The expertise of designers consists, primarily, of information about the relationship between goals or performance criteria and the attributes of the desired artifact that will result in performances that will satisfy these criteria. The designer like experts in other fields is typically better at applying the knowledge that constitutes his expertise than he is at articulating this knowledge. Generation and simulation models are discussed as a means of generating a set of designs for which the set of attributes defining these designs and the performance of these designs in terms of the criteria considered are explicitly defined. Pareto optimization is discussed as a means of structuring these designs on the basis of their performance. The induction algorithm ID3 is used as a means of inferring general statements about the nature of solutions which exhibit Pareto optimal performance in terms of a set of performance criteria. The rules inferred in building design domain are compared with those extracted using a heuristic based learning system.

The goal of this paper is to raise awareness and generate discussion about research methodology in engineering design. Design researchers are viewed as a single communicating community searching for scientific theories of engineering design; that is, theories that can be tested by formal methods of hypothesis testing. In the paper, the scientific method for validating theories is reviewed, and the need for operational definitions and for experiments to identify variables and meaningful abstractions is stressed. The development of a design problem taxonomy is advocated. Generating theories is viewed as guided search. Three types of design theories are described: prescriptive, cognitive descriptive, and computational. It is argued that to seek prescriptions is premature and that, unless the human and institutional variables are reduced to knowledge and control, cognitive descriptive theories will be impossibly complex. A case is made for a computational approach, though it also shown that computational and cognitive research approaches can be mutually supportive.

The specific semantics of CAD objects are analysed concerning their nature their dynamicity and their consistency during the design process. The nature of CAD objects is concerned with their structure and relationships. The dynamicity is concerned with the evolving nature of the objects, i.e. their behavior. The consistency is concerned with their completeness and relationships with integrity constraints.

A new methodology for semantic constraints management and control is defined. It relies extensively on database and expert system technologies for the implementation of new concepts, e.g. logical prototypes of objects and object equivalence class.

It provides a sound and unified basis for modelling the dynamic nature of complex objects, concerning both the management of their structure and the certification of the update operations, i.e. the control of their correctness.

The functionalities of CADB, a prototype expert database system that supports these features, are detailed. CADB is currently implemented in Prolog on VAX™ 11/785 and APOLLO™ workstations.

This article gives an overview of the metallurgical database of ALADIN, an expert system that aids metallurgists in the design of new aluminum alloys. Declarative structured representations in the form of schemata are used for metallurgical data and concepts. The representation is very general, as the goal has been to create a representation for all knowledge about aluminum alloys and metallurgy relevant to the design process. The alloy database and the architecture of the microstructure database is discussed in detail. The microstructure of alloys is described by an enumeration of the types of microstructural elements present along with their characteristics.

KANT is a knowledge-based system designed to diagnose Olivetti personal computers connected as remote terminals to a host computer through a SNA link. KANT is a collection of a few knowledge-based units: some of them operate in the field, and some operate back at the home office. They are configured in two blackboard systems which exchange data via the SNA link. The first blackboard runs on a cheap personal computer, only employs shallow knowledge, and performs the diagnoses that can be achieved in the field. The second blackboard runs on corporate mainframes, employs deep knowledge, and supports the more sophisticated analysis that is required from the project team for fixing complex problems.

This paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.

First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.

The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

In this paper a unique design methodology known as 1stPRINCE (FIRST PRINciple Computational Evalualor) is developed to perform innovative design of mechanical structures from first principle knowledge. The method is based on the assumption that the creation of innovative designs of physical significance, concerning geometric and material properties, requires reasoning from first principles. The innovative designs discovered by 1stPRINCE differ from routine designs in that new primitives are created. Monotonicity analysis and computer algebra are utilized to direct design variables in a globally optimal direction relative to the goals specified. In contrast to strict constraint propagation approaches, formal qualitative optimization techniques efficiently search the solution space in an optimizing direction, eliminate infeasible and suboptimal designs, and reason with both equality and inequality constraints. Modification of the design configuration space and the creation of new primitives, in order to meet the constraints or improve the design, are achieved by manipulating mathematical quantities such as the integral. The result is a design system which requires a knowledge base only of fundamental equations of deformation with physical constraints on variables, constitutive relations, and fundamental engineering assumptions; no pre-compiled knowledge of mechanical behavior is needed. Application of this theory to the design of a beam under torsion leads to designs of a hollow tube and a composite rod exhibiting globally optimal behavior. Further, these optimally-behaved designs are described symbolically as a function of the material properties and system parameters. This method is implemented in a LISP environment as a module in a larger intelligent CAD system that integrates qualitative, functional and numerical computation for engineering applications.

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.

Inductive learning is proposed as a tool for synthesizing domain knowledge from data generated by a model-based simulator. In order to use an inductive engine to generate decision rules, the pre-classification process becomes more complicated in the presence of multiple competing objectives. Instead of relying on a domain expert to perform this pre-classification task, a clustering algorithm is used to eliminate human biases involved in the selection of a classification function for pre-classification. It is shown that the use of a clustering algorithm for pre-classification not only further automates the process of knowledge by synthesizing, but also improves the quality of the rules generated by the inductive engine.

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.

An interactive system, referred to as MECXPERT {Mechanism Expert}, has been designed with the expressed purpose of assisting nonexpert design engineers in creating mechanisms for fulfilling specific motion-conversion and/or power-transmission requirements. The particular knowledge representation scheme chosen for this application comprises a hybrid formulation of a rule-based production system with a frame-based approach. The underlying control strategy is based on a series of special-purpose, domain-specific operators whose function is to move from one problem space to another through various stages or ‘states’ that comprise the mechanism design process.

The primary focus of this paper centers on the representation of knowledge and its control within an expert system for creative mechanism design. An overview summarizing the reasons for developing such an expert system is provided, and the formulation of a problem is discussed through an example taken from the design of a variable-stroke internal-combustion engine mechanism.

This article discusses the issues that arise in the design and implementation of expert systems. These issues include: task selection; the stages of development of expert system projects; knowledge acquisition; languages and tools; development and run-time environments; and organizational and institutional issues. The article closes with some speculation about the future development of expert systems.

Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.

The knowledge used in the design of engineering systems includes: understanding systems and their components, and the understanding implications of design decisions on other decisions and further problem decomposition This paper presents design as a process and then characterizes the knowledge used in synthesizing design alternatives. A knowledge based approach to design synthesis is proposed, followed by a description of the implementation of a domain independent synthesis framework. The implementation is further illustrated by an example application to structural design.

Engineering design involves the evaluation and satisfaction of a wide variety of constraints. The ability to represent and process these constraints in a computer is important for the verification of the output produced by computer-aided design programs. Constraints need not only check designs but can also be used to derive design solution s that satisfy constraints. The paper discusses how to represent the dual nature of constraints so that design consistency is maintained as the design evolves.

Assumptions and rules of thumb are used frequently in design to propose initial solutions. We represent the logic behind the derivation of these assumptions as heuristic procedures and maintain the dependencies between these assumptions and their consequents as an aid to the management of design consistency. We also propose a simple scheme, involving the partitioning of the design modules, to effect design changes when constraint violations occur. An example from structural design illustrates the methodology.