Present-day software applications are increasingly required to be “reuse-conscious” in terms of the operating platforms, topology, and evolutionary requirements. Traditionally, there has been much difficulty in communicating specialized knowledge like design intents, design recommendations, and design justifications in the discipline of software engineering. This paper presents a methodology based on the combination of design rationale and design patterns to design reusable software systems. Design rationale is the representation of the reasoning behind the design of an artifact. Design patterns are descriptions of communicating objects and classes that are customized to solve a general design problem in a particular context. The paper details the use of an explicit software development process to capture and disseminate the specialized knowledge (i.e., intents, recommendations, and justifications) that augments the description of the cases in a library (i.e., design patterns) during the development of software applications by heterogeneous groups. The importance of preserving and using this specialized knowledge has become apparent with the growing trend of combining the software development process with the product (i.e., software code). The importance of codifying corporate memory in this regard is also important considering the changing nature of the workplace, where more people are on contract. The information on how and why a software code was developed becomes essential for efficient and smooth continuity of the present software project as well as for reusing the code in future projects. It has become essential to capture the design rationale to develop and design software systems efficiently and reliably. The software prototype developed using the combined methodology will be used as a part of an integrated design environment for reusable software design. This environment supports collaborative development of software applications by a group of software specialists from a library of building block cases represented by design patterns.

This paper describes decisions made during the development of an expert system for advising on problems that arise in the use of cutting fluids in engineering. It covers the problems of knowledge acquisition and of knowledge representation and of the relationship between them. The need for iterative prototyping is noted, and the choice between a database and a rule-based approach is discussed. The paper model and the machine model may not be isomorphic, although both are useful, albeit for different purposes.

The combination of the paradigms of shape algebras and predicate logic representations, used in a new method for describing designs, is presented. First-order predicate logic provides a natural, intuitive way of representing shapes and spatial relations in the development of complete computer systems for reasoning about designs. Shape algebraic formalisms have advantages over more traditional representations of geometric objects. Here we illustrate the definition of a large set of high-level design relations from a small set of simple structures and spatial relations, with examples from the domains of geographic information systems and architecture.

Most of machine learning in design has focussed on learning generalizations to predict some future behavior of the system under consideration. Such approaches have been applied primarily to the analysis and synthesis stages of designing. There has been little work done relating to the formulation stage. This paper applies a particular machine learning approach to the improvement of the formal description of the design formulation. It applies an evolutionary technique to the problem reformulation to improve the formulation. This results in both a near optimal problem formulation and an improvement in the solution synthesized from that formulation.

During the course of the design of a complex artifact many thousands of objects will be created that will refer to many different types of information. Thus, an effective design support system must be able to store various types of data and allow easy navigation of the resulting extensive network of objects. In addition, the quality of the design records (both the record of the design artifact and rationale) is increased by the quality and quantity of information which the designer is able to record. In this paper we describe two new developments to Kbds a design support system for chemical process design, which enable easier navigation of the design network, and a fuller representation of the design process. In addition, we show how these extensions may be used together to improve the quality of the design information recorded—both for the evolution of the design artifact and the supporting rationale. The first development enables the designer to record a variety of complementary types of document within the process design history, fulfilling the task of improving the design information recorded. The second enhancement to Kbds eases the navigation of the design history by categorizing design objects according to a user-defined set of keywords. The categorization of design objects is carried out semiautomatically using, in part, the various design representations (documents) enabled through the first extension and enables rapid navigation of the design network. Finally, a method of checking the consistency of design rationale structures using keywords is also described. Thus, various representations may be used to generate keywords that in turn may be used to improve the quality of design rationale records. An example showing this situation is described.

An approach to providing computational support for concurrent design is discussed in the context of an industrial cable harness design problem. Key issues include the development of an architecture that supports collaboration among specialists, the development of hierarchical representations that capture different characteristics of the design, and the decomposition of tasks to achieve a trade-off between efficiency and robustness. An architecture is presented in which the main design tasks are supported by agents – asynchronous and semiautonomous modules that automate routine design tasks and provide specialized interfaces for working on particular aspects of the design. The agent communication and coordination mechanisms permit members of an engineering team to work concurrently, at different levels of detail and on different versions of the design. The design is represented hierarchically, with detailed models maintained by the participating agents. Abstractions of the detailed models, called “agent model images,” are shared with other agents. In conjunction with the architecture and design representations, issues pertaining to the exchange of information among different views of the design, management of dependencies and constraints, and propagation of design changes are discussed.

A number of automated reasoning systems find their basis in process control engineering. These programs are often model-based and use individual frames to represent component functionality. This representation scheme allows the process system to be dynamically monitored and controlled as the reasoning system need only simulate the behavior of the modeled system while comparing its behavior to real-time data. The knowledge acquisition task required for the construction of knowledge bases for these systems is formidable because of the necessity of accurately modeling hundreds of physical devices. We discuss a novel approach to the capture of this component knowledge entitled automated knowledge generation (AKG) that utilizes constraint mechanisms predicated on physical behavior of devices for the propagation of truth through the component model base. A basic objective has been to construct a complete knowledge base for a model-based reasoning system from information that resides in computer-aided design (CAD) databases. If CAD has been used in the design of a process control system, then structural information relating the components will be available and can be utilized for the knowledge acquisition function. Relaxation labeling is the constraint-satisfaction method used to resolve the functionality of the network of components. It is shown that the relaxation algorithm used is superior to simple translation schemes.

The goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.

Recent research in the area of computer-aided engineering design has focused on the development of environments that provide effective integration of several domain specialties for complex multidisciplinary synthesis problems. The definition of communication requirements for co-operative interaction—and the subsequent establishment of a conceptual model for automating the process—are important considerations in the development of such environments. A communication model can also provide the basis for development of a knowledge engineering strategy by defining the organizational and representational requirements for domain knowledge in the automated system. This paper presents a conceptual model for communication in automated interactive design and demonstrates how the model can be employed as a knowledge engineering tool to facilitate the acquisition and organization of domain expertise. Both the process architecture and semantic modeling aspects of the communication problem are considered. An example is included which illustrates the use of the model in formulating an automated integrated engineering system in the domain of floor and equipment layout and design for industrial facilities.