Number of co-authors:13
Number of publications with 3 favourite co-authors:Keiichi Ishimaru:2Akira Nukuzuma:2Naoki Harada:2
Riichiro Mizoguchi's 3 most productive colleagues in number of publications:Katia Sycara:13Kaoru Sumi:8Koichi Hayashi:5
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Publications by Riichiro Mizoguchi (bibliography)
Sasajima, Munehiko, Kitamura, Yoshinobu, Naganuma, Takefumi, Kurakake, Shoji and Mizoguchi, Riichiro (2007): OOPS: User Modeling Method for Task Oriented Mobile Internet Services. In: 2007 IEEE / WIC / ACM International Conference on Web Intelligence WI 2007 2-5 November, 2007, Silicon Valley, CA, USA. pp. 771-775. Available online
Hayashi, Koichi and Mizoguchi, Riichiro (2003): Document exchange model for augmenting added value of B2B collaboration. In: Sadeh, Norman M., Dively, Mary Jo, Kauffman, Robert J., Labrou, Yannis, Shehory, Onn, Telang, Rahul and Cranor, Lorrie Faith (eds.) Proceedings of the 5th International Conference on Electronic Commerce - ICEC 2003 September 30 - October 03, 2003, Pittsburgh, Pennsylvania, USA. pp. 458-464. Available online
Mizoguchi, Riichiro (2001): Ontological Engineering: Foundation of the Next Generation Knowledge Processing. In: Zhong, Ning, Yao, Yiyu, Liu, Jiming and Ohsuga, Setsuo (eds.) Web Intelligence Research and Development - First Asia-Pacific Conference - WI 2001 October 23-26, 2001, Maebashi City, Japan. pp. 44-57. Available online
Sumi, Kaoru and Mizoguchi, Riichiro (2001): Supporting Cooperative Consensus Formation via Ontologies. In: Zhong, Ning, Yao, Yiyu, Liu, Jiming and Ohsuga, Setsuo (eds.) Web Intelligence Research and Development - First Asia-Pacific Conference - WI 2001 October 23-26, 2001, Maebashi City, Japan. pp. 525-529. Available online
Miyashita, Kazuo, Sycara, Katia and Mizoguchi, Riichiro (1994): Capturing Scheduling Knowledge from Repair Experiences. In International Journal of Human-Computer Studies, 41 (5) pp. 751-773.
In recent years, there have been a lot of efforts in solving scheduling problems by using the techniques of artificial intelligence (AI). However, through development of a variety of AI-based scheduling systems, it has become well known that eliciting effective problem-solving knowledge from human experts is arduous work, and that human schedulers typically lack the knowledge for solving large and complicated scheduling problems in a sophisticated manner. This paper discusses the characteristics of a scheduling problem and describes prior work on acquiring human schedulers' knowledge in scheduling expert systems. Then, a case-based approach, implemented in a system called CABINS, is presented for capturing human experts' preferential criteria about scheduling quality and control knowledge to speed up problem solving. Through iterative schedule repair, CABINS improves the quality of sub-optimal schedules, and during the process CABINS utilizes past repair experiences for (1) repair tactic selection and (2) repair result evaluation. It is empirically demonstrated in this paper that CABINS can revise a schedule along objectives captured in its case base and can improve the efficiency of the revision process while preserving the quality of a resultant schedule.
© All rights reserved Miyashita et al. and/or Academic Press
Yamada, Koichi, Mizoguchi, Riichiro, Harada, Naoki, Nukuzuma, Akira and Ishimaru, Keiichi (1993): Intention Recognition Based on Normal Use for Topic Object and Generation of Cooperative Responses. In: Proceedings of the Fifth International Conference on Human-Computer Interaction - Poster Sessions: Abridged Proceedings 1993. p. 280.
Yamada, Koichi, Mizoguchi, Riichiro, Harada, Naoki, Nukuzuma, Akira, Ishimaru, Keiichi and Furukawa, Hiroshi (1993): Model of Utterance and Its Use in Cooperative Response Generation. In: East-West International Conference on Human-Computer Interaction: Proceedings of the EWHCI93 1993. pp. 252-264.
A cooperative response model is proposed for interactive intelligent systems that recognizes user intentions and makes cooperative responses. Though many models developed so far have shown that they can achieve some form of cooperative responses, the coverage of each model is limited. In this paper, we propose a model which covers various types of cooperative responses. The paper starts with a classification of cooperative responses and discusses the relation between intentions and responses. Based on the discussion, a user utterance model is introduced and an intention recognition mechanism is developed employing domain-independent rules and knowledge about the normal usage of the topic object. The recognized intentions are then used to generate appropriate cooperative responses.
© All rights reserved Yamada et al. and/or Intl. Centre for Scientific And Technical Information
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