Environment Engine for Situated MAS

Halim Djerroud and Arab Ali Cherif

LIASD, Advanced Computing Laboratory of Saint-Denis (LIASD),

Paris 8 University, 2 Rue de la Libert

e, 93526 Saint-Denis, France

Keywords:

Situated Multi-agent, Multi-agent Environment, Modeling Environment, Framework.

Abstract:

In the multi-agent system research community, there is general consensus that the environment is important

for multi-agent systems (MAS). However, most researchers minimize the responsibilities of the environment

by reducing it to inter-agent communication, or neglecting to integrate it as a main element of their MAS

models, which can be sufﬁcient depending on the focus and objectives of their work. As a consequence of

these decisions, the potential of MAS is not fully exploited. In some cases, the environment is a key element

that cannot be written off as inter-agent communication, as it currently is in classical MAS. In our opinion it is

important to focus the MAS around the environment. Reducing the environment to inter-agent communication

deprives multi-agent systems of great potential. Our point of view is that the environment is an active entity

with its own processes that can change its state, regardless of the activity of its embedded agents. We propose

including the environment as an entity with a set of laws. Laws can be considered rules that cannot be broken

by agents. However, some researchers have been interested in integrating the environment as ﬁrst class and

have proposed some interesting models. Unfortunately, they are not sustained by practical applications. The

aim of this paper is to contribute in two ways: ﬁrst, to propose an MAS model where an environmental engine

is integrated; the capabilities of this model are comparable to those of a physics engine. Second, we propose

an implementation of this model and some practical cases where it can bring concrete added value.

1 INTRODUCTION

In the multi-agent system research community, there

is general consensus that the environment is impor-

tant for multi-agent systems (MAS). Most multi-agent

frameworks used by the scientiﬁc community and in-

dustry, such as Mobile-C (Chen et al., 2006), JADE

(Bellifemine et al., 1999), JACK (Howden et al.,

2001), Restina (Sycara et al., 2003), Zeus (Nwana

et al., 1999) reduce the environment to a message

transport system or broker infrastructure. Some plat-

forms like MadKit (Gutknecht and Ferber, 2000) and

educational platforms such as NetLogo (Tisue and

Wilensky, 2004) include a system that allows the loca-

tion of agents in a 2D space, which can then be assim-

ilated into a minimalist environment. Even in other

important works such as FIPA speciﬁcations (Suguri,

1999), it is difﬁcult to ﬁnd functionality for the en-

vironment beyond message transport systems or bro-

kers. Some methodologies like Message (Bergenti

et al., 2006), Prometheus (Padgham and Winikoff,

2002) and Trops (Bresciani et al., 2004) offer the ba-

sic features of an environment, but it is not repre-

sented as a fundamental entity in multi-agent systems.

In the literature (Russell and Norvig, 2016; Ferber

and Weiss, 1999; Briot and Demazeau, 2001), envi-

ronments are discussed brieﬂy.

In recent years, the scientiﬁc community has

talked more and more about integrating the environ-

ment as a main part of MAS. Among these works we

can mention SODA (Omicini, 2000), a methodology

in which the environment is taken into account and

that provides speciﬁc abstractions and procedures for

the design of agent infrastructures. In SODA, the en-

vironment is the space in which agents operate and

interact.

D.Weyns et al. have shown in (Weyns et al., 2004;

Weyns et al., 2015; Weyns and Michel, 2015) that

the environment is important for multi-agent systems,

and that environment must be considered a ﬁrst-class

entity. To overcome the concept of the environment’s

absence in MAS, the scientiﬁc community launched

ﬁrst-class: A module that is a ﬁrst class data ob-

ject of the programming language, e.g. a record contain-

ing functions. In a functional language, it is standard to

have ﬁrst class programs, so program building blocks can

have the same status. (Free on-line computing dictionary:

http://foldoc.org/ﬁrst-class )

Djerroud, H. and Cherif, A.

Environment Engine for Situated MAS.

DOI: 10.5220/0007310501290137

In Proceedings of the 11th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2019), pages 129-137

ISBN: 978-989-758-350-6

129

a series of E4MAS (Environments for Multi-agents

Systems) workshops in order to relaunch the debate

and ﬁnd new ideas. The ﬁrst series took place be-

tween 2004-2007

, and the second series ten years

later, from 2013-2014

In the literature, we ﬁnd the concept of the sit-

uated agent. Agents live and act in the environment.

Situated agents choose their actions based on their po-

sition, the state of their perceived world, and their in-

ternal state. Unlike knowledge-based agents, situated

agents do not emphasize the internal modeling of the

environment. Instead, they use the environment as a

source of information. This type of MAS is supposed

to include the position of agents in the environment,

but Wooldridge and Jennings (Wooldridge and Jen-

nings, 1995) deﬁne an agent as: “...a computer sys-

tem that is situated in some environment, and that is

capable of autonomous action in this environment in

order to meet its design objectives”. In this deﬁnition,

”situated agent” refers to an agent in the environment,

however the concept of environment remains abstract.

The deﬁnition does not make explicit what it means

for an agent to be situated in an environment; noth-

ing in the deﬁnition explicitly refers to the fact that

the existence of an agent in an environment entails a

social component.

The word ”environment” can lead to confusion.

