Early research in artificial intelligence (AI) in design was “AI in the large.” Artificial intelligence promised a revolutionary approach to assisting or automating design processes, separate from other computational systems. As the field matures, “AI in the small” has the most potential. Rather than seeing large AI programs, we see the results of research in AI in design providing the glue that holds larger systems together using reasoning systems that represent or manage processes, information, and interaction devices that use conventional procedural programming; effectively blurring the boundaries between AI and non-AI. Alternatively, we see research in AI in design as playing a role in blurring the boundaries between the physical and the virtual.

As a measure to manage the climate impact of aviation, significant enhancements to aviation technologies and operations are necessary. When assessing these enhancements and their respective impacts on the climate, it is important that we also quantify the associated uncertainties. This is important to support an effective decision and policymaking process. However, such quantification of uncertainty is challenging, especially in a complex system that comprises multiple interacting components. The uncertainty quantification task can quickly become computationally intractable and cumbersome for one individual or group to manage. Recognizing the challenge of quantifying uncertainty in multicomponent systems, we utilize a divide-and-conquer approach, inspired by the decomposition-based approaches used in multidisciplinary analysis and optimization. Specifically, we perform uncertainty analysis and global sensitivity analysis of our multicomponent aviation system in a decomposition-based manner. In this work, we demonstrate how to handle a high-dimensional multicomponent interface using sensitivity-based dimension reduction and a novel importance sampling method. Our results demonstrate that the decomposition-based uncertainty quantification approach can effectively quantify the uncertainty of a feed-forward multicomponent system for which the component models are housed in different locations and owned by different groups.

While previous work in automated process planning established plan ordering on an empirical basis alone, we derive our process plans based on the Holding-Under-Uncertainty Principle. We will introduce the principle, and we will describe the operational requirements needed to make this principle implementable in practice. The principle takes into account the form and geometric tolerances needed to locate features in deriving plan steps. Rather than just focusing on technological features, our planning strategy is controlled by the geometric relationships among features. By implementing a constraint propagation paradigm, we ensure that the tolerances accumulated in generating the part geometry remain within the tolerances specified by the design.

Modular product design is an efficient strategy to let manufacturing companies meet the customers’ requirements by offering a wide variety and customization of products and significantly saving time and cost during engineering and production (Fei et al., 2011). Despite numerous approaches for function modeling and modular product design (Srinivasan et al., 2012; Eckert, 2013; Vermaas, 2013) that have been developed in the last decades, carrying out an efficient product variants’ design process is still an open issue for many manufacturing companies. The proposed approaches offer numerous ways to model information about product functionality, but each approach is useful and particularly well suited for different applications and domains (Summers et al., 2013). The present research compares the existing approaches for product variants design and defines a function-based model to support product design and redesign according to a modular framework, merging qualitative technical issues with business-oriented evaluation. Such a framework has been used to develop a multiuser IT platform, composed of a knowledge-based engine and four different tools to support designers and engineers in product variants creation, management, and configuration, from product functional modeling to cost estimation and life cycle assessment. The proposed model has been tested on industrial cases in the context of household appliances. Experimental results demonstrates that, after a preliminary context analysis and a proper knowledge base creation, such a model supports a more conscious decision-making and promote collaboration within an interdisciplinary design team. Finally, the case study shows the necessity, but in the meanwhile the insufficiency, of a functional decomposition as the only representation viewpoint.

Numerical simulation of partial differential equations (PDEs) plays a crucial role in predicting the behavior-of physical systems and in modern engineering design. However, to produce reliable results with a PDE simulator, a human expert must typically expend considerable time and effort in setting up the simulation. Most of this effort is spent in generating the grid, the discretization of the spatial domain that the PDE simulator requires as input. To properly design a grid, the gridder must not only consider the characteristics of the spatial domain, but also the physics of the situation and the peculiarities of the numerical simulator. This article describes an intelligent gridder that is capable of analyzing the topology of the spatial domain and of predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically, gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains with a wide range of configurations.

Design knowledge is continually refined and expanded through experience. This research is concerned with design knowledge expressed as constraints. A simple learning mechanism simulates an expert designer's ability to incrementally adjust her knowledge when presented with slightly new problems. In response to unsatisfied expectations during the design process the system will examine its general knowledge about the design artifact, discover some relevant constraining knowledge, and convert that knowledge into a design constraint for future use. This process, referred to as constraint inheritance, should automatically improve the problem-solving performance.

An expert system for the preliminary design of ships developed. The design process is understood as determining design variables and the relationships among design parameters. The relationships among the elements of design knowledge are represented by a network model. The object-oriented knowledge representations are introduced in the computer system to manipulate design variables such as the principle particulars of a ship in the network model. The expert system based on the above concept provides the following functions: (1) flexibility for building a model and easy modification of the model; (2) effective diagnosis of the design process by using rule-based knowledge representations; (3) hybrid function with both symbolic treatment of the design knowledge and numerical computations by coupling the systems programmed in LISP and FORTRAN and (4) a supporting method for determination of the design variables.

