Logic-based models are thriving within artificial intelligence. A great number of new logics have been defined, and their theory investigated. Epistemic logics introduce modal operators for knowledge or belief; deontic logics are about norms, and introduce operators of deontic necessity and possibility (i.e., obligation or prohibition). And then we have a much investigated class—temporal logics—to whose application to engineering this special issue is devoted. This kind of formalism deserves increased widespread recognition and application in engineering, a domain where other kinds of temporal models (e.g., Petri nets) are by now a fairly standard part of the modelling toolbox.

To obtain optimal design efficiently in the initial design stage of a ship, a hybrid system is developed by integrating the optimization algorithm and knowledge-based system. The hybrid system can manipulate numeric and symbolic data simultaneously. To increase search efficiency in a design space, the optimization algorithm (optimizer) is implemented by coupling a genetic algorithm (GA) and search method. The optimizer determines a candidate region around the optimum point by using the GA, then searches the optimum point by the search method concentrating in this region, thus reducing calculation time and increasing search efficiency. To generate input data for the optimizer, a rule-based system is developed. Some domain knowledge for ship optimization in the initial design stage is retrieved from a database of existing ship and design experts. The obtained knowledge is stored in the knowledge base. The optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the design variables. Therefore, search speed and the capability of finding an optimum point will be increased in comparison with conventional approach. The developed system is applied principally to particulars of optimization of ships with multicriteria. Through application ship design, it shows that the hybrid system can be a useful tool for optimum design.

This issue of AIEDAM is the second in a series of three “mini” special issues on Evolutionary Design by computers. The papers continue the theme that began in Vol. 13, No. 3, 1999, of using Evolutionary Computation for design problems. The first paper by Eby, Averill, Punch and Goodman provides an excellent overview of the most recent work at Michigan State University on this subject. They describe their work on the optimization of flywheels by an injection island genetic algorithm, and show the importance of minimizing the computation time devoted to evaluation for such real-world applications.

Generative research in design investigates compact representations that facilitate the rapid exploration of large design spaces. It includes among other things: rule-based systems, feature structures, and genetic algorithms. Generative systems are being applied to a variety of design problems from various disciplines including engineering, product design, and architecture. The objective is to further our understanding of the design operations and manipulations to create systems that better support the designers' activity.

Throughout the natural world and our human-designed world, design and evolution seem to go hand-in-hand. Some of the most astonishing and complex designs known to humankind—the embryogeny process, the immune system, the brain, the very structure of DNA—are products of natural evolution, not human endeavor. In addition, the progress of our own designs seems evolutionary, as the best concepts from existing designs are combined with some small variation to produce the next generation of cars, computers, and indeed, most types of human design. And now, as the papers in this and subsequent special issues will show, our computers are allowing us to harness the power of evolution directly, to aid the design process.

Applying grammatical formalisms to engineering problems requires consideration of spatial, functional, and behavioral design attributes. This paper explores structural design languages and semantics for the generation of feasible and purposeful discrete structures. In an application of shape annealing, a combination of grammatical design generation and search, to the generation of discrete structures, rule syntax, and semantics are used to model desired relations between structural form and function as well as control design generation. Explicit domain knowledge is placed within the grammar through rule and syntax formulation, resulting in the generation of only forms that make functional sense and adhere to preferred visual styles. Design interpretation, or semantics, is then used to select forms that meet functional and visual goals. The distinction between syntax used in grammar rules to explicitly drive geometric design and semantics used in design interpretation to implicitly guide geometric form is shown. Overall, the designs presented show the validity of applying a grammatical formalism to an engineering design problem and illustrate a range of possibilities for modeling functional and visual design criteria.

The paper presents a knowledge-based system (KBS) for the conceptual design of grippers for handling fabrics. Its main purpose is the integration of the domain knowledge in a single system for the systematic design of this type of grippers. The knowledge presented, in terms of gripper, material and handling process, are classified. The reasoning strategy is based upon a combination of a depth-first search method and a heuristic method. The heuristic search method finds a final solution from a given set of feasible solutions and can synthesize new solutions to accomplish the required specifications. Details of the main features of the system are given, including its ability to take critical design decisions according to four criteria, weighted by the designer. The knowledge-based system was implemented in the Kappa P. C. 2.3.2 environment. Two examples are given to illustrate some critical aspects concerning the KBS development, to explain the operation of the proposed searching heuristic method, and to show its effectiveness in producing design concepts for grippers.

