number of welding operations” and “the number of pieces” were found to be the most effective parameters. The findings show that machine learning algorithms can estimate standard time, and the findings can be used for several purposes, including lowering production costs, increasing productivity, and ensuring efficiency in the execution of their operating processes by other companies that manufacture similar products.

Reuse has long been a major goal of the knowledge engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis, we chose a well-known benchmark knowledge base (KB) system written in CLIPS, which was based on the propose and revise problem-solving method and which had a lift/elevator KB. The KB contained four components, including constraints and data tables, expressed in an ontology that reflects the propose and revise task structure. Sufficient trial data was extracted manually to demonstrate the approach on two alternative problem solvers: a spreadsheet (Excel) and a constraint logic solver (ECLiPSe). The next phase was to implement ExtrAKTor, which automated the process for the whole KB. Each KB that is processed results in a working system that is able to solve the corresponding configuration task (and not only for elevators). This is in contrast to earlier work, which produced abstract formulations of the problem-solving methods but which were unable to perform reuse of actual KBs. We subsequently used the ECLiPSe solver on some more demanding vertical transport configuration tasks. We found that we had to use a little-known propagation technique described by Le Provost and Wallace (1991). Further, our techniques did not use any heuristic “fix”’ information, yet we successfully dealt with a “thrashing” problem that had been a key issue in the original vertical transit work. Consequently, we believe we have developed a widely usable approach for solving this class of parametric design problem, by applying novel constraint-based problem solvers to data and formulae stored in existing KBs.

The subject of this paper is design intentionality. The paper is concerned with the property of the mind to hold intentional states (its capacity to represent or reflect existing and nonexisting realities) and with the way that these mental states are constructed during design tasks. The aim is to develop a mathematical theory of design intentionality, capturing the structures and processes that characterize an intentional system with the mental ability to address design tasks. The philosophical notion of intentionality is approached methodologically from a complexity theoretic perspective. More specifically, the focus is placed on the mathematical characterization of the organizational complexity of intentional states and the type of phase transitions that occur on the mental states of an intentional system during design tasks. The paper uses category theory in order to build a framework that is able to mathematically capture the meaning of these notions.

Neurotechnology has been applied to gain insights on creativity-related cognitive factors. Prior research has identified relations between cognitive factors and creativity qualitatively; while quantitative relations, such as the relative importance of cognitive factors and creativity, have not been fully determined. Therefore, taking the creative design process as an example, this study using electroencephalography (EEG) aims to objectively identify how creativity-related cognitive factors of retrieval, recall, association, and combination contribute to creativity. The theoretical basis for an EEG-based decoding method to objectively identify which cognitive factors occur in a creative process is developed. Thirty participants were recruited for a practical study to verify the reliability of the decoding method. Based on the methodology, relationships between the relative importance level of the cognitive factor and creative output quality levels were detected. Results indicated that the occurrence of recall and association are reported with a high reliability level by the decoding method. The results also indicated that association is the dominant cognitive factor for higher creative output quality levels. Recall is the dominant cognitive factor for lower creative output quality levels.

This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1996 Conference on Artificial Intelligence in Design, AID'96 (Gero & Sudweeks, 1996), the third of such workshops, with the previous two being held at AID'92 (Gero, 1992) and at AID'94 (Gero & Sudweeks, 1994). The first two workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996).

Architectural spatial design is a wicked problem that can have a multitude of solutions for any given brief. The information needed to resolve architectural design problems is often not readily available during the early conceptual stages, requiring proposals to be evaluated only after an initial solution is reached. This “solution-driven” design approach focuses on the generation of designs as a means to explore the solution space. Generative design can be achieved computationally through parametric and algorithmic processes. However, utilizing a large repertoire of organiational patterns and design precedent knowledge together with the precise criteria of spatial evaluation can present design challenges even to an experienced architect. In the implementation of a parametric design process lies an opportunity to supplement the designer's knowledge with computational decision support that provides real-time spatial feedback during conceptual design. This paper presents an approach based on a generative multiperformance framework, configured for generating and optimizing architectural designs based on a precedent design. The system is constructed using a parametric modeling environment enabling the capture of precedent designs, extraction of spatial analytics, and demonstration of how populations can be used to drive the generation and optimization of alternate spatial solutions. A pilot study implementing the complete workflow of the system is used to illustrate the benefits of coupling parametric modeling with structured precedent analysis and design generation.