A CONTEXTUAL ENVIRONMENT APPROACH

FOR MULTI-AGENT-BASED SIMULATION

Fabien Badeig

1,2

, Flavien Balbo

1,2

and Suzanne Pinson

Universit´e Paris-Dauphine, LAMSADE Place du Mar´echal de Lattre de Tassigny, Paris Cedex 16, France

INRETS Institute, Gretia Laboratory, 2, Rue de la Butte Verte, 93166 Noisy Le Grand, France

Keywords:

Multi-agent-based simulation framework, Scheduling policy, Environment, Contextual activation.

Abstract:

Multi-agent-based simulation (MABS) is used to understand complex real life processes and to experiment

several scenarios in order to reproduce, understand and evaluate these processes. A crucial point in the design

of a multi-agent-based simulation is the choice of a scheduling policy. In classical multi-agent-based simula-

tion frameworks, a pitfall is the fact that the action phase, based on local agent context analysis, is repeated

in each agent at each time cycle during the simulation execution. This analysis inside the agents reduces

agent ﬂexibility and genericity and limits agent behavior reuse in various simulations. If the designer wants to

modify the way the agent reacts to the context, he could not do it without altering the way the agent is imple-

mented because the link between agent context and agent actions is an internal part of the agent. In contrast to

classical approaches, our proposition, called EASS (Environment as Active Support for Simulation), is a new

multi-agent-based simulation framework, where the context is analyzed by the environment and where agent

activation is based on context evaluation. This activation process is what we call contextual activation. The

main advantage of contextual activation is the improvement of complex agent simulation design in terms of

ﬂexibility, genericity and agent behavior reuse.

1 INTRODUCTION

Multi-Agent-based simulation (MABS) modeling is

decomposed in three phases (Fishwick, 1994): 1)

problem modeling, 2) simulation model implementa-

tion, 3) output analysis. Problem modeling is related

to the agent modeling, accurately specifying their be-

haviors according to their contexts (Macal and North,

2005); Model implementation is related to coding

and execution of the simulation model by the MABS

framework; Output analysis is related to the analysis

of the simulation results. The scheduling policy is an

essential part of the simulation design. Both the pro-

gression of time and how the agents are activated to

perform an action are taken into account by schedul-

ing policy. The way the agents are activated remains a

relatively unexplored issue even if the activation pro-

cess impacts the agent design.

In multi-agent-based simulation, the agents are

situated in an environment. They are capable of au-

tonomous actions in this environmentin order to meet

their objectives. The scheduling policy deﬁnes the ac-

tivation order of the agents that behave according to

their own state and the state of the environment that

they perceive. These states constitute what we call

the agent context. How the agents are activated and

what information is available to compute the agent

context depend on the multi-agent-based simulation

framework.

In most MABS frameworks, context perception

and computation is done by the agents, this type of

framework is agent-oriented and presents several lim-

its. The ﬁrst limit is that an agent model is not inde-

pendent on the MABS framework. The modeling of

the relations between the context and the agent ac-

tions is done during the problem modeling phase. As

a consequence, the choice of a MABS framework im-

plies the choice of an agent model. Since problem

modeling and model implementation phases are not

clearly separated, the genericity of the agent model is

limited. The second limit is that the context compu-

tation is repetitive because the context is computed in

each agent at each time cycle during the simulation

execution. This computation is done in each agent

even if the agent context has not changed between two

time cycles and/or if a group of agents shares the same

context during a time cycle. The last limit is related

to the ﬂexibility and the reusability of the simulation

model. The ﬂexibility of the simulation model de-

pends on the ease with which the simulation designer

212

Badeig F., Balbo F. and Pinson S. (2010).

A CONTEXTUAL ENVIRONMENT APPROACH FOR MULTI-AGENT-BASED SIMULATION.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 212-217

DOI: 10.5220/0002733302120217

 SciTePress

can change the relation between a context and an ac-

tion. In agent-oriented frameworks, this relation is

computed in the agents and any change in this relation

implies the modiﬁcation of the agent implementation.

