Copyright (C) 2010 Field Informatics Research Group Kyoto University All Rights Reserved. Multi-agent Simulation Toru ISHIDAHiromitsu HATTORI Dept. of

Copyright (C) 2010 Field Informatics Research Group Kyoto University All Rights Reserved.

Multi-agent Simulation

Toru ISHIDA Hiromitsu HATTORI

Dept. of Social Informatics, Graduate School of Informatics

Kyoto University 1/34

Introduction to Field InformaticsChapter 8

Prediction of Changes in Society How to predict ICT-driven innovations emerge in

society and daily-life? Difficulties of the prediction based on the extrapolation

Diversity and complexity of human behaviors and their interdependency

Limitation of the mechanism design based on top-down approach

How to acquire useful knowledge to design adequate mechanism for unknown society? 2/34


Prediction of Changes in Society How to predict ICT-driven innovations emerge in

society and daily-life? Difficulties of the prediction based on the extrapolation

Technologies for calculating emergent phenomena

Diversity and complexity of human behaviors and their interdependency Representing heterogeneity of behavioral entities

Limitation of the mechanism design based on top-down approach Methodologies to include stakeholders with the design

process

How to acquire useful knowledge to design adequate mechanism for unknown society? 3/34


Multi-agent Simulation (MAS) to Analyze Human Society Are there universal laws to define

behaviors of human society? Different approaches from physical/

chemical simulations are required.

MAS: A technology to represent dynamic human society Modeling human society based on multi-agent

[Multi-Agent Simulation]

Simulation method to calculate a sequence of interactions between humans/organizations based on individual models of autonomous decision-making entities

Typhoon Simulation (The Earth Simulation Center)http://www.jamstec.go.jp/esc/index.html

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MAS and Social Science

Social simulation based on multi-agent systemsMAS to understand and analyze social problems,

economic problems, etc.

The increasing perception that it is essential to analyze local behaviors to understand global phenomena.

Decision Making

Behaviors

Computational model to represent adaptive/interactive

behaviors

Real World Virtual World

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MAS-based Social Simulation: Two Approaches

A method to conduct experiments and to prove hypotheses in the social

science

Social System Analysis

A method to conduct experiments for synthesizing

complex social systems

Social System Synthesis

MAS is used to understand social phenomena and to analyze social systems

MAS for understanding micro-macro relationship in emergent behavior

MAS is used for ex ante verification of new social systems and mechanisms.

MAS for understanding human behaviors under a mechanism/social system

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MAS for Social System Analysis

Investigating human society as a complex systemMacro-level phenomena emerge through micro-level

interactions among individuals Coarse-grained agent behavior model

An agent is normally constructed according to the KISS principle (Keep It Simple Stupid).It becomes difficult to understand the causal

relationships between individuals (agents) and the results of simulations.

Confirmation and understanding of principlese.g. Artificial market/ecosystem simulation

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MAS for Social System Synthesis

Designing new social mechanisms and systemsHow humans or decision-making entities perform in

(simulated) actual environment? Fine-grained agent behavior model

No problem with complex agent behavior model. Simple model is meaningless!

Developing system prototypes and training humanse.g. Robocup rescue

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Participatory Simulation based on MAS

Participatory multiagent simulation (PMAS) is a MAS in which humans control some agents.Some agents are replaced with human-controlled

avatars so as to incorporate realistic humans behaviors

The human can observe the virtual space from his/her internal view point.

Scenario

Virtual space Agent

Multiagent Simulation

Real space

HumanSubject

Scenario

Virtual space Avatar

Participatory Simulation

Monitoring！

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Participatory Simulation based on MAS

PMAS enables us to ..check the validity of the agent’s behaviors acquire information observed by each subject in the

simulation and the operating history.Modeling human behaviorsReproduction of the reality of the actual environment

Real space

HumanSubject

Virtual space

Observation

OperatingHistory

Behavior Model

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Examples of Multi-agent Simulations

Agricultural economics simulation in Thailand

Evacuation simulation

Augmented participatory evacuation simulation

Traffic simulation

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Agricultural Economics Simulation in Thailand (1)

Participatory simulations for understanding issues of agricultural economics in Thailand

Identifying farmers’ land-use changes and their decision-making process

Shared experience between modelers and stakeholders in RPG and MAS

Obtaining decision-making model of famers

The distribution of different rice varieties by government isnot always efficient. How to develop a system that can both deliver good quality seeds & conserve rice biodiversity

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Creating initial models according to literatures Iterative model refinement based on RPG,

interview, and analysis of RPG and interview logs Iterative model refinement based on multi-agent

simulation and analysis of their results

ModelCreation

RPGAnalysis ofRPG logs & interviews

Interview

ModelRefinement

Multi-agentSimulation

SimulationAnalysis

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Modeling based on scientific papers and controlled experiments Conducting RPG with a board representing the

stakeholders' environment Log data for each stakeholder is recorded Interview to stakeholders to get the reasons for their

decisions


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The farmer's decision making process is defined as a decision tree. The tree is refined by machine learning technique and dialogue

with farmers.

[Initial model]


15




with farmers.

