TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED

SYSTEMS

Ante Vilenica and Winfried Lamersdorf

Distributed Systems and Information Systems, Computer Science Department, University of Hamburg, Hamburg, Germany

Keywords:

Multi agent based simulation, Optimization, Automatization, Declarative description language.

Abstract:

The simulation of systems offers a viable approach to ﬁnd the optimal conﬁguration of a system with respect

to time, costs or any other utility function. In order to speed up the development process and to relieve

developers from doing cumbersome work that is related to the execution of simulation runs, i.e. doing the

simulation management manually, it is desirable to have a framework that provides tools which perform the

simulation management automatically. Therefore, this work addresses this issue and presents an approach that

reduces the effort to manage simulations, i.e. it eases and automates the execution, observation, optimization

and evaluation. The approach consists of a declarative simulation description language and a framework that

is capable of automatically managing simulations. Thereby, this approach reduces the costs, i.e. with respect

to time and money, to perform simulations. Furthermore, this work targets a special subset of simulations, i.e.

multi agent based simulation, that has grown large attention in many areas of science as well as in commercial

applications in the last decade. The applicability of the approach is proven by a case study called ”mining ore

resources” that has been conducted.

1 INTRODUCTION

Multi-Agent Systems (MAS) are a well established

approach to model and build complex distributed sys-

tems. Due to the inherent distribution and separa-

tion of functionality as well as the ability to act au-

tonomous software agents are a suited paradigm to

build applications that consist of many single enti-

ties that cooperate in order to achieve a certain goal.

The purposeful developmentof MAS is challenged by

the dynamics that these systems exhibit and it leads

sometimes to emergent phenomena (Serugendo et al.,

2006). Therefore, it is a real challenge for application

developers to equip the vast number of parameters

that agents may have with appropriate values in order

to ensure an appropriate behaviour of the MAS appli-

cation. In order to handle this challenge (Edmonds

and Bryson, 2004) have proposed a simulation-based

development process that validates parameter settings

in order to understand the behaviour of the system.

Simulation itself has a long tradition in science

and it is sometimes called as the ”the third way of

doing science” (Macal and North, 2007, page 95) be-

sides observation and experimentation (Kelly, 1998).

Thereby,simulation is used for many reasons. Mostly,

it is used to gather information about systems that are

not accessible due to undesired perturbations. Fur-

ther, it is applied when the behaviour of the system

has a time scale that is too small or to large in order

to be observated (Banks, 1998).

This work targets multi-agent based simulation

(MABS) (Drogoul et al., 2002) which has progres-

sively replaced simulation techniques like object-

oriented (Troitzsch, 1997) or micro-simulation (Or-

cutt, 1957) in many areas. MABS is therefore mostly

of interest for areas that beneﬁt from the opportu-

nity to model and simulate different types of individ-

uals at the same time. In the last ten years MABS

has been successfully used in sociology (Pietrula

et al., 1998), chemistry (Resnick, 1995), physics

(Schweitzer and Zimmermann, 2001), ecology (Hu-

berman and Glance, 1993) and economy (Said et al.,

2002). In economy, for instance, MABS has been

used to gather information and knowledge about stock

markets, self organising markets, trade networks, the

management of supply chains and the behaviour of

consumers. One prominent example of the link be-

tween MABS and economy is the Eurace

project

that aims at simulating the whole european economic

space (Groetker, 2009). Thereby, information taken

http://www.eurace.org/

Vilenica A. and Lamersdorf W. (2010).

TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED SYSTEMS.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Software Agents and Internet Computing, pages 38-46

DOI: 10.5220/0002967000380046

 SciTePress

from MABS can be used to reduce costs, optimize

the efﬁciency of plants or reduce the risk of failure of

an investment to be done.

The work presented here aims at easing the con-

duction of MABS by managing them automatically.

Therefore, a declarative description language for sim-

ulations is presented in connection with a framework

that is capable of computing simulations automati-

cally. This approach enables developers to run dif-

ferent simulations with different settings without the

need to manually adjust the simulation after each run.

Simulations can be conducted as a background pro-

cess or even over night. This reduces the time and at-

tention needed to run simulations for the developers.

Furthermore, the approach presented here can also be

used to search for local maxima (minima) or a global

maximum (minimum) since it contains an optimiza-

tion component.