To cope with these limits, we propose a new

MABS framework, the Environment as Active Sup-

port for Simulation (EASS) framework. The EASS

framework is based on a coordination principle that

we have called Property-based Coordination (PbC)

(Zargayouna et al., 2006). This improves the ﬂexi-

bility and reusability of the simulation model: 1) ﬂex-

ibility: in order to evaluate several hypotheses for the

same simulation model, the agent behaviors should

be changed without modifying the implementation of

the agents; 2) reusability: the same action implemen-

tation and context modeling should be used in several

simulation models. These objectives imply that con-

text evaluation should not be computed in the agents,

but should be externalized. Our new framework is

environment-oriented because the environment man-

ages the scheduling policy and the activation process.

Our objective is that an agent is directly activated by

the environment according to its context to perform a

suitable action associated with this context. This acti-

vation process is what we call contextual activation.

The remainder of the paper is organized as fol-

lows. Section 2 focuses on the classical activation

process where advantages and limits are highlighted.

Then we explain why the environment as an active

support of simulation is an interesting alternative. In

Section 3, we detail our EASS framework. Section

4 presents our ﬁrst results and evaluation. The paper

concludes with general remarks.

2 STATE OF THE ART

As we said before, one of the main tasks in multi-

agent-based simulation design is the choice of a

scheduling policy and more precisely the choice of

the scheduler role in the agent activation process. In

classical MABS frameworks, the scheduler is a spe-

ciﬁc component which ordonnes the activation of the

agents. When an agent is activated, it has to com-

pute its context before acting. By context computa-

tion we mean the recovery and the accessible infor-

mation analysis process. When all agents of the sim-

ulation are activated, the time of the simulation is up-

dated (from t to t + δt).

The classical MABS frameworks are designed

to support this activation process. For example, in

the well-known platform CORMAS (Bousquet et al.,

1998), the scheduler activates a same method for each

agent. To represent agent behavior, the designer has

then to specialize it. In the Logo-based multi-agent

platforms such as TurtleKit simulation tool of MAD-

KIT (Ferber and Gutknecht, 2000) or the STARLOGO

system (http://education.mit.edu/starlogo/), an agent

has an automaton that determines the next action that

should be executed.

The main problem of these frameworks is that the

context computation is implemented in each agent.

As presented in the introduction, there are three lim-

its to these frameworks: 1) the difﬁculty to propose

a simulation model that is independent on the imple-

mentation; 2) the repetitive characteristic of the con-

text computation process during the simulation exe-

cution; 3) the difﬁculty to propose a simulation model

that is ﬂexible and reusable. Indeed, if the simulation

designer wants to modify the simulation behavior, he

has two ways to do so. The ﬁrst way is to modify the

scheduler, i.e. the activation order. The second way is

to modify the behavior of the agents, i.e. their reaction

to their context, which often implies a modiﬁcation of

the way the agent is implemented.

To cope with this last limit, we propose an

environment-oriented framework, that we call EASS

(Environment as Active Support for Simulation).

Context interpretation and context-based reasoning

are key factors in the development of intelligent au-

tonomous systems. Despite signiﬁcant body of work

in MABS design, there is still a great deal to do in

context modeling since generic context models need

to be further explored, more speciﬁcally, the link be-

tween context and agent activation needs to be deeply

studied.

Since the environment is a shared space for the

agents, resources and services, the relations between

them have to be managed. The ﬁrst responsibility

of the environment is the structuring of the MAS.

Modeling the environment is useful to give a space-

time referential to the system components (Mike and

B., 1999). The second responsibility of the environ-

ment is to maintain its own dynamics (Helleboogh

et al., 2007). Following its structuring responsi-

bility, it has to manage the dynamics of the sim-

ulated environment, ensuring the coherence of the

simulation. For example in the simulation of ant

colonies, the environment can ensure the propagation

and evaporation of the pheromone (Parunak, 2008).

Moreover, the environment can ensure services that

are not at the agent level or can simplify the agent

design. Implemented with the simulation platform

SeSam (http://www.simsesam.de/), in a trafﬁc light

control system (Bazzan, 2005), the environment, with

its global view, gives rewards or penalties to self-

interested agents according to their local decision.