A voxel-based shape representation when integrated with an evolutionary algorithm offers a number of potential advantages for shape optimization. Topology need not be predefined, geometric constraints are easily imposed and, with adequate resolution, any shape can be approximated to arbitrary accuracy. However, lack of boundary smoothness, length of chromosome, and inclusion of small holes in the final shape have been stated as problems with this representation. This paper describes two experiments performed in an attempt to address some of these problems. First, a design problem with only a small computational cost of evaluating candidate shapes was used as a testbed for designing genetic operators for this shape representation. Second, these operators were refined for a design problem using a more costly finite element evaluation. It was concluded that the voxel representation can, with careful design of genetic operators, be useful in shape optimization.

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.

This paper describes a knowledge-based temporal representation of state transitions for industrial real-time systems. To allow expression of uncertainty, we shall define fluents as disjuncts of positive/negative time-varying properties. A state of the world is represented as a collection of fluents, which is usually incomplete in the sense that neither the positive form nor the negative form of some properties can be implied from it. The world under consideration is assumed to persist in a given state until an action(s) takes place to effect a transition of it into another state, where actions may either be instantaneous or durative. High-level causal laws are characterized in terms of relationships between actions and the involved world states. An effect completion axiom is imposed on each causal law to guarantee that all the fluents that can be affected by the performance of the corresponding action are governed. This completion requirement is practical for most industrial real-time applications and in fact provides a simple and effective treatment to the so-called frame problem.

Most engineering design problems involve optimizing a number of often conflicting performance measures in the presence of multiple constraints. Traditional vector optimization techniques approach these problems by generating a set of Pareto-optimal solutions, where any specific objective can be further improved only at the cost of degrading one or more other objectives. The solutions obtained in this manner, however, are only single points within the space of all possible Pareto-optimal solutions and generally do not indicate to designers how small deviations from predicted design parameters settings affect the performance of the product or the process under study.

In this paper we introduce a new approach to robust design based on the concept of inductive learning with regression trees. Given a set of training examples relating to a multiobjective design problem, we demonstrate how a multivariate regression tree can utilize an information-theoretic measure of covariance complexity to capture optimal, tradeoff design surfaces. The novelty of generating design surfaces as opposed to traditional points in the design space is that now designers are able to easily determine how the responses of a product or process vary as design parameters change. This ability is of paramount importance in situations where design parameter settings need to be modified during the lifetime of a product/process due to various economic or operational constraints. As a result, designers will be able to select optimal ranges for design parameters such that the product's performance indices exhibit minimal or tolerable deviations from their target values. To highlight the advantages of our methodology, we present a multiobjective example that deals with optimum design of an electric discharge machining (EDM) process.

In this paper we present a specialized system for temporal reasoning representing possibly uncertain qualitative and quantitative constraints relative to temporal intervals in a homogeneous framework. The underlying logic is that of Allen, which we have extended to include quantitative temporal information. Intervals are still assumed as primitive entities while, to represent metric information, time points are introduced only at the algorithmic level. We illustrate the properties of the temporal representation, the algorithms used for constraint propagation, and their complexity. We show how the system can be used to schedule manufacturing operations in a small factory.

In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer.

A grammatical approach to product design is demonstrated. In particular, shape grammars are shown to be especially useful for products that are differentiated primarily on the basis of form yet driven by function; they allow products to be designed as a sequence of well-defined steps. However, it is not always clear how to choose the sequence of rules that should be applied to generate the final shape. In this paper we demonstrate that at each stage during the process, partial designs of the final product can be used to provide feedback to the designer based on specific design objectives and thus suggest possible rule choices. We take advantage of the shape grammar for the generation of coffeemakers introduced by Agarwal and Cagan, and associate with the grammar rules expressions that model manufacturing costs. With each application of a shape grammar rule, an understanding of the overall cost of manufacturing the product is incrementally improved. Thus, at each stage of the design process the designer has an indication of what the overall cost of the product will be and how the selection of one grammar rule over another influences the final cost. Once the complete product is generated, an appraisal of its manufacturing cost is given to the designer. This evaluation methodology helps the designer understand the implications of decisions made early on in the design process. We have also verified the accuracy of this approach through the costs of some commercially available coffeemakers, generated by this method, which are comparable to the costs for those designs listed in the literature.