This paper is structured as follows. The next sec-

tion introduces the declarative simulation language

whereas section 3 describes in detail the architec-

ture of the framework that enables the automated ex-

ecution of simulations. Section 4 presents a case

study from the area of economy that has been con-

ducted using the simulation description language and

the framework. Related work is discussed in section

5 before section 6 gives a conclusion and talks about

future work.

2 DESCRIBING SIMULATIONS

USING A DECLARATIVE

LANGUAGE

This section will present a declarative simulation lan-

guage that is designed in order to describe all infor-

mation related to the execution and evaluation of a

simulation.

2.1 DSDL: Declarative Simulation

Description Language

The aim of the declarative simulation description lan-

guage (DSDL) is to offer application developers an

easy to use but still powerful and ﬂexible language to

describe MABS that can be conducted automatically.

Therefore, the beneﬁt of automatically executing the

simulation and evaluating the results has to be much

higher than the effort needed to model the simulation

with the description language. Figure 1 depicts the

most important aspects of the simulation language on

an abstract level whereas ﬁgure 2 shows a code list-

ing using DSDL to deﬁne an automated simulation.

DSDL itself has been deﬁned using XML Schema.

Therefore, it bases on a widespread standard that is

supported by various software tools which again en-

able a convenient way to create such ﬁles. The next

subsections will explain the elements of DSDL in de-

tail.

2.1.1 Run Conﬁguration

First of all, DSDL introduces the concept of ensem-

bles and single experiments. A simulation consists of

i ensembles that again consist of j single experiments

with i, j ∈ 1...n. Thereby, one ensemble represents

one possible setting of parameters which is simulated

j-times. Such a hierarchy is especially needed for

simulations that have a non-deterministic behaviour.

Thus, in order to get a signiﬁcant result several sin-

gle experiments have to be conducted. The settings

for the ensembles and single experiments are done in

the run conﬁguration part of DSDL. Also, this part

handles two other important aspects: the termination

condition for the experiments as well as the start time

of the simulation.

Termination conditions can be either a time ex-

pression, i.e. the duration an experiment should run,

or a semantic expression, i.e. denoting a criterion

that has to be true in order to terminate the experi-

ment. Semantic expressions can be formulated using

the Java Condition Language (JCL)

. This language

roughly supports expressions that can be formulated

within an ”if” statement in Java.

The other aspect, i.e. the start time, works as a

timer for the simulation. It allows to postpone the

start of the simulation by specifying either a relative

or absolute time. Such an option is especially inter-

esting for simulations that need many resources and

that should therefore be conducted at a time when the

computer is mostly idle.

2.1.2 Data Observer

Data observers allow to specify those elements of the

simulation that should be observed and that in conclu-

sion help to determine the ﬁtness of a certain parame-

ter setting. Such observed elements are mostly certain

agent types or objects in the environment. Data ob-

servers may also contain certain ﬁlters that determine

whether all elements of a type should be observed

or only certain ones. Therefore, the observation can

be customized to the needs of every simulation set-

ting. Also, it can be deﬁned whether the observation

should pull data from the observed elements periodi-

cally, i.e. every n seconds, or on change, i.e. a certain

http://jadex-rules.informatik.uni-hamburg.de/xwiki/

bin/view/Resources/Rule+Languages

TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED SYSTEMS

Optional Components

Simulation ConfigurationData Observer

Visualization

Optimization and

Parameter

Sweeping

Persist Options

Run Configuration

Distribution Options

Mandatory Components

Figure 1: Most important components of DSDL.

trigger is activated that denotes an interesting event.

Thus, the observed events have an id, value and times-

tamp. These raw data can then be further processed

by other components, i.e. especially the optimization

and parameter sweeping part as well as the visualiza-

tion unit.

2.1.3 Optimization and Parameter Sweeping

The optimization of a MAS application with respect

to a given utility function is the main motivation for

automatically executing simulations. Thereby, var-

ious methods and algorithms can be applied while

sweeping through a parameter space in order to ﬁnd

local optima (minima) or a global optimum (mini-

mum).

Generally, algorithms for optimization can be di-

vided into two subclasses: deterministic and proba-

bilistic approaches. The ﬁrst ones ”are most often

used if a clear relation between the characteristics

of the possible solutions and their utility for a given

problem exists” (Weise, 2008, page 22). In contrast,

the latter ones are more feasible if ”the relation be-

tween a solution candidate and its ’ﬁtness’ are not so

obvious or too complicated, or the dimensionality of

the search space is very high” (Weise, 2008, page 22).

