Yohei Murakami

IJCAI Conference 2009 Conference Paper

• Rie Tanaka
• Yohei Murakami
• Toru Ishida

Machine translation services available on the Web are becoming increasingly popular. However, a pivot translation service is required to realize translations between non-English languages by cascading different translation services via English. As a result, the meaning of words often drifts due to the inconsistency, asymmetry and intransitivity of word selections among translation services. In this paper, we propose context-based coordination to maintain the consistency of word meanings during pivot translation services. First, we propose a method to automatically generate multilingual equivalent terms based on bilingual dictionaries and use generated terms to propagate context among combined translation services. Second, we show a multiagent architecture as one way of implementation, wherein a coordinator agent gathers and propagates context from/to a translation agent. We generated trilingual equivalent noun terms and implemented a Japanese-to-German-and-back translation, cascading into four translation services. The evaluation results showed that the generated terms can cover over 58% of all nouns. The translation quality was improved by 40% for all sentences, and the quality rating for all sentences increased by an average of 0. 47 points on a five-point scale. These results indicate that we can realize consistent pivot translation services through context-based coordination based on existing services.

IJCAI Conference 2007 Conference Paper

• Toru Ishida
• Yuu Nakajima
• Yohei Murakami
• Hideyuki Nakanishi

To test large scale socially embedded systems, this paper proposes a multiagent-based participatory design that consists of two steps; 1) participatory simulation, where scenario-guided agents and human-controlled avatars coexist in a shared virtual space and jointly perform simulations, and the extension of the participatory simulation into the 2) augmented experiment, where an experiment is performed in real space by human subjects enhanced by a large scale multiagent simulation. The augmented experiment, proposed in this paper, consist of 1) various sensors to collect the real world activities of human subjects and project them into the virtual space, 2) multiagent simulations to simulate human activities in the virtual space, and 3) communication channels to inform simulation status to human subjects in the real space. To create agent and interaction models incrementally from the participatory design process, we propose the participatory design loop that uses deductive machine learning technologies. Indoor and outdoor augmented experiments have been actually conducted in the city of Kyoto. Both experiments were intended to test new disaster evacuation systems based on mobile phones.

AAAI Conference 2005 Conference Paper

Modeling Human Behavior for Virtual Training Systems

• Yohei Murakami

Constructing highly realistic agents is essential if agents are to be employed in virtual training systems. In training for collaboration based on face-to-face interaction, the generation of emotional expressions is one key. In training for guidance based on one-to-many interaction such as direction giving for evacuations, emotional expressions must be supplemented by diverse agent behaviors to make the training realistic. To reproduce diverse behavior, we characterize agents by using a various combinations of operation rules instantiated by the user operating the agent. To accomplish this goal, we introduce a user modeling method based on participatory simulations. These simulations enable us to acquire information observed by each user in the simulation and the operating history. Using these data and the domain knowledge including known operation rules, we can generate an explanation for each behavior. Moreover, the application of hypothetical reasoning, which offers consistent selection of hypotheses, to the generation of explanations allows us to use otherwise incompatible operation rules as domain knowledge. In order to validate the proposed modeling method, we apply it to the acquisition of an evacuee’s model in a fire-drill experiment. We successfully acquire a subject’s model corresponding to the results of an interview with the subject.

AAMAS Conference 2003 Conference Paper

Scenario Description for Multi-Agent Simulation

• Yohei Murakami
• Tomoyuki Kawasoe
• Reiko Hishiyama
• Toru Ishida