Since the environment with its own dynamics can

A CONTEXTUAL ENVIRONMENT APPROACH FOR MULTI-AGENT-BASED SIMULATION

213

control the shared space, its third responsibility is to

deﬁne rules for managing the multi-agent system. For

example, in a bus network simulation (Meignan and

et al., 2006), the main role of the environment is to

constrain agent perceptions and interactions accord-

ing to their state. Because the agents are ”users” of

the services of the environment, and in order to re-

ally create common knowledge, the last responsibil-

ity of the environment is to make its own structure

observable and accessible. In the remaining, we de-

tail our environment-oriented framework and moti-

vate our design choices.

3 THE ENVIRONMENT AS

ACTIVE SUPPORT FOR

SIMULATION (EASS)

FRAMEWORK

The objective of the EASS framework is to improve

the ﬂexibility and reusability of simulation models.

Another advantage of our framework is the use of a

dynamic ﬂexible process for agent activation, i.e. the

possibility for an agent to modify its behavior in an

easy way during the simulation excution. In section

3.1, we present an example that we use all along the

paper to illustrate our framework. Section 3.2 intro-

duces the Property-based Coordination (PbC) princi-

ple. In section 3.3, we describe the general simulation

process with the new scheduling policy. Section 3.4

details the activation process.

3.1 An Illustrative Example

To illustrate our proposition, we use a robot agent-

based simulation example. The interest of this sim-

ulation is the illustration of the following principles

encompassed in our framework: active perception,

decision making of situated agents and coordination

through the environment. In this example, the robot

agents and packets are situated on a grid, i.e. a two-

dimensional space environment. The robot agents act

in this environment and cooperate in order to shift

packets. We have simpliﬁed the coordination pro-

cess of the robot simulation because our objective is

to focus on the ﬂexibility and reusability advantages

that underline our proposition. Each robot agent has

a ﬁeld of perception that limits its perception of the

environment.

As said before, the agent context is computed

thanks to the perceptible available information. We

haveidentiﬁed ﬁve kinds of contexts. The ﬁrst context

is the packet shifting where two robot agents are ready

to shift a packet. This context is related to the follow-

ing information: 1) the skills of the robot agents; 2)

the position of the robot agents and the packet. This

context happens when two robot agents with comple-

mentary skills are close to a packet. The other con-

texts are the following: 1) the context packet seeking

happens when a robot agent has no packet at proxim-

ity; 2) the context packet proximity when a robot agent

is close to a packet and no robot agent with a comple-

mentary skill is close to the same packet; 3) the con-

text closest packet discovery where a robot agent per-

ceives the closest packet in its perception ﬁeld; 4) the

context handled packet discovery where a robot agent

perceives a packet that is handled by another robot

agent with the complementary skill. The two contexts

related to the packet discovery (closest packet discov-

ery and engaged packet discovery) enable to have two

different behaviors of the robot agents : 1) the oppor-

tunist robot agents are those that choose the closest

packet, and 2) the altruist robots are those that choose

the packet knowing that another agent is waiting to

shift it.

A robot agent is able to perform one of the fol-

lowing actions in a time cycle: 1) the action move

randomly where the robot agent move randomly; 2)

the action move in a direction where the robot agent

moves towards a position; 3) the action wait where

the robot agent waits at a position; 4) the action shift

packet where the robot agent shifts the packet.

3.2 Property-based Coordination

Principle

In order to design our environment-oriented frame-

work, we choose to use the Property-based Coor-

dination (PbC) principle (Zargayouna et al., 2006).

This PbC principle enables the environment to en-

compass all the responsibilities presented previously.

In (Zargayouna et al., 2006), we have deﬁned the PbC

principle as follows: The objective of the Property-

based Coordination principle is to represent multi-

agent components by observable Symbolic Compo-

nents and to manage their processing for coordina-

tion purposes.

