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.

This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.

This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include two potentially significant observations. First, the coevolution process occurs frequently within the design knowledge level (i.e., when using Rhino) and within the rule algorithm level (i.e., when using Grasshopper) of the parametric design process. Second, the designers’ coevolution process was focused on the design knowledge level at the beginning of the design session, while they focused more on the rule algorithm level toward the end of the design session. These results support an improved understanding of the design process that occurs in PDEs.

A genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem.

The standard penetration test (SPT) is the most common test conducted in the field, and it is used to determine in situ properties of different soils. Although it is a matter of debate, these tests are also used for the determination of the consistency of fine-grained soils, whereby the test results can also be utilized to establish numerous empirical correlations to predict the strength of soils in the field. In this study, unsupervised clustering algorithms were employed to classify the SPT standard penetration resistance value (SPT-N) in the field. In this scope, shear strength and liquidity index parameters were used to classify the SPT-N values by taking the classification system of Terzaghi and Peck (1967) into consideration. The results showed that the input parameters were successful for classifying the SPT-N value to an acceptable degree of strength attribute. Therefore, in cases where the SPT tests are unreliable or could not be performed, laboratory tests on undisturbed specimens can give valuable information regarding the consistency and SPT-N value of the soil specimen under investigation. Data in this study is based on several tests that were conducted in a region; nevertheless, it is advised that the results of this study should be evaluated using global data.

The use of analytics has been emerging as a way to better understand the complex dynamics and resulting trends that occur when social and technical systems intersect. For instance, web analytics studies the intersection between society and the Internet to better understand use patterns and preferences. Business analytics studies the interfaces between human capital and technical systems in the context of corporate management and industrial production. Engineering design is ripe with such sociotechnical systems where consumers and engineered systems intersect producing a complex sociotechnical system marked by difficult to predict behavior and trends. In this paper, the paradigm of design analytics is further developed and used to study a diverse sociotechnical product. The product is a refrigerator that is hypothetically equipped with sensors and feedback mechanisms, and a simulator models the interactions of a population of 1000 users. Analyzing this sociotechnical product using design analytics demonstrates the ability to extract valuable insights at the juncture of people and technology. Insights into the leading behaviors of different populations are presented, and it is shown that design analytics and the associated tools can provide a platform for designers to develop better performing products that meet both explicit and implicit customer needs.

When considering a system that performs a role, it is often stated that performing that role is a function of the system. The general form of such statements is that “the function of S is R,” where S is the functioning system and R is the functional role it plays. However, such statements do not represent how that single function was selected from many possible alternatives. This article renders those alternatives explicit by revealing the other possible function statements that might be made when either S or R is being considered. In particular, two forms of selection are emphasized. First, when we say “the function of S is R,” there are typically many systems other than S that are required to be in operation for that role to be fulfilled. The functioning system, S, does not perform the role, R, all by itself, and those systems that support S in performing that role might also have been considered as functioning. Second, when we say, “the function of S is R,” there are typically many other roles that S plays apart from R, and those other roles might also have been considered functional. When we make function assignments, we select both the functioning system, S, and the functional role, R, from a range of alternatives. To emphasize these alternatives, this article develops a diagrammatic representation of multiple systems playing multiple roles in multiple supersystems.

Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Nonlinear dynamical system and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.

How do humans judge the creativeness of an artwork or other artifact? This article suggests that such judgments are based on the pleasures of an aesthetic experience, which can be modeled as a mathematical product of psychological arousal and appraisal. The arousal stems from surprise, and is computed as a marginal entropy using information theory. The appraisal assigns meaning, by which the surprise is resolved, and is computed as a posterior probability using Bayesian theory. This model is tested by obtaining human ratings of surprise, meaning, and creativeness for artifacts in a domain of advertising design. The empirical results show that humans do judge creativeness as a product of surprise and meaning, consistent with the computational model of arousal and appraisal. Implications of the model are discussed with respect to advancing artificial intelligence in the arts as well as improving the computational evaluation of creativity in engineering and design.