• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.33-40
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• 2008

In this paper, we propose a method to enhance adaptability in a dialogue system using the reinforcement learning that reduces response errors by trials and error-search similar to a human dialogue process. The adaptive dialogue strategy means that the dialogue system improves users' satisfaction and dialogue efficiency by loaming users' dialogue styles. To apply the reinforcement learning to the dialogue system, we use a main-dialogue span and sub-dialogue spans as the mathematic application units, and evaluate system usability by using features; success or failure, completion time, and error rate in sub-dialogue and the satisfaction in main-dialogue. In addition, we classify users' groups into beginners and experts to increase users' convenience in training steps. Then, we apply reinforcement learning policies according to users' groups. In the experiments, we evaluated the performance of the proposed method on the individual reinforcement learning policy and group's reinforcement learning policy.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.3
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• pp.92-100
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• 2015

Unlike most other planning processes, brownfield planning generally requires a high level of technical and legal expertise due to potential site contamination. To successfully engage in inclusionary decision making, an adaptive collaboration strategy for brownfield planning is therefore critical. This study examines how a communicative planning approach can be used to overcome the challenge of enabling experts from different fields to work alongside lay people from the local community to achieve a properly balanced collaboration in brownfield planning. After identifying appropriate indicators for collaboration through a literature review of established communicative planning theory, these indicators are applied to the brownfield planning process, highlighting critical points of collaboration such as site prioritization, assessment, remediation, and redevelopment throughout. The results suggest the critical need for an adaptive model focusing on three aspects: 1. Facilitation of a balanced dialogue between the experts with social, cultural, and design-based knowledge and the ones with scientific and engineering-based knowledge, 2. Preparation of an appropriate tool for risk communication with the lay people, 3. Development of decision support system for the integration of expert-oriented technical data and public opinion-oriented subjective data.