[Refined model using RPG data]

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with farmers.

[Final model]

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Evaluating the validity of simulation with modelers and stakeholders. PMAS makes it possible to … Extracting unconscious factors which lead farmers’ decision

Incentive of growing upland cash crops large-scale sugarcane quota leaders influence the crop choice

of smaller growers. help the famers understand the real problems

Farmers were aware of the effects of a decrease in sugarcane prices and the need for coping strategies

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Traffic simulation

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Evacuation Simulation (1)

Virtual city simulator, FreeWalk/Q

Q language processor

(State1 ((?posture :name Follower :state Standing) (!speak :to Follower :sentence "Follow me") (go State2)))(State2 ((?position :name Follower :distance Far) (!turn :to Follower) (go State2)) ((?position :name Follower :distance Near) (!walk :to Exit) (go State2)) ((?position :name Follower :at Exit) (go State3))) Scenario Description Language

Ｑ

Virtual city Simulator FreeWalkAvatars and Agents can coexist

Interpretation

AvatarControl

AgentControl

User

Scenario

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MAS to reproduce Prof. Sugiman’s experimentAgents are controlled by Q scenarios which is

generated according to sugiman’s technical papers. Follow-direction: the leader shouts out evacuation

instructions Follow-me: the leader tells nearest evacuees to follow

him/her

Time(sec)

# of

eva

cuee

s le

ft f

rom

th

e ro

om



Example of Leader’s scenarioScenario, described by Q, is represented by state

transition model

(State1 ((?posture :name Follower :state Standing) (!speak :to Follower :sentence "Follow me") (go State2)))(State2 ((?position :name Follower :at Exit) (go State3)) ((?position :name Follower :distance Far) (!turn :to Follower (go State2)) ((?position :name Follower :distance Near) (!walk :to Exit) (go State2)))

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Unexpected results by the participation of humansSophisticating the evacuee behavior models using the

data from participatory simulation

Fine-tuning evacuation method reflecting humans’ behaviors

[Immanent mode] [Transcendent mode]

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PMAS showed a similar difference between two evacuation methods PMAS can be a platform to evaluate new

evacuation methods.24/34






Traffic simulation

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Augmented Participatory Evacuation Simulation (1)

28 free curvature mirror cameras capture human behaviors on the platform at Kyoto StationHumans behaviors are projected to the virtual spaceHuman subjects do not control avatars, but act in the

real world

Camerason the ceiling

Platformin the Real World

Virtual Space Evacuation Scenario

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There were 15 human subjects on the real space and 3,000 evacuee agents on the virtual space The leader instructs directions to the shelters. Evacuees get information about affected areas, location of

shelters, a direction to the destination. Human subjects know the presence of the virtual evacuees

via the maps on their mobile phones.

1. location information

4. Navigation information

2. Aggregated disaster info.

3. Rough Navigation

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Traffic simulation

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Traffic Simulation (1)

Massive Multiagent Traffic Simulation on MATSimMATSim (Multi-agent Transport Simulation

Toolkit):Implemented by TU-Berlin, and Swiss Federal Institute of Technology Zurich

Each driver is represented as an agent, and the process of route planning is iteratively executed.

The expected traffic flow by MATSim is more precise than that by DTA (Dynamic Traffic Assignment)

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MATSimによる大規模交通シミュレーションMATSim:スイス連邦工科大学チューリッヒ校とベルリン工科大学が開発したマルチエージェントシミュレータ

個々のドライバーをエージェントとして表現し，経路選択を個別に決定するプロセスを反復実行実際の交通流の計測データとの比較では，従来法（動的交通量配分）より正確に交通量を予測

MATSimのエージェントモデルは非常にシンプル新しい交通システムのデザインに向けた，人間の運転行動を採り入れた交通シミュレーションへの要請

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Visualization of traffic flows with driving behavior models obtained from human subjects

Estimation of the effect of traffic mechanisms

Driving Simulation Traffic Simulation

Human Subjectsin the Real World

Traffic SystemDesignerNew Policy/

Mechanism

DomainKnowledge

ObservedData

Driving Behavior Mode

Interview

VehicleAgent

PolicyEvaluation

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参加型シミュレーションの結果から得る運転モデルの挙動と交通流を大規模交通シミュレーションで可視化

実世界に適用する制度の効果を検証・分析

運転シミュレーション大規模交通シミュレーション

実世界（フィールド）

交通システム設計者交通制度

領域知識

観測データ

運転モデル参加

適用

インタビュー

適用

車両エージェント

改善適用

適用

施策評価・分析

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Conclusion

How to interpret the results of multiagent simulation?The results of simulations do not always explain

observation in the real worldWe should be able to explain the simulation results

logically according to a theoretical basis. PMAS as a media for facilitating discussion

PMAS can help to reach an agreement because stakeholders can understand the problem and others ideas/strategies.

We may catch unexpected needs through the lively discussions among stakeholders and domain experts.

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Documents

Copyright (C) 2010 Field Informatics Research Group Kyoto University All Rights Reserved. Multi-agent Simulation Toru ISHIDAHiromitsu HATTORI Dept. of