Additionally, the same author has proposed a taxon-

omy of global optimization algorithms that shows the

different approaches and techniques that are applied

in this area.

Nevertheless, DSDL does support the use of any

optimization algorithm due to its black box approach.

Therefore, it relies on the Jadex XML data binding

framework

which allows easily to specify the opti-

mization library that will be loaded at runtime to per-

http://jadex-xml.informatik.uni-hamburg.de/xwiki/

bin/view/About/Overview

form the optimization. The developer speciﬁes the

needed input parameter settings in DSDL and those

parameters a passed to the referenced optimization li-

brary which again returns the output parameter set-

tings. Even more, DSDL does support parameter

sweeping without the use of dedicated optimization

methods. Thereby, it is inspired by the speciﬁcation

of batch parameters used in Repast Symphony

. It

allows to deﬁne a parameter range and the step size

that should be applied in order to sweep through this

range. Also, a simple list of parameters can be spec-

iﬁed that should be iterated through. Moreover, pa-

rameter sweeps can also be nested, i.e. the sweeping

through ranges of different parameters can be easily

combined.

2.1.4 Visualization

The visualization section of DSDL is an optional

component. It does not have to be speciﬁed in order to

run a simulation but it can help to understand the re-

sults by not only having the pure facts but also a visual

representation. Thereby, this component is closely re-

lated to the events received from the data observer. It

can take these events and compute different (statisti-

cal) functions that support the evaluation of a certain

parameter setting. Also, the visualization component

can cake eventsfrom different observers and visualize

them within the same chart. Thereby, different types

of charts like pie, area, line, bar, histogram etc. are

supported.

Now, that the features of DSDL have been pre-

sented the next section will introduce the architecture

of a system that is capable of automatically process-

ing simulations speciﬁed in this language.

http://repast.sourceforge.net/docs/reference/SIM/Batch

Parameters.html

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

<SimulationConfiguration name="MarsWorldExperiments"

applicationReference="..\\jadex\\MarsWorldSimulationExperiments.application.xml">

</Data>

</Observer>

</Data>

</Observer>

</Data>

</Observer>

</Observers>

<Input>$MissionTime</Input>

<Type>LineChart</Type>

<Labels xAxis="Number of sentries" yAxis="Time in seconds"

title=“Evaluation of case study: mining ore resources“/>

</DataVisualization>

</Data>

</ParameterSweeping>

</Optimization>

</TerminateCondition>

</RunConfiguration>

</SimulationConfiguration>

Data Observers

Visualization

Parameter

Sweeping

Run

Configuration

Figure 2: Exemplarily simulation description using DSDL.

3 SYSTEM ARCHITECTURE OF

THE FRAMEWORK

The basic idea of this framework is to relieve appli-

cation developers from further work once the simula-

tion has been modelled using DSDL. The simulation

is automatically managed by the framework and the

developer can continue working on other things until

the simulation terminates.

Figure 3 depicts the architecture of the framework

that is capable of automatically executing simulation

experiments. It shows that the framework itself is

composed as a MAS and that it consists mainly of

two types of agents that handle the simulation.

The master simulation agent encapsulates the

main functionality. It processes the declarative sim-

ulation description (cf. section 2) that contains all in-

formation and parameters that are needed to perform

the simulation experiments and returns the results of

the simulation to the developer. Thereby, the master

simulation agent does not perform the single simula-

tion experiments itself. Moreover, it delegates those

to a client simulation agent that is in charge of per-

forming a single experiment. Basically, the master

simulation agent consists of four components: a simu-

lation run manager, a visualization component,an op-

timization component and a distribution component.

Thereby, the simulation run manager is the most

important component. It is instantiated on agent cre-

ation and it handles the progress of the whole simula-

tion by delegating tasks to other subcomponents and

agents. Figure 4 shows the workﬂow of the master

simulation agent. After the agent has parsed the simu-

lation description it creates a task for the execution of

the ﬁrst ensemble. The experiments of this ensemble

are performed by the client simulation agents. Once

the master agent has received these results it evaluates

the observed data accordingly to the simulation de-

scription. Furthermore, the agent persists the raw data

results in a database in order to make sure that they

can be later reprocessed in a different manner than

described in the description. Therefore, this approach

makes sure that the single simulation results can be

analyzed easily after the simulation has been termi-

nated. As the master simulation agent has received all

the results of the experiments of an ensemble it has

to compute the target function of the simulation. This

function may be deﬁned as a simple parameter sweep-

ing with predeﬁned values or it may be a utility func-

tion. Depending on the result of the evaluation of the

target function the simulation master agent terminates

the simulation or it starts to prepare the execution of

a new ensemble with new parameters. In the case of a

simple parameter sweeping those parameters are pre-

deﬁned in the simulation description. In the case of a

utility function the master simulation agent creates a

task for the optimization component. This component

is then in charge of computing the new values for the

parameters. Finally, the master simulation agent cre-

ates a new ensemble and delegates again the execution

of the single experiments to the client agents.

