Large-scale human behavior data collection based on real-time challenge balancing and distributed constraint optimization

2015 Fiscal Year Final Research Report

• Project/Area Number: 25540092
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Human interface and interaction
• Research Institution: National Institute of Informatics
• Principal Investigator: Prendinger Helmut 国立情報学研究所, 大学共同利用機関等の部局等, 教授 (40390596)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 環境配慮型運転トレーニング / 最適化された難易度調整 / 分配型制限最適化問題（DCOP）

Outline of Final Research Achievements

We apply multi-agent systems to realize sustainable eco-friendly traffic management We developed iCO2, an online tool for training eco-friendly driving in a multi-user 3D environment. iCO2 supports eco-driving practice by instructing computer-controlled agents, such as traffic lights and other vehicles, to create traffic situations that make eco-driving more difficult. Hence the agents take the role of “opponents” that try to achieve the optimal challenge level for the skill level of each user. The research challenge is to find the optimal challenge level for all user drivers in a shared simulation space that (1) involves both controllable entities (“opponents”) and non-controllable entities (users) and (2) is highly dynamic, with dependencies between entities being created and destroyed in real time. We solve this problem by modeling the scenario as a distributed constraint optimization problem (DCOP).

Free Research Field

AI