How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.247

Problem frames: analyzing and structuring software development problems. By Michael Jackson. Published by Addison‐Wesley, Boston, Massachusetts, U.S.A., ACM Press Series, 2001. ISBN: 0‐201‐59627‐X, 390 pages. Price: U.K. ?32.95, U.S.A. $49.99, soft cover.

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Multinomial Language Learning: Investigations into the Geometry of Language

A method for designing an Enterprise information system, which is based on an implementation independent model of the organization, has been developed. This model, also called an ontological model, consists of both implementation independent business processes and implementation independent information processes. An Enterprise information system (EIS) has to be understood as an implementation scenario of the ontological model. This study contains a specification framework that provides rules for taking into account during engineering the EIS.

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A Method for Enterprise Ontology based Design of Enterprise Information Systems

Communication with a machines is inherently not ‘natural’, but still we prefer to interact with them without learning new skills but by using types of communication we already know. Ideally, we want to communicate with a machine just as we communicate with people: we explain (using our voice and gestures) what we want the machine to do, and it understands this and performs the required task.
In its simplest form, such a dialogue system receives the user’s input as written text, which it has to parse and analyze to extract the intentions of the user. But a more complex dialogue system can perceive the user via a microphone and a camera, and the user can use normal speech and gestures to explain his or her intentions. However, this means that the system has to take other aspects of human conversation into account besides interpreting the user’s intentions. For example, it has to manage correct turn-taking behaviour, it has to provide feedback, and it has to manage a correct level of politeness.
This thesis focusses on two aspects of the interaction between a user and a virtual agent (a dialogue system with a visual embodiment), namely the perception of turn-taking strategies and the selection of appropriate responses. This research was carried out in the context of the SEMAINE project, in which a virtual listening agent was built: a virtual agent that tries to keep the user talking for as long as possible. Additionally, the system consists of four specific characters, each with a certain emotional state: a happy, a gloomy, an aggressive, and a pragmatic one. These characters also try to get the user in the same emotional state as they themselves are in.
Turn-taking is a good example of something that is completely natural for most people, but very hard to teach a system. And while most dialogue systems focus on having the agent’s responses start as soon as possible after the user’s end of turn without overlapping it, evidence indicates that starting too early or too late is not always inappropriate per se. People might start speaking too early because of their enthusiasm, or they might start later than usual because they are thinking.
This thesis describes the study of how different turn-taking strategies used by a dialogue system influence the perception that users have of that system. These turn-taking strategies are different start times of the next turn (starting before the user’s turn has finished, directly when it finishes or after a small pause) and different responses when overlapping speech is detected (stop speaking, continue normally or continue with a raised voice).
These strategies were evaluated in two studies. In the first study, a simulator was cre- ated that generated conversations by having two agents ‘talk’ to each other. The turn-taking behaviour of each agent was scripted beforehand, and the resulting conversation was played by using non-intelligible speech. After listening to a simulated conversation, the users had to complete a questionnaire containing semantic differential scales about how they perceived a participant in the conversation. In the second study, the users actively participated in the conversation themselves. They were interviewed by a dialogue system, but the exact timing of each question was controlled by a human wizard. This wizard varied the start time of the questions depending on the selected strategy of that particular interview, and after each interview the users had to complete a questionnaire about how they perceived the dialogue system.
These studies showed that starting too early (that is, interrupting the user) was mostly associated with negative and strong personality attributes: agents were perceived as less agreeable and more assertive. Leaving pauses between turns had contrary associations: it was perceived as more agreeable, less assertive, and created the feeling of having more rapport. It also showed that different strategies influence the response behaviour of the users as well. The users seemed to ‘adapt’ to the interviewing agent’s turn-taking strategy, for example by talking faster and with shorter turns when the interviewer started early during the interview.
The final part of the thesis describes the response selection of the listening agent. We decided to select an appropriate response based on the non-verbal input, rather than on the content of the user’s speech, to make the listening agent capable of responding appropriately regardless of the topic. This thesis first describes the handcrafted models and then the more data-driven approach. In this approach, humans annotated videos containing user turns with appropriate possible responses. Classifiers were then used to learn how to respond after a user’s turn. Different methods were used to create the training data and evaluate the results. The classifiers were tested by letting them predict appropriate responses for new fragments and let humans rate these responses. We found that some classifiers produced significantly more appropriate responses than a random model.

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Response Selection and Turn-taking for a Sensitive Artificial Listening Agent

2 1 1 2 4 5 5 5 5 5 concrete content 1 1 1 1 3 5 5 5 4 4 packing input for development 1 1 1 1 3 4 5 5 4 4 output of development descriptive / static 1 1 1 1 3 3 5 5 4 4 use / time dimension app. functionality 1 1 1 1 3 3 3 4 5 5 system administration design 1 1 1 1 3 4 3 5 5 5 deployment conceptual 1 1 1 2 2 4 3 4 5 5 concrete/instance high level 5 3 1 1 2 5 3 4 4 4 detailed absolute 1 1 1 1 3 5 2 2 2 2 relative (wrt prev. version) application 1 1 1 1 5 2 2 2 2 2 organisation used by me 4 2 1 2 5 2 1 1 3 3 not used by me analyzed, for instance to determine clusters of documents that contain similar documents. Figs. 16.6 and 16.7 depict the document clusters according to auditor 1 and 2 respectively. The clusters have been determined with the single linkage hierarchical clustering method (Johnson, 1967). The axis denotes the difference between the documents, calculated as 1 minus the similarity. For instance, for auditor 1 the similarity between documents FD and FM has been calculated as 0.87, therefore the difference between the two equals 0.13, as shown in Fig. 16.6. Although there are some differences between the two cluster configurations, both auditors seem to agree that there are two large document clusters. One cluster contains documents FD, FM, PD, and XX. The other documents are grouped in the second cluster. Note that we left AS and DB out of the figures to allow for a fair comparison with the effect of LSA. Recall that those two documents were no longer available, due to which LSA was unable to process them. Had we included them in the cluster figures, they would have shown as a small sub-cluster of two very similar documents (similarity according to auditor 1: 0.96; auditor 2: 1.00). For both auditors this sub-cluster is most similar to document IM (auditor 1: 0.79, auditor 2: 0.84). To illustrate the effect of LSA on the document vector-space model, we applied LSA to the 8 documents from the audit that were still available. We determined the

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R.C. de Boer

Papers

Architectural Knowledge Management: Supporting Architects and Auditors