Finally the system's validity and effectiveness is ascertained by applying it to the preliminary design of a bulk carrier ship.

Knowledge representation (KR) is an important area in artificial intelligence (AI) and is often related to specific domains. The representation of knowledge in domain-specific contexts makes it desirable to capture semantics as domain experts would. This motivates the development of semantics-preserving standards for KR within the given domain. In addition to the storage and analysis of information using such standards, the effect of globalization today necessitates the publishing of information on the Web. Thus, it is advisable to use formats that make the information easily publishable and accessible while developing KR standards. In this article, we propose such a standard called Quenching Markup Language (QuenchML). This follows the syntax of the eXtensible Markup Language and captures the semantics of the quenching domain within the heat treating of materials. We describe the development of QuenchML, a multidisciplinary effort spanning the realms of AI, database management, and materials science, considering various aspects such as ontology, data modeling, and domain-specific constraints. We also explain the usefulness of QuenchML in semantics-preserving information retrieval and in text mining guided by domain knowledge. Furthermore, we outline the significance of this work in software tools within the field of AI.

Physical systems can be modelled at many levels of approximation. The right model depends on the problem to be solved. In many cases, a combination of models will be more effective than a single model. Our research investigates this idea in the context of engineering design optimization. We present a family of strategies that use multiple models for unconstrained optimization of engineering designs. The strategies are useful when multiple approximations of an objective function can be implemented by compositional modelling techniques. We show how a compositional modelling library can be used to construct a variety of locally calibratable approximation schemes that can be incorporated into the optimization strategies. We analyze the optimization strategies and approximation schemes to formulate and prove sufficient conditions for correctness and convergence. We also report experimental tests of our methods in the domain of sailing yacht design. Our results demonstrate dramatic reductions in the CPU time required for optimization, on the problems we tested, with no significant loss in design quality.

Studies on the application of AI techniques to engineering design, analysis, and manufacturing (AI EDAM) problems have been expanding steadily over the past decade. These studies have led to the founding of many new journals and the initiation of series of conferences. If all these research efforts were successful, some of us might have been unemployed but living happily off the royalties from our successful research products that were deployed in practice.

During a crisis situation, the ability of emergency department to take reliable and quick decisions is the main feature that defines the success or failure of this organization in the course of its crisis management. Decision makers spend time on identifying the decisions that will be taken for the whole of the crisis management, and on anticipating the preparation of these decisions, ensuring that they have time to properly prepare all decisions to be taken and, be able to implement them as fast as possible. However, the context and the characteristics of the crisis make the decision process complicated because there is no specific methodology to anticipate these decisions and properly manage collaboration with the other protagonists. There is also the pressure of time, a significant stress and, the emotional impact on the decision maker that lead to losing objectivity in decision making. We understand so that the right decision will be greatly facilitated and enhanced by the development of an adequate tool and process for decision-making. This tool must respect methods of the emergency department considered, and highlight the importance of experience feedback referencing to past cases, especially success and failures. We propose in this paper, software in order to handle experience feedback as a support for decision-making in crisis management “Crisis Clever System”. Several dimensions are considered in this study, from one side: organization, communication and problem-solving activities and from the other side the presentation and finding of experience feedback thanks to an analogy technique.

This paper reports on a computational model developed to study the effects of various modes of social learning on task coordination in teams through the mapping of distributed team competence, a significant aspect of efficient teamwork. The computational model emphasizes and operationalizes distinct modes of social learning, differentiated in terms of socialization opportunities. Simulation results demonstrate that computational models based on fundamental principles of social learning provide a robust approach to study task coordination in teams and can be used to explore ways to organize opportunities for social learning depending upon member retention, team structure, and the complexity of the design task.

The presence of aerodynamics loadings makes the design of some classes of elastic structures, as, for instance, marine structures and risers, very challenging. Moreover, capturing the complex physical interaction between the structure and the fluid is challenging for both theoretical and numerical models. One of the most important phenomena that appear in these situations is vortex-induced vibrations. The picture is even more complicated when multiple elastic elements are close enough to interact by modifying the fluid flow pattern. In the present work, we show how the common design practice for these structures, which is entirely based on deterministic simulations, needs to be complemented by the uncertainty quantification analysis. The model problem is a structure constituted by two elastically mounted cylinders exposed to a two-dimensional uniform flow at Reynolds number 200. The presence of a manufacturing tolerance in the relative position of the two cylinders, which we consider to be a source of uncertainty, is addressed. The overall numerical procedure is based on a Navier–Stokes immersed boundary solver that uses a flexible moving least squares approach to compute the aerodynamics loadings on the structure, whereas the uncertainty quantification propagation is obtained by means of a nonintrusive polynomial chaos technique. A range of reduced velocities is considered, and the quantification, in a probabilistic sense, of the difference in the performances of this structure with respect to the case of an isolated cylinder is provided. The numerical investigation is also complemented by a global sensitivity analysis based on the analysis of variance.