Therefore, it is obvious that the master simula-

tion agent uses basically four components to perform

the simulation and that the simulation run component

manages the execution of the components. Due to the

clear separation of functionality and clear interfaces

TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED SYSTEMS

Master Simulation Agent

Declarative Simulation

Description

<……

….

……

…./>

Single

Simulation

Experiment

Single

Simulation

Experiment

Single

Simulation

Experiment

Client

Simulation

Agent

- Oberserve Execution

- Collect Data

- Terminate Experiment

Client

Simulation

Agent

Client

Simulation

Agent

Optimization Algorithm A

Optimization Component

Init

Online

Offline

Visualization Component

Simulation Run Manager

Distribution Component

Optimization Algorithm B

Report File

<……

….

……

…./>

Figure 3: System architecture of the framework for automatically managing simulations.

components can be easily interchanged. This is espe-

cially important for the optimization component and

the visualization component. Since there are many

approaches towards optimization (cf. section 2.1.3) it

is crucial to have an approach that supports the use of

different mechanisms and libraries. Therefore, exist-

ing libraries like the Java Genetic Algorithms Package

(JGAP)

can be used within the framework to com-

pute the parameters for a new ensemble.

Also, the visualization of simulation results can be

customized according to the requirements of a certain

application domain. Results may be visualized on the

local computer that is running the simulation by using

existing libraries like JFreeChart

. On the other hand,

the results may also be accessed remotely using a web

server and browser to visualize them. Furthermore,

the type of result visualization can not only be dis-

tinguished spatially but also temporarily, i.e. online

or ofﬂine visualization. Whereas online visualization

means access to data of currently running simulation

experiments ofﬂine visualization denotes the process-

ing of data from simulation runs that have terminated.

Thus, the framework presented here, supports both

approaches at the same time.

Finally, the master simulation agent contains a dis-

tribution component. The aim of this component is

twofold: to speed up the simulation process and to

http://jgap.sourceforge.net/

http://www.jfree.org/jfreechart/

use the computation power of several nodes in a com-

puter network. By accessing shared computational

resources across several nodes the simulation frame-

work is even able to run massive agent based sim-

ulations (Yamamoto et al., 2007) which can not be

performed on a single computer. On the other hand,

the simulation framework can use the nodes to paral-

lelize, e.g. to speed up, the simulation by running the

single experiments of an ensemble at the same time

on different computers.

The framework, described in this section, has

been implemented using the Jadex Agent Framework

(Braubach et al., 2005). The next section will describe

a case study that has been done using this framework

implementation.

4 CASE STUDY: MINING ORE

RESOURCES

This section will present a case study called mining

ore ressources that was conducted in order to prove

the applicability of the framework introduced in the

section before. First, the setting of the case study will

be present. Second, the processing of the case study

will be described as well as the evaluation of the re-

sults.

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

Init Master Agent

Parse XML-File

Start Ensemble of

Experiments

Evaluate Results of

Ensemble

& Persist

Do Optimization /

Parameter Sweeping

Finish

Compare

with Target

Function

Init Ensemble with

new Parameters

Figure 4: Workﬂow of the master simulation agent.

4.1 The Setting

The setting of the case study is inspired by an example

presented in (Ferber, 1995) and the basic implemen-

tation of this example was taken from the Jadex ex-

amples library. The case study deals with a MAS that

was implemented in order to perform and coordinate

the mining of ore resources on a non-explored ﬁeld.

As this setting was initially introduced in connection

with the NASA (National Aeronautics and Space Ad-

ministration)

it is sometimes also called marsworld.

Nevertheless, this case study is a representative for

a whole class of problems from the economy. Ba-

sically it targets the question: how many (different)

resources do I need in order to accomplish a goal in

time and budget? Therefore, this is a classic optimiza-

tion function that targets a trade-off often found in the

economy: time vs. money/costs.

