Shohei Yamane

Disaster Evacuation Guide: Using a Massively Multiagent Server and GPS Mobile Phones

The ubiquitous environment enables us to build systems that provide individual users with personalized navigation services in cities. In developing such a system, it is necessary to estimate the influence and the movement of its users and to get feedback. However, it is difficult to perform tests on such a system given the large number of human subjects involved and its scale, which matches that of a major city. One possible solution is proposed herein, the augmented experiment; it combines a multiagent simulation with a small-scale experiment performed with human subjects. In the experiment, the movements of agents that simulate users are shown to human subjects in order to give them the impression that the environment is populated with a large number of users. In this research, we build an evacuation guide system based on GPS-capable cellular phones and perform an augmented experiment wherein human subjects and evacuee agents are directed while the status of the simulation is passed to the human subjects. Interviews of the human subjects confirmed that the augmented experiment successfully gave the impression that a large number of users were present

Massive Multiagent-Based Urban Traffic Simulation with Fine-Grained Behavior Models

As it is getting easier to obtain reams of data on human behavior via ubiquitous devices, it is becoming obvious that we must work on two conflicting research directions for realizing multiagent-based social simulations; creating large-scale simulations and elaborating fine-scale human behavior models. The challenge in this paper is to achieve massively urban traffic simulations with fine-grained levels of driving behavior. Toward our objective, we show the design and implementation of a multiagent-based simulation platform, that enables us to execute massive but sophisticated multiagent traffic simulations. We show the capability of the developed platform to reproduce the urban traffic with a social experiment scenario. We investigate its potential to analyze the traffic from both macroscopic and microscopic viewpoints.

Caribbean/Q: A Massively Multi-Agent Platform with Scenario Description Language

Making a truly useful massively multi-agent system is difficult since the actions of the full ensemble of agents cannot be controlled by designing just one agent. It is critical to control all the agents by using protocols that describe the interaction of agents and the environment in a top-down approach. We introduce a system that uses interaction protocol descriptions and has the capability of controlling hundreds of thousands of agents. This makes it feasible to realize a mega-scale navigation system that can assist the inhabitants of a small city. In developing a massively multiagent system, protocol design and agent development need to be separated to allow specialists to work in concert with one another while honing the different technologies. As a platform for mega-scale navigation system, we devise an architecture for multiagent platforms where the execution of agents scenario and the implementation of agents are explicitly separated. This paper also gives the evaluation and an application example using the platform.

Multi-model based simulation platform for urban traffic simulation

Multiagent-based simulations are regarded as a useful technology for analyzing complex social systems; for example, traffic in a city. Traffic in a city has various aspects such as route planning on the road network and driving operations on a certain road. Both types of human behavior are being studied separately by specialists in their respective domains. We believe that traffic simulation platforms should integrate the various paradigms underlying agent decision making and the target environment. We focus on urban traffic as the target problem and attempt to realize a multiagent simulation platform based on the multi-model approach. While traffic flow simulations using simple agents are popular in the traffic domain, it has been recognized that driving behavior simulations with sophisticated agents are also beneficial. However, there is no software platform that can integrate traffic simulators dealing with different aspects of urban traffic. In this paper, we propose a traffic simulation platform that can execute citywide traffic simulations that take account of the aspects of route selection on a road network and driving behavior on individual roads. The proposed simulation platform enables the multiple aspects of city traffic to be reproduced while still retaining scalability.

Introduction to Scenario Description Language Q

One of the most important attributes of social agents is the ability to fluently interact with human communities. Interaction scenarios are seldom designed by computer professionals, and instead are most often specified by application designers such as sales managers, travel agencies, and school teachers. Our challenge is to extend agent technologies to ensure the social acceptance of agents in various applications. To this end, we have developed Q, a language for designing interaction scenarios among agents and human communities. Q can also act as an interface between computer professionals and application designers. This paper details the specifications of Q, and shows how to design and execute scenarios for social agents

Participatory Simulation Environment gumonji/Q: A Network Game Empowered by Agents

Network games are attracting attention as simulation platforms for social experiments because of their rich visualization performance and scalability. Our objective in this study is to develop a participatory simulation platform on a network game. Unlike non player characters (NPCs) in network games, agents in a participatory multiagent-based simulation (PMAS) should behave as real-world humans according to behavior models. We developed a novel networked participatory simulation platform called gumonji/Q by integrating scenario description language Q with the network game gumonji. This paper details the implementation of gumonji/Q. In order to connect Q and gumonji, we implement communication sub-components that realize TCP/IP communication between them, and a scenario translator to convert a request from Q into a sequence of operators. This makes it possible for the gumonji simulator to deal with human-controlled avatars and Q-controlled agents in a unified way.

An Agent Modeling Method Based on Scenario Rehearsal for Multiagent Simulation

Multiagent Systems are potential computational systems for various practical applications, tools, and so on. Multiagent simulation is one of the remarkable application to evaluate several kinds of phenomena. In order to design an agent for multiagent simulation, it is important to reflect users opinion. However, if a user is not computer professional or does not have technical knowledge of agent logics and programming language, it is hard for him/her to implement his/her own opinion. Participatory design is a promising approach to incorporate users opinion in the agent design and modification process. In this paper, we propose rehearsal oriented testing for implementation of participatory design. By the rehearsal oriented testing, it becomes possible to carry out anytime modification of agents scenario, which describe its behavior during simulation. For rehearsal oriented testing, we set operators for modifying scenarios, which is described using finite state machine model. We also design interaction protocol between a user and an agent to smoothly get information through the user-agent dialog for modifying operators. Under this protocol, an agent informs a user about what kind of information is required.