In PbC, two categories of symbolic components

are deﬁned. The ﬁrst category is the description of

real components of the multi-agent system: agents,

messages, and objects. The descriptions constitute

an observable state of the MAS components. The

data structure chosen is a set of property-value pairs.

Thus, an agent has its own process and knowledge,

and possesses a description which is registered in-

side the environment. Since the only observable

components are the descriptions inside the environ-

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

214

ment, a control can be applied by the environment

on these descriptions. In the robot simulation exam-

ple (see section 3.1), the description of a opportunist

robot with the skill carry is written as follows: <

(id, 5), ( field

perception

, 4), (id

packet

, unknown), (skill,

carry), (position, (3, 8)), (behavior, opportunist),

(time, 5) >. This opportunist robot agent with the id 5

perceives the descriptions of the other MAS compo-

nents that are situated more 4 cases around it (prop-

erty field

perception

). This robot agent is situated at the

position (3, 8) and has no packet. This description is

registered inside the environment.

The second symbolic component category is re-

lated to the abstract MAS components, especially to

the coordination components. By coordination com-

ponents, called ﬁlters, we mean a reiﬁcation of the

relation between an activation context and an agent

action. An activation context is a set of constraints

on the properties of the descriptions that deﬁnes the

context that satisﬁes an agent need. In the modeling

phase, the relations between the contexts and the ac-

tions have to be represented. We have deﬁned only

one kind of robot agent, the robot agents with the skill

carry or raise and the behavior altruist or opportunist.

The behavior of a robot agent is either opportunist or

altruist as far as its choice of a packet in its percep-

tion ﬁeld is concerned. The opportunist robot agents

are those that choose the closest packet. The altruist

robots are those that choose the packet knowing that

another agent is waiting to shift it. A robot agent is

modeled when all the relations between the contexts

and the actions are represented giving as a result a

set of ﬁlters. Figure 1 gives the relations that enable

to model opportunistic or altruist robot agents. The

difference relies on the context to activate the action

move in a direction.

ROBOT

CONTEXT IDENTIFICATION

packet

proximity

packet

shifting

packet

seeking

engaged

packet discovery

closest

LINK CONTEXT - ACTION

waitingshift packetmove randomly

packet seeking

packet shifting

engaged packet discovery

closest packet discovery

move in a direction

packet proximity

altruist robot

opportunist robot

altruist robot

opportunist robot

actioncontext

caption:

Figure 1: Agent behaviors and activation context.

In the robot simulation example, if a robot agent

perceives a packet in its perception ﬁeld, its need is

to move in the direction of the packet and its con-

text is then the information about this packet. When

a ﬁlter is triggered, the action to be performed by an

agent is activated. The same ﬁlter can be used by sev-

eral agents if they have the same need. In our exam-

ple, the altruist and opportunist robot agent share the

ﬁlters f

packet seeking

, f

packet shifting

and f

packet proximity

For example, the ﬁlter f

packet proximity

reiﬁes the link

between the context packet proximity and the agent

action waiting.

The reiﬁcation of agent needs by the ﬁlters is the

starting point of contextual processing of agent action.

The advantage is that each agent choose its reaction to

its current context including its own state.

3.3 Our Approach of the Simulation

Process

In EASS framework, the environment manages the

ﬁlters that are the result of the modeling phase, and

the information related to the simulation components

(agents, objects, ...). In addition, the environment in-

tegrates the management of the scheduler and of the

simulation time. Thus, the environment is able to ap-

ply what we call contextual activation. An agent is

directly activated by the environment depending on

its context and performs the suitable action associated

with this context (see ﬁgure 2).