To prevent any confusion about the use of the word

environment, for the rest of this article we consider

the environment to be the space in which the agents

evolve and not the software infrastructure on which

the agents are executed.

MAS are used in many ﬁelds. In the simulation of

complex systems, the aim is to reproduce an observed

natural system within a multi-agent system. The pro-

duced MAS can serve to understand the system and

predict its future state. In this paper the aim is to pro-

duce a model and implementation of MAS that allow

a robot to represent its environment in a multi-agent

system, in order to understand the evolution of its en-

vironment based on the robot’s own actions and then

adapt its behavior accordingly.

So, the robot must make an internal representa-

tion of its environment and the other agents evolving

within it as a multi-agent system. However, the en-

vironment in which the robot evolves is governed by

the laws of physics. In this case the environment is

considered an entity that acts upon the agents.

The challenge in this article is to propose a multi-

agent model that allows the representation of the envi-

E4MAS - Environments for Multiagent Systems: https:

//distrinet.cs.kuleuven.be/events/e4mas/

E4MAS - 10 years later: http://homepage.lnu.se/staff/

daweaa/events/E4MAS/2014.htm

ronment as a main entity, the implementation of envi-

ronmental laws, certainty that these laws are not trans-

gressed by agents, and ﬁnally the ability of the envi-

ronment to act upon agents.

As an alternative to using the multi-agent system,

the robot can use a physics engine to represent its

environment. This solution may seem efﬁcient but

is difﬁcult to implement; physics engines use New-

tonian laws to simulate the behavior of the environ-

ment, but to use these laws the robot must know the

exact characteristics of each element within the envi-

ronment, which makes the task complex or virtually

impossible. So, the use of MAS is a good compro-

mise.

The next section gives an overview of the princi-

pal studies and reﬂections on MAS environments by

different authors.

Afterwards, we highlight our disagreement with

these points of view and identify the weaknesses in

these concepts. Subsequently, in section 3 we propose

our deﬁnition of an MAS environment and propose a

model. In section 4 we present an implementation of

said model. We end by discussing the difﬁculties and

shortcomings of our approach before concluding.

2 REVIEW MULTI-AGENTS

ENVIRONMENT MODELS

In the study (Weyns et al., 2004; Weyns et al., 2015;

Weyns and Michel, 2015), several research tracks that

include some notion of the environment are shown. In

this section, our goal is to summarize the most impor-

tant among them.

In (Russell and Norvig, 2016), Russell and Norvig

propose a simple representation of the environment

and its interaction with agents. They argue that “An

agent is anything that can be viewed as perceiving its

environment through sensors and acting upon the en-

vironment through effectors”.

According to Russell and Norvig, the environ-

ment holds the following properties: 1) Accessible

or inaccessible: indicates whether agents can obtain

complete and accurate information about the environ-

ment’s state or not 2) Deterministic or nondetermin-

istic: indicates whether a change of state of the envi-

ronment is only determined by its current state and

the actions selected by the agents or not. 3) Static

or dynamic: indicates whether the environment may

change while an agent deliberates or not. 4) Discrete

or continuous: indicates if the number of percepts

and actions is limited or not. These properties of envi-

ronments have now been adopted by most researchers

in the multi-agent domain.

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

130

In (Ferber and Weiss, 1999) J.Ferber proposes an-

other view of an environment: 1) The environment is

discrete and composed of a set of cells. 2) Central-

ized or decentralized environment: the environment

can be centralized (the cells are grouped together in

a monolithic system) or decentralized (the cells are

linked together via a network). Agents evolve and

perceive their environment through these cells. How-

ever, a distributed environment cell differs in several

ways from a centralized environment. 3) Generalized

or specialized environment: a generalized model

of an environment is independent of the type of ac-

tions that can be performed by agents. A specialized

model of an environment is characterized by a well-

deﬁned set of actions. 4) inﬂuences and reactions

to inﬂuences: inﬂuences come from agents and are

attempts to alter the course of events in the environ-

ment. The environment produces reactions which in

turn cause state changes by combining the inﬂuences

of all agents, given the local state of the environment

and the laws of the world.

In (Parunak, 1997) (Odell et al., 2003) Parunak,

Odell et al. deﬁne the environment as a space

which provides the conditions where agents exist.

Parunak, Odell and their colleagues make the dis-

tinction between physical and communication envi-

ronments. 1) The physical environment provides

the laws, rules, constraints, and policies that gov-

ern and support the physical existence of agents and

objects. 2) the communication environment pro-

vides processes that govern and support the exchange

of ideas, knowledge and information. Functions and

structures are commonly employed to exchange com-

munication, such as roles, groups and the interaction

protocols between roles and groups.

Speciﬁcally in (Parunak, 1997), an MAS is de-

ﬁned as a three-tuple: a set of Agents, an Environ-

ment, and a Coupling between them, shown as fol-

lows:

MAS =< Agents, Environment,Coupling >

Agents = Agent

, ..., Agent

Agent

=< State

, Input

, Out put

, Process

Environment =< State

, Process

An agent is deﬁned as set of states, inputs, outputs

and processes. A state is the set of attributes that de-

ﬁne the agent, the differences in these attributes being

responsible for the variation between different types

of agents. Inputs and Outputs are subsets of State,

whose variables are coupled to the environment. In-

puts and