This article discusses building a computable design process model, which is a prerequisite for realizing intelligent computer-aided design systems. First, we introduce general design theory, from which a descriptive model of design processes is derived. In this model, the concept of metamodels plays a crucial role in describing the evolutionary nature of design. Second, we show a cognitive design process model obtained by observing design processes using a protocol analysis method. We then discuss a computable model that can explain most parts of the cognitive model and also interpret the descriptive model. In the computable model, a design process is regarded as an iterative logical process realized by abduction, deduction, and circumscription. We implemented a design simulator that can trace design processes in which design specifications and design solutions are gradually revised as the design proceeds.
In part, the critics of AI are driven by the knowledge that'white collar jobs' are the ones that are now under threat. Business leaders are frequently confronted by notions of job-killing automation and headlines on the variation of the theme that "Robots Will Steal Our Jobs." Elon Musk, CEO of Tesla, Silicon Valley figurehead, and champion of technology-driven innovation even goes a step further by suggesting AI is a fundamental threat to human civilisation. The robot on the assembly line is now a familiar image. AI in middle management is new.
More generally, all systems that are supposed to interact with realistic worlds are time-varying. Thus, many formal studies in artificial intelligence relating to time and continuous change representation have been performed [McDermott 1982; Allen 1983; Kowalski and Sergot 1986; Galton 1990; Van Beek 1992]. Unfortunately, there is a big gap between this advanced work and the integration of a time map manager into real-time systems (such as intelligent monitoring systems) essentially due to the complexity of the temporal constraint propagation algorithms, and due to the expressive power insufficiency of the techniques proposed compared to the richness of the situations to be modelled. For real applications, domain-dependent considerations have to be introduced into temporal reasoning systems to ensure computational tractability [Williamson and Hanks 1993]. We adopted a different kind of approach than that based on constraint satisfaction methods. Our work is an endeavour to mimic the physician's attitude who tries to interpret dynamically physiological data. We propose a model using two fundamental mechanisms: aggregation and forgetting, appropriate for a class of applications such as real-time patient monitoring.
Software reuse is an appealing solution to the high cost of software construction and maintenance: If a library of reusable software components were available, then developers could use this library to greatly reduce software development time and effort. Since the goal of software reuse is to reduce development cost, it is valuable to view reuse from an economic perspective. Thus, the effort needed to build a software component library is the reuse investment cost, and the return on that investment is measured by the savings in effort achieved by exploiting reuse over the lifetime of each component. The benefit from a single instance of reuse is the difference between development costs with reuse and estimated development costs without reuse. Reuse is successful only when these benefits outweigh the investment costs. Barnes and Bollinger (1991) outline three ways to make reuse more cost-effective: (1) reduce the initial investment cost of constructing the component; (2) increase the number of times a component is reused; and (3) reduce the cost of selecting, adapting and reusing a component. In this paper, we focus on the third approach, and especially on the cost of adapting a preexisting component.