Each agent chooses to put dynamically a ﬁlter in-

side the environment according to its behavior, and

only ﬁlters inside the environment are taken into ac-

count in the simulation process. In ﬁgure 2, the robot

agents R

and R

put in the environment several ﬁlters

corresponding to the contexts they want to activate. In

this ﬁgure, each ﬁlter is represented by a link between

the scheduler (SP) and the agent. So the environ-

ment contains the ﬁlters related to the contexts packet

seeking, closest packet discovery, engaged packet dis-

covery, packet proximity and packet shifting. The al-

truist agent robots and the opportunist ones share the

same ﬁlters for the evaluation of the contexts packet

seeking, packet proximity and packet shifting. The be-

havior distinction between this two types of agents is

done on the evaluation of the context about the packet

discovery (see section 3.1). When an agent is acti-

vated, it performs the suitable action and has to up-

date its internal time. Before performing an action

associated to a context, an agent robot validates the

execution of this action. The minimal condition to

evaluate the context of a ﬁlter for an agent is the com-

parison between the internal time of the agent and the

simulation time. The internal time of the agent has to

be inferior or equal to the simulation time.

In the robot simulation example, at a time cycle,

if the internal time of the agent R

is inferior or equal

to the simulation time and if only the context engaged

packet discovery is veriﬁed for R

, R

is activated to

perform the action associated with this link and it up-

A CONTEXTUAL ENVIRONMENT APPROACH FOR MULTI-AGENT-BASED SIMULATION

215

EASS framework

1/ validation of the action

2/ execution of the action

1/ validation of the action

2/ execution of the action

Agent altruist R

Agent opportunist R

Scheduler

2/ activation of the agent action

1/ context computation

action

engaged packet discovery

packet seeking

closest packet discovery

packet shifting

packet proximity

contextual activation

Environment

update

time

simulation

Figure 2: Contextual activation approach.

dates its internal time. When all the agents have an

internal time superior to the simulation time, all the

agents have been activated and the simulation time

cycle is over. So the simulation time is updated from

time t to t + δt.

The advantage of contextual activation is the sim-

ulation ﬂexibility. This means that agent behavior

can be easily modiﬁed. By modifying the relation

between actions and contexts, agent behavior is au-

tomatically modiﬁed as well as simulation behavior.

Agent behavior can be modiﬁed in three ways: 1) if

the action associated with a link is modiﬁed (the agent

wants to react differently to a context); 2) if the con-

text of a ﬁlter is modiﬁed and not the action associated

(the agent wants to modify the situation triggering an

action) or if the agent changestheconditions on a con-

text; 3) if the agent put or removes a link during the

simulation execution (the agent wants to activate or to

desactivate a reaction to a speciﬁc context).

3.4 Scheduling Policy

The environment manages the scheduling policy that

consists in organizing the set of the ﬁlters and the sim-

ulation time. Our purpose is to manage the schedul-

ing policy and the simulation time thanks to the same

formalism about the ﬁlters. Consequently, the envi-

ronment contains two types of ﬁlters. The ﬁrst type is

related to the activation process of the agents and the

second is related to the management of the simula-

tion. In a multi-agent-based simulation using EASS,

each ﬁlter evaluation has to be scheduled. In EASS,

there are two scheduling levels: the global scheduling

level which controls the execution of the simulation,

and the local scheduling level which controls the con-

textual activation of the agents.

1/ At the global scheduling level, our proposition

is to control the execution of the simulation by a state

automaton that we call execution automaton where a

state is a set of ﬁlters. By default, the execution au-

tomaton contains only one state related to the man-

agement of the simulation. Its contains at least two

ﬁlters called f

default

that activates the agents by de-

fault and f

time update

that updates the time of the sim-

ulation. The ﬁlter f

default

compares the internal time

of agents and the time of the simulation. The internal

time of an agent represents the time when an agent

wants to be activated. Consequently, the internal time

of agents is observable and the agents have an observ-

able property called time. The value of this property

corresponds to the next time when the agent wants to

be activated. In the robot simulation example, the ﬁl-

ter f

default

is renamed f

packet seeking

With this only state, our simulator behavesaccord-

ing to the classical scheduling policy presented in sec-

tion 2 with only the ﬁlters ftime update and f

default

The ﬁlter called f

time update

enables a discrete man-

agement of the simulation time. The ﬁlter f

default

ac-

tivates the action defaultAction that has to be spe-

cialized by the designer. Thus, at each time cycle,

all agents are activated without context thanks to the

ﬁlter f

default

. When an agent is activated, he has to

compute their context to determine the suitable action

to perform.