This paper evaluates an evolution strategy to tune conventional proportional plus integral plus derivative (PID) and gain scheduling PID control algorithms. The approach deals with the utilization of an evolution strategy with learning acceleration by derandomized mutative step-size control using accumulated information. This technique is useful to obtain the following characteristics: (1) freedom of choice of a performance index, (2) increase of the convergence speed of evolution strategies to get a local minimum to determine controller design parameters, and (3) flexibility and robustness in the automatic design of controllers. Performance analysis and experimental results are carried out using a laboratory scale nonlinear process fan and plate. The practical prototype contains features such as nonminimum phase, dead time, resonant, and turbulent disturbance behavior that motivate the utilization of intelligent control techniques.

Generated designs are formed through rules that change, and structures that interpret, descriptions. The structure of design descriptions is examined with particular reference to generative shape descriptions acting as the infrastructure for attributes. Structure is represented by relations among the parts of descriptions. In particular, a closure structure allows relations to be described by the parts themselves. Design processes use descriptions in combination. It is shown how to combine separate descriptions in parallel and sequentially. Rules change descriptions. It is shown how to aggregate local changes consistently across a design and to use rules with structure on their component shapes to highlight selected emergent features. Further, multiple attribute descriptions, including shape and associated properties, are constructed. Their structures are represented algebraically.

Computer-aided process planning has been recognized as an important tool for coordinating the different operations involved in making the product. While temporal knowledge is central to the design of efficient and reliable process plans, little attention is given to the integration of process planning and temporal processing and reasoning. To fill the void, we propose in this paper a practical approach, which is inspired by the framework of Temporal Constraint Satisfaction Problem (TCSP), to integrate process planning and temporal reasoning. We show that a TCSP formulation is a subset of a formulation using a reified temporal logic, and discuss the advantages of using such a restricted model. To reflect more realistic process planning encountered in real manufacturing environments, we present a model, called n-TCSP, which is a generalization of the TCSP framework. We envision the proposed temporal reasoning framework as one of the modules in the evolving new intelligent computer-aided process planning.

In this paper, the design of geometrical shapes of function carriers and their layout in given space is called configuration design. The constraint satisfaction problem in configuration design may be difficult to solve due to the lack of tight constraints and the countless combinations of the layout; a diversity of solutions that satisfy the constraints should be allowed. Therefore, to allow such diversity, we directed our attention to developmental processes in biology and proposed an adaptive-growth-type 3D representation based on evolutionary algorithms. Here, the adaptive-growth-type means the shape expressed in the process, which develops through interaction with an outside environment, like shape generation of a living organism in the natural world. The usefulness of the representation was verified by applying it to the component layout problem in the early stage of satellite design.

This paper describes a generic ontology-based approach that eases the requirement analysis (RA) work. The approach enables the user to derive method-specific RA tools for different applications. The derivation process is based on a unified framework that contains a software methodology ontology and a knowledge acquisition ontology. The former contains a library of software RA methods and a set of modeling support entities, which use the library to construct methodological knowledge. The latter contains a library of knowledge acquisition techniques and a set of acquisition support entities, which work on the library, guided by the generated methodological knowledge, to extract domain knowledge. The generated methodological knowledge, coupled with the domain knowledge, forms the RA tools. We have demonstrated the use of the approach by deriving an RA tool to assist the system analyst to acquire and formalize a requirement specification for a network management system. This approach facilitates the integrating, sharing, and reuse of software methodologies and knowledge acquisition techniques and alleviates the problems associated with the correct generation of requirement specification for different domains.

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.