One of the fundamental elements that determines the precision of coordinate measuring machines (CMMs) is the probe, which locates measuring points within measurement volume. In this paper genetically generated fuzzy knowledge based models of three-dimensional (3-D) probing accuracy for one- and two-stage touch trigger probes are proposed. The fuzzy models are automatically generated using a dedicated genetic algorithm developed by the authors. The algorithm uses hybrid coding, binary for the rule base and real for the database. This hybrid coding, used with a set of specialized operators of reproduction, proved to be an effective learning environment in this case. Data collection of the measured objects' coordinates was carried out using a special setup for probe testing. The authors used a novel method that applies a low-force high-resolution displacement transducer for probe error examination in 3-D space outside the CMM measurement. The genetically generated fuzzy models are constructed for both one stage (TP6) and two stage (TP200) types of probes. First, the optimal number of settings is defined using an analysis of the influence of fuzzy rules on TP6 accuracy. Then, once the number of settings is obtained, near optimal fuzzy knowledge bases are generated for both TP6 and TP200 triggering probes, followed by analysis of the finalized fuzzy rules bases for knowledge extraction about the relationships between physical setup values and error levels of the probes. The number of fuzzy sets on each premise leads to the number of physical setups needed to get satisfactory error profiles, whereas the fuzzy rules base adds to the knowledge linking the design experiment parameters to the pretravel error of CMM machines. Satisfactory fuzzy logic equivalents of the 3-D error profiles were obtained for both TP6 and TP200 with root mean squsre errors ranging from 0.00 mm to a maximum of 0.58 mm.

This paper interprets the concept of biologically inspired design as understanding design based on the biological evidence. Borrowing its concept of design competence from Chomsky's definition of linguistic competence, the paper reviews biological evidence from fields including evolution, genetics, and animal behavior from the perspective of design research to propose that design competence is the product of an evolutionary history during which five key developments in cognitive evolution came together: conception unbounded by sensory perception, symbolic manipulation at a level of metarepresentation, theory of mind, curiosity, and mental time travel. These cognitive capabilities were derived from the biological evidence based upon the criteria that they are presumed to be unique to humans (Homo sapiens), they may be lost because of neurodegenerative diseases or they may fail to develop because of neurodevelopmental disorders, and they are not immediately present upon birth and develop as a child's brain matures. Based on these five capabilities, the paper concludes by discussing how computation may provide a useful way to understand the origins and evolution of design competence.

This paper claims that style, in addition to being identified by common visible physical characteristics of form, can be thought of in terms of a set of common abstract characteristics. A prototype computational design support tool is described that explores this idea in the domain of architecture. The Architect's Collaborator (TAC) supports articulation and evaluation of abstract characteristics of style (e.g., experiential characteristics such as privacy and shelter) and does so by mapping abstract characteristics to details of physical form. The implementation of TAC is described and successful experiments are reported in which abstract characteristics of Frank Lloyd Wright's Prairie houses were mapped to physical form characteristics and used to evaluate Prairie and non-Prairie houses.

Why has integration and interoperability of design and manufacturing knowledge proven so difficult? There is a common puzzle that appears in your Sunday newspaper or The New Yorker magazine.

Detecting and recognizing objects is one of the most important uses of vision systems in nature and is consequently highly evolved. This paper aims to accurately detect an object using its shape and color information from a complex background. In particular, we evaluated our algorithm to detect 19 different integrated circuits (IC) from 10 different printed circuit boards (PCB) of different colors. We have compared three different shape descriptors for four different color space models. We have evaluated shape detection algorithms in different lighting conditions (indoor, outdoor, and controlled light source) to find suitable illumination for image acquisition. We undertook statistical hypothesis testing to find the effect of color space models and shape descriptors on the accuracy, false positive and false negative rates. While measuring accuracy, we have noted that L*a*b* color space is significantly worse, and the best result is obtained in YCbCr color space using bounding box shape descriptors for 2500 Lux using LED.

Manufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rule-definition or semantically invalid expression, standard NLP techniques cannot selectively derive precise information for the extraction of the desired formal manufacturing rule. To address the gap, we developed the feedback generation method based on Constraint-based Modeling (CBM) coupled with NLP and domain ontology, designed to support formal manufacturing rule extraction. Specifically, the developed method identifies the necessity of input text validation based on the predefined constraints and provides the relevant feedback to help the user modify the input text, so that the desired rule can be extracted. We proved the feasibility of the method by extending the previously implemented formal rule extraction framework. The effectiveness of the method is demonstrated by enabling the extraction of correct manufacturing rules from all the cases that need input text validation, about 30% of the dataset, after modifying the input text based on the feedback. We expect the feedback generation method will contribute to the adoption of semantics-based technology in the manufacturing field, by facilitating precise knowledge acquisition from manufacturing-related documents in a practical scenario.