In our example of robot simulation, the execu-

tion automaton is composed of two states. The ﬁrst

state contains the ﬁlters associated with the con-

texts : packet shifting, packet proximity and packet

discovery (respectively f

packet shifting

, f

packet proximity

and f

packet discovery

). The second state contains the

ﬁlters f

packetseeking

which is the default ﬁlter, and

time update

. The transitions between the execution au-

tomaton states are triggered by the value of a variable

state that is registred inside the environment. The ﬁl-

ter f

stateX→stateY

enables to change the value of the

variable state from stateX to stateY. The transition

between states is done when there is no ﬁlters belong-

ing to the current state waiting to be triggered. A cy-

cle of simulation corresponds to the evaluation of all

ﬁlters in each state of the execution automaton. The

order of the execution states is important because it

determines the group of ﬁlters that will be evaluated.

Consequently, the order of the states can be changed

to test other scenario and to modify the simulation be-

havior.

2/ The local scheduling level is related to the man-

agement of the ﬁlters which are in the same state of

the execution automaton. At each time cycle, a simu-

lation agent performs at most one action. In one state

of the executionautomaton, three problems may arise:

1) a conﬂict problem between potential ﬁlters which

can be triggered for the same agent; 2) the fact that

the same agent may be activated more than once in

the same cycle (uniqueness of agent action); 3) what

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

216

happens if a simulation agent is not related to any ﬁl-

ters. To solve the conﬂict problem, our proposition is

to give a priority level to each ﬁlter. The ﬁlter with

the highest priority is evaluated before a ﬁlter with

a lower priority in the same state of the execution au-

tomaton. In a simulation time cycle, the uniqueness of

an agent action is ensured by a comparison between

the internal time of the agent and the time of the simu-

lation. If an agent has not ﬁlter that has been triggered

at the current time cycle and if its internal time is in-

ferior or equal to the time of the simulation, the ﬁlter

default

is used to activate this agent.

This contextual activation approach presents sev-

eral advantages: 1) two control levels, i.e. a global

scheduling level and a local scheduling level, allow a

better way to modify the simulation behavior; 2) the

activation is contextual and avoids a repetitive com-

putation of the context inside the agents; 3) the same

agent is activated at most once a time cycle.

4 EXPERIMENTATION AND

FIRST RESULTS

In order to study the feasibility of our proposal, a

prototype of our MABS framework has been imple-

mented as a plugging of the multi-agent platform

Madkit (Ferber and Gutknecht, 2000). MadKit is a

modular and scalable multi-agent platform written in

Java. MadKit software architecture is based on plug-

ins and supports the addition or removal of plugins to

be adapted to speciﬁc needs. Our plugin is composed

of an environment component with an API that en-

ables agents to add/retract/modify their descriptions

and ﬁlters. The instantiation of the MAS components

into a Rules Based System (RBS) is straightforward:

the descriptions are the facts of the rule engine, and

the ﬁlters are its rules. The RETE algorithm decides

rule ﬁring.

Thanks to a more complex robot simulation ex-

ample (Badeig et al., 2007), we have undertaken sev-

eral tests and validation as far as the ﬂexibility of

the EASS model is concerned. The ﬂexibility of the

proposition has been evaluated by the number of sim-

ulations that have been tested without changing the

implementation of the agents. With eight ﬁlters, we

have tested six simulations with two different inter-

action policies. More details related to the efﬁciency

of the EASS framework are given in (Badeig et al.,

2007).

5 CONCLUSIONS

In this paper, we have presented a new framework

called EASS (Environment as Active Support for

Simulation) for multi-agent-based simulations. In

this framework, we have proposed a new activa-

tion process that we call contextual activation. Our

proposition is based on the Property-based Coordina-

tion(PbC) principle which argues that the components

of a multi-agent system have to be observable through

a set of properties. We propose to represent explicitly

the relation between the agent actions and their con-

texts. The main advantage is to improve the ﬂexibil-

ity of the simulation design and the reusability of the

simulation components.

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