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In this research, we show a paradox of the "theme park" problem. In the crowded amusement park, it is generally believed that the equalization of queue lines of people can decrease the waiting time for riding on attraction. However, the equalization of queue lines occasionally increases the waiting time in the case where congestion degree is over the limit of capacity. This paradox makes it difficult to reduce congestion. In this paper, we propose a method to reduce the waiting time even in the "theme park paradox" situation, and evaluate effectiveness of our method by multiagent simulation. [ABSTRACT FROM AUTHOR]

The research and development of artificial intelligence (AI) at NTT is advancing along four directions: (1) Agent-AI for analyzing information issued by people and understanding intentions and emotions in that information; (2) Heart-Touching-AI for analyzing unconscious and unnoticeable aspects of a person's mind and body and understanding deep psychological, intellectual, and instinctual states in that person; (3) Ambient-AI for analyzing and understanding just about anything in the world (objects, people, the environment) and instantaneously predicting and controlling those things; and (4) Network-AI for organically connecting and cultivating multiple types of AI and optimizing the entire social system. This article introduces the technologies supporting these four forms of AI and specific AI-related activities in the NTT Group. [ABSTRACT FROM AUTHOR]