This paper reports on an Intelligent CASE tool, applicable in a structured programming phase, or from detailed design to coding. This is automation of the bottom level in the hierarchical design process of detailed design and coding, where the largest man-hours are consumed. The main idea is that human designers use a CASE tool for the initial design of a software system, and the design knowledge is automatically acquired from the structured charts and stored in the knowledge base. The acquired design knowledge may be reused in designs. By reusing it, a similar software system may be designed automatically. It has been shown that knowledge acquired in this way has a Logarithmic Learning Effect. Based on this, a quantitative evaluation of productivity is made. By accumulating design experiences (e. g. 10 times), more than 80% of the detailing designs are performed automatically, and productivity increases by up to 4 times. This tool features universality, an essentially zero start-up cost for automatic design, and a substantial increase in software productivity after enough experiences have been accumulated. This paper proposes a new basic idea and its implementation, a quantitative evaluation applying techniques from Industrial Engineering, which proves the effectiveness of the proposed system.

This paper discusses the characteristics of human design knowledge. By studying a number of actual human made designs of excellent designers, the most frequent basic mental operations of a typical human designer have been found. They are: a design rule for hierarchical detailing reported previously, a micro design rule for generating a hierarchical expansion, dictionary operations to build a micro design rule and dictionaries. This study assumes a multiplicity of knowledge based on Zipf's theory, "the principle of least effort. " Zipf's principle may be proved and it becomes possible to understand the fundamental nature of human design.

A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.

This paper reports on clich and related mechanisms appearing in a process of human design of software. During studies on human design knowledge, the authors found frequent instance of same pattern of detailing, named clich. In our study, clich is an intermediate level of design knowledge, during a hierarchical detailing step, residing in between simple reuse and creation by micro design rules, which have already been reported. These three kinds of design knowledge are of various types and have different complexities. Discussions on them, focusing on clich type, with procedures of formation of a simple clich skeleton and generation of a clich are given. The studies show a working model of Zipf's principle, and are some trials to reveal a more detail of human designs.