Hence, the case study conducted targets the min-

ing of ore resources (cf. ﬁgure 5). It consists of three

types of components (i.e. agents): sentries, produc-

ers and carriers. These three components cooperate

in order to achieve the goal, i.e. to ﬁnd ore resources,

inspect whether they can be explored and ﬁnally to

bring the ore to the home base. At the beginning, all

components explore the environment in order to ﬁnd

ore resources. If an agents ﬁnds a resource it reports

the position to the sentry. The sentry is in charge of

inspecting this resource in order to determine its ca-

pacity. If it can be exploited the sentry sends a mes-

sage to the producer that is in charge of producing ore

http://www.nasa.gov/home/index.html

as much as the capacity permits. As the producer has

ﬁnished its job it informs the carrier. Then, the car-

rier brings the ore to the home base. The agents have

accomplished their goal when all available ore of the

environment has been brought to the home base.

4.2 The Evaluation

Now, the afore described scenario has been taken to

prove the beneﬁt of DSDL and the framework for au-

tomated simulation. In fact, the framework has been

taken in order to answer an important question that

rises from the description of the scenario: What is the

optimal number of sentries, producers and carriers for

a given environment? As every component costs a

certain amount of money it is obvious that the util-

ity function has to take into account the acquisition

cost and operation costs of the agents. Therefore, the

framework has been used to investigate the relation

between the number of operating agents and the time

they need to complete the task.

Table 1 and ﬁgure 6 show the results from the sim-

ulation, that focuses on the relation between the num-

ber of sentries and the time needed to accomplish the

goal, i.e. to collect all ore found in the environment.

The results reveal that in the range from 1 to 10 sen-

tries the time decreases continuously as the number

of sentries rises. At the same time it can also be seen

that this relationship does not hold for simulation ex-

periments that contained more than 10 sentries. Two

things can be concluded from these results. First, it

seems that in order to minimize the time needed to

TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED SYSTEMS

Homebase

Unexplored ore resource

Explored ore resource

Producer: Producing ore

Sentry: Exploring the environment

Carrier: Bringing ore to the homebase

Figure 5: Screenshot depicting the scenario of the case study ”mining ore resources”.

accomplish the goal more than 10 sentries do not pro-

vide a signiﬁcant speed up. Therefore, other compo-

nents than the sentry seem to be a bottleneck which

prevent a better performance. With respect to the set-

ting of the case study these bottlenecks may be the

carrier and the producer since they are not able to an-

alyze and carry all the ore resources in time that have

been discovered by the many sentries. Second, it can

be concluded from the results of the settings with less

than 10 sentries that there is a trade-off between time

and money. Therefore, it depends on the costs of a

sentry whether an additional sentry, that increases the

costs but speeds up the time, pays off.

The case study has proven the applicability of the

frameworkas well of DSDL. It shows the beneﬁt of an

automated simulation management that allows appli-

cation developers easily to run experiments with dif-

ferent parameter settings without the need to manu-

ally edit, observe or evaluate the simulation runs. All

this work is performed by the framework.

5 RELATED WORK

Most of related work is linked to the interference

between simulation and optimization performed by

MAS. Therefore, existing approaches use simula-

tion runs in order to optimize the performance of

an application with respect to some utility function.

These approaches usually use the phrase simulation

based optimization to characterize their aim and have

basically an infrastructure as depicted in ﬁgure 7.

Thereby, these types of approaches often focus on dif-

Table 1: Results of the evaluation of the relationship be-

tween the number of sentries and the time needed to collect

all ore. Every setting was simulated sixty times.

Number of

Sentries

Time:

Mean Value

(sec.)

Time:

Median

Value (sec.)

1 117.94 101.52

2 99.74 87.51

3 84.44 73.75

4 72.13 66.17

5 71.87 65.01

6 62.67 58.51

7 61.45 56.60

8 56.49 54.52

9 55.94 51.04

10 53.95 51.02

11 54.34 52.06

12 52.74 50.19

13 53.92 51.04

14 50.85 49.28

15 52.13 50.55

ferent optimization algorithms and implementations

that use these mechanisms. Often, these approaches

aim to solve problems of a certain application domain.