A selective, multiple-criteria method is proposed for handling constraint violations in well-defined design problems. First, the types of design problems upon which the method may apply are presented and a graph topology is adopted for representing the problem decomposition in the level of design parameter interrelations (design parameter graphs). Next, the problem of constraint violations for the design parameters is discussed. It is shown that these violations can be resolved by modifying the values of the primary design parameters and recalculating the values of the violated parameters. Any “blind” attempt of modifying the values of the primary design parameters for resolving occurring violations may, eventually, create additional violations. The proposed method guides the designer toward those primary design parameters that present the least possibility of creating more violations when their values are modified. This is achieved by applying multiple criteria and by producing a final, sorted list of primary design parameters. The designer may then choose the first element of this list to handle efficiently the violations of the design parameters. Two examples are given on a design space where constraint violations occur. Through these examples, the capability of the proposed method in helping the designer to handle constraint violation is shown. The concluding remarks, except for summarizing the potential of the method, determine its boundaries and include a reference on relative work currently under investigation.

The paper by Woodbury and Burrow is examined using four criteria: completeness, discrimination, alternative approaches, and combining exploration in different problem domains. Although the paper covers significant aspects of the search space literature it leaves out some relevant aspects of the cognitive approach. It fares much better in terms of discriminating important concepts and alternative approaches to the modeling of the design search space. The structure–function–behavior model is suggested as an analogy for the central parameters of the search space paradigm. The Woodbury and Burrow paper reveals more than what has been accomplished up to now in the design search space area, but its task still remains incomplete.

Requirement planning is one of the most critical tasks in the product development process. Despite its significant impact on the outcomes of the design process, engineering requirement planning is often conducted in an ad hoc manner without much structure. In particular, the requirement planning phase suffers from a lack of quantifiable measures for evaluating the quality of the generated requirements and also a lack of structure and formality in representing engineering requirements. The main objective of this research is to develop a formal Web Ontology Language ontology for standard representation of engineering requirements. The proposed ontology uses explicit semantics that makes the ontology amenable to automated reasoning. To demonstrate how the proposed ontology can support requirement analysis and evaluation in engineering design, three possible services enabled by the ontology are introduced in this paper. These services are information content measurement, specificity and completeness analysis, and requirement classification. The proposed ontology and its associated algorithms and tools are validated experimentally in this work.

The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of vehicles, in a simulated, augmented, or real environment. Such a framework brings forward the need for formal languages in which to specify the vehicles that compose a team, the scenario in which they will operate, and the mission to be performed. This paper introduces the Scenario Description Language (SDL) and the Team Description Language (TDL), two Extensible Markup Language based dialects that compose the static components necessary for representing scenario and mission knowledge. SDL provides a specification of physical scenario and global operational constraints, while TDL defines the team of vehicles, as well as team-specific operational restrictions. The dialects were defined using Extensible Markup Language schemas, with all required information being integrated in the definitions. An interface was developed and incorporated into the framework, allowing for the creation and edition of SDL and TDL files. Once the information is specified, it can be used in the framework, thus facilitating environment and team specification and deployment. A survey answered by practitioners and researchers shows that the satisfaction with SDL+TDL is elevated (the overall evaluation of SDL+TDL achieved a score of 4 out of 5, with 81%/78.6% of the answers ≥4); in addition, the usability of the interface was evaluated, achieving a score of 86.7 in the System Usability Scale survey. These results imply that SDL+TDL is flexible enough to represent scenarios and teams, through a user-friendly interface.

Technical function is a key concept in engineering design. Despite the centrality of the concept, a systematic, rigorous analysis of the utility of function and its different conceptualizations is missing in the engineering design literature. This paper addresses this challenge. We investigate the utility of function and its different meanings in the following engineering design contexts: malfunction explanation, innovative design, redesign, and routine design. This analysis provides theoretical justification for the current engineering practice of accepting ambiguity of functional descriptions and for methods to translate and/or convert functional descriptions across engineering design frameworks. We show that the utility of specific meanings of function is highly task dependent, identify novel roles for functional descriptions in engineering design, and present methodological implications for translation methods for functional descriptions.

Question–answering (QA) systems have proven to be helpful, especially to those who feel uncomfortable entering keywords, sometimes extended with search symbols such as +, *, and so forth. In developing such systems, the main focus has been on the enhanced retrieval performance of searches, and recent trends in QA systems center on the extraction of exact answers. However, when their usability was evaluated, some users indicated that they found it difficult to accept the answers because of the absence of supporting context and rationale. Current approaches to address this problem include providing answers with linking paragraphs or with summarizing extensions. Both methods are believed to be sufficient to answer questions seeking the names of objects or quantities that have only a single answer. However, neither method addresses the situation when an answer requires the comparison and integration of information appearing in multiple documents or in several places in a single document. This paper argues that coherent answer generation is crucial for such questions, and that the key to this coherence is to analyze texts to a level beyond sentence annotations. To demonstrate this idea, a prototype has been developed based on rhetorical structure theory, and a preliminary evaluation has been carried out. The evaluation indicates that users prefer to see the extended answers that can be generated using such semantic annotations, provided that additional context and rationale information are made available.