In the field of content generation by machine, the state-of-the-art text-to-image model, DALL⋅E, has advanced and diverse capacities for the combinational image generation with specific textual prompts. The images generated by DALL⋅E seem to exhibit an appreciable level of combinational creativity close to that of humans in terms of visualizing a combinational idea. Although there are several common metrics which can be applied to assess the quality of the images generated by generative models, such as IS, FID, GIQA, and CLIP, it is unclear whether these metrics are equally applicable to assessing images containing combinational creativity. In this study, we collected the generated image data from machine (DALL⋅E) and human designers, respectively. The results of group ranking in the Consensual Assessment Technique (CAT) and the Turing Test (TT) were used as the benchmarks to assess the combinational creativity. Considering the metrics’ mathematical principles and different starting points in evaluating image quality, we introduced coincident rate (CR) and average rank variation (ARV) which are two comparable spaces. An experiment to calculate the consistency of group ranking of each metric by comparing the benchmarks then was conducted. By comparing the consistency results of CR and ARV on group ranking, we summarized the applicability of the existing evaluation metrics in assessing generative images containing combinational creativity. In the four metrics, GIQA performed the closest consistency to the CAT and TT. It shows the potential as an automated assessment for images containing combinational creativity, which can be used to evaluate the images containing combinational creativity in the relevant task of design and engineering such as conceptual sketch, digital design image, and prototyping image.

I discuss how the capabilities of the Edinburgh Designer System can be extended and used to support symbolic computation for robotics. I conclude that the Algebra Engine requires to handle temporal constructs, groups and tolerances, that the taxonomy can support activity modules and that automatic plan formation would require the creation of a specialist.

The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

Design is a decision-making process for which knowledge is a prerequisite. Most decisions are taken at the conceptual stage and have pronounced influence on the final design. The literature, therefore, recommends the incorporation of sustainability criteria, such as environment, at this stage. Difficulty in performing life cycle assessment (LCA) due to low availability of information at the conceptual stage for evaluation and highly abstract nature of solutions, inadequate incorporation of DfE (Design for Environment) guidelines and LCA reports into the design process, and a lack of effective communication of the same to the designers for prompt decision-making are major motivations for the development of a support. This paper discusses a “conceptual Tool for environmentally benign design” – concepTe – that supports designers in decision-making during the conceptual design stage, by offering environmental impact (EI) estimates of abstract solutions with associated uncertainty, for evaluation and selection of the most environmentally benign solution as concept. The EI estimates are calculated by a module in the tool based on a proposed EI estimation method, which requires the support of a knowledge base to fetch appropriate LCA information corresponding to the design element being conceptualized. This knowledge base is grounded in the domain-agnostic SAPPhIRE model ontology, allows semantic operability of the knowledge, and offers the results to the designers in a familiar domain language to aid decision-making. A “proof of concept” of the tool is developed for application in design of building in the AEC (Architectural design, Engineering, and Construction) domain. Further, empirical studies are conducted to evaluate the effectiveness of the “proof of concept” to support decision-making and results are found favorable. The paper also discusses the future scope for further development of the tool into a holistic design decision-making platform.

In reality, the behavior of processes is sometimes vague and the observed data is irregular. This research proposes an approach for optimizing fuzzy multiple responses using fuzzy regression. In this approach, each response repetition is transformed into a signal to noise ratio then modeled using statistical multiple regression. A trapezoidal fuzzy regression model is formulated for each response utilizing the statistical regression coefficients. The most desirable response values and the deviation function are determined for each response. Finally, four optimization models are formulated for the trapezoidal membership fuzzy number to obtain the optimal factor level at each number. Two case studies are adopted for illustration, where excluding response fuzziness will result in misleading optimal factor settings if solved by the traditional optimization techniques. In conclusion, the proposed approach based on fuzzy regression approach can successfully optimize fuzzy multiple responses in a wide range of manufacturing applications on the Taguchi's method. Moreover, compared to other approaches, such as data envelopment analysis and grey relational analysis, the proposed approach has the distinct advantage of being able to generate models using only a small number of experimental data sets and minimizing inherent variations.

A computational approach for the design of self-organizing systems is proposed that employs a genetic algorithm to efficiently explore the vast space of possible configurations of a given system description. To generate the description of the system, a two-field based model is proposed in which agents are assigned parameterized responses to two “fields,” a task field encompassing environmental features and task objects, and a social field arising from agent interactions. The aggregate effect of these two fields, sensed by agents individually, governs the behavior of each agent, while the system-level behavior emerges from the actions of and interactions among the agents. Task requirements together with performance preferences are used to compose system fitness functions for evolving functional and efficient self-organizing mechanisms. Case studies on the evolutionary synthesis of self-organizing systems are presented and discussed. These case studies focus on achieving system-level behavior with minimal explicit coordination among agents. Agents were able to collectively display flocking, exploration, and foraging through self-organization. The proposed two-field model was able to capture important features of self-organizing systems, and the genetic algorithm was able to generate self-organizing mechanisms by which agents could form task-based structures to fulfill functional requirements.

In order to respond to the difficulties encountered by CAD software applications in really assisting the conceptual designer, we propose a tool that is capable of interpreting design sketches and feeding data to various project evaluators, right from the early phases in the design process. For that purpose, we use the concept of the absent interface, which is the only interface that is compatible with the cognitive process involved in sketching. In this paper, we present the principles of such an interface, illustrated by EsQUIsE, a software prototype for capturing and interpreting architectural sketches, which has been under development for several years.

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

In this paper we are interested in the decision problem faced by an agent when requesting bids for collections of tasks with complex time constraints and interdependencies. In particular, we study the problem of specifying an appropriate schedule for the tasks in the request for bids. We expect bids to require resource commitments, so we expect different settings of time windows to solicit different bids and different costs. The agent is interested in soliciting “desirable” bids, where desirable means bids that can be feasibly combined in a low-cost combination that covers the entire collection of tasks. Since the request for bids has to be issued before the agent can see any bids, in this decision process there is a probability of loss as well as a probability of gain. This requires the decision process to deal with the risk posture of the person or organization on whose behalf the agent is acting. We describe a model based on Expected Utility Theory and show how an agent can attempt to maximize its profits while managing its financial risk exposure. We illustrate the operation and properties of the model and discuss what assumptions are required for its successful integration in multiagent contracting applications.

One challenge in building collaborative design tools that use speech and sketch input is distinguishing gesture pen strokes from those representing device structure, that is, object strokes. In previous work, we developed a gesture/object classifier that uses features computed from the pen strokes and the speech aligned with them. Experiments indicated that the speech features were the most important for distinguishing gestures, thus indicating the critical importance of the speech–sketch alignment. Consequently, we have developed a new alignment technique that employs a two-step process: the speech is first explicitly segmented (primarily into clauses), then the segments are aligned with the pen strokes. Our speech segmentation step is unique in that it uses sketch features for locating segment boundaries in multimodal dialog. In addition, it uses a single classifier to directly combine word-based, prosodic (pause), and sketch-based features. In the second step, segments are initially aligned with strokes based on temporal correlation, and then classifiers are used to detect and correct two common alignment errors. Our two-step technique has proven to be substantially more accurate at alignment than the existing technique that lacked explicit segmentation. It is more important that, for nearly all cases, our new technique results in greater gesture classification accuracy than the existing technique, and performed nearly as well as the benchmark manual speech–sketch alignment.

Within the engineering design community there is support for further research into the development of improved approaches to design management. Such research has lead to coordination being identified as an important and pervasive characteristic of many existing approaches (e.g., concurrent engineering and work-flow management). In this article, operational design coordination is proposed as the basis for an improved approach. This article also presents a novel integrated approach that incorporates the key elements of operational design coordination: coherence, communication, task management, resource management, schedule management, and real-time support. Through unifying these key elements, this approach provides an integrated means of managing design in a controlled and harmonious fashion. The approach also provides knowledge of the constituent techniques involved in operational design coordination, the interrelationships and dynamic interactions between them, and the knowledge used and maintained within and between them. The approach has been realized within an agent-oriented system called the Design Coordination System, which provides a systematic means of simultaneously coordinating operational management tasks and technical design tasks. To evaluate the approach, the system has been applied to an industrial case study involving the computational process of turbine blade design. This application has been shown to enable the structured undertaking of interrelated tasks by allocating and using resources of varying performance efficiency in an optimized fashion in accordance with dynamically derived schedules in a coherent, appropriate, and timely manner. This is achieved by managing tasks, their dependencies, and the information required to undertake them. In addition, the approach enables and sustains the continuous optimized use of resources by monitoring, forecasting, and disseminating resource performance efficiency. The approach facilitates dynamic scheduling and the subsequent enactment of the resulting schedules. Decision making for rescheduling is also incorporated within the approach such that it is only performed as and when appropriate. If rescheduling is performed, it is done so in parallel with task enactment such that resources continue to be utilized in an optimized manner.

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

The external formation of integrated circuits based on lithographic processes is not the only possible method for manufacturing electron devices, either integrated circuits or photovoltaic cells. Planar technology, based on external formation, requires a certain sequence of interactions between the structured medium and the object being formed, in which the configuration of the region to be formed and its position are defined by the structure of the media and their alignment with the object. By contrast, in self-formation the interaction between an object to be formed and its chaotic medium is controlled by the object structure itself, and it is this interaction that changes the object's structure, leading to its increasing complexity. Three kinds of self-formation can be used in manufacturing: self-formation, based on a certain sequential interaction between an object and chaotic media; development, which requires only a single chaotic medium; and reproduction, which involves a developing object generating primary objects. All these cases represent self-formation of artificial systems. Simpler patterns of self-formation have already led to various patents and technologies, while more complex ones may be applied in the future. The following article attempts to define the necessary and sufficient conditions for self-formation.