Whereas (Fu, 2002) has in general investigated how

different optimization techniques are used in the ﬁeld

of simulation (April et al., 2003; April et al., 2004)

present a software tool that has been used to solve

different real-world problems. These approaches use

a mathematical model as input and do therefore not

take advantage of the possibilities offered by simu-

lation models that base on agents (Macal and North,

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

100

120

0 2 4 6 8 10 12 14 16 18

Time in seconds

Number of sentries

Evaluation of case study: mining ore resources

Mean Value

Median Value

Figure 6: Evaluating the relation between the number of

sentries and the time needed to collect all ore. Every setting

was simulated sixty times.

2007). Also, they are designed as closed systems that

can not be extended by users in order to customize the

software according to their need.

(Brueckner and Van Dyke Parunak, 2003) have

presented an approach that goes one step further than

those described above and that is therefore not only

limited to different optimization techniques. More-

over, this work describes an infrastructure that targets

the evaluation of the properties of dynamic systems.

Thereby, optimization is only one aspect among oth-

ers like visualization or persistence. Unfortunately,

the work of Brueckner et al. lacks of the description

of a simulation modelling language as well as certain

details of the infrastructure that is mostly described

on an abstract level. Therefore, it is difﬁcult to value

this work with respect to reusability and extensibility.

In conclusion, existing work does not offer the

connection of a declarative simulation description

language and an open framework for automatically

conducting simulations that is capable of more than

just doing optimization. Nevertheless, existing opti-

mization techniques and tools can be easily integrated

into this open framework.

6 CONCLUSIONS AND FUTURE

WORK

This work targets the development of complex and

dynamic applications. It advocates, that simula-

tion is a viable approach to gain information about

the dynamic behaviour of such systems. Therefore,

this work has presented an approach that automates

the management of simulations and relieves thereby

Optimization /

Parameter

Sweeping

Simulation

Input

Output

Figure 7: Simulation-based optimization.

the application developer from managing simulations

manually.

The approach consists of a declarative simulation

description language (DSDL) that offers the possibil-

ity to deﬁne all aspects related to the execution of

a simulation. In detail, DSDL has components that

deﬁne which data has to be observed, how data can

be visualized and persisted. Moreover, DSDL can be

used to deﬁne the sweep through a parameter space

and this sweeping can be linked with an optimization

mechanism that supports and speeds up the parameter

sweeping process.

Additionally, this work has presented the architec-

ture and implementation of a framework that is capa-

ble of automatically performing simulations that have

been modelled using DSDL. This framework eases

the developer from manually starting simulations with

certain parameter settings and evaluating them. It

supports the whole life cycle of a simulation with

built-in components that can easily be extended and

customized towards the needs of a certain application

domain.

As a proof of concept, a case study has been con-

ducted that shows the beneﬁt of automatically man-

aging simulations. The case study shows further, how

DSDL and the framework can be used to easily op-

timize the setting of an application with respect to a

certain utility function. Although this work targets the

multi-agent based simulation the approach presented

can be adopted and extended to other simulation tech-

niques.

Future work will strive towards further extending

the functionality of the framework. It is envisioned to

add a component that supports the validation of sys-

tem dynamics by offering the possibility to deﬁne hy-

potheses that target the causal structure of the system

as introduced by (Sterman, 2000). Therefore, DSDL

will be extended in order to be able to specify hy-

potheses and the simulation framework will be able

to automatically validate these hypotheses while per-

forming the simulations. Also, it is envisioned to built

up a library that contains the results of all system val-

TOWARDS AUTOMATED SIMULATION OF MULTI AGENT BASED SYSTEMS

idations that have been conducted. The aim of this

library is to gain information about the qualitative be-

haviour of systems that exhibit a certain causal struc-

ture. Therefore, it will be able to predict the behaviour

of a system by analyzing its causal structure and com-

paring it with the results from the library.

ACKNOWLEDGEMENTS

The authors would like to thank the Deutsche

Forschungsgemeinschaft (DFG) for supporting this

work in a project on ”Self-organisation based on

decentralized Co-ordination in Distributed Systems”

(SodekoVS). Furthermore, the authors would like to

thank the Distributed Systems and Information Sys-

tems (VSIS) group at Hamburg University, particu-

larly Lars Braubach and Alexander Pokahr, as well

as the Multimedia Systems Laboratory (MMLab) at

HamburgUniversity of Applied Sciences, particularly

Wolfgang Renz and Jan Sudeikat, for inspiring dis-

cussion and encouragement.

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advances and applications for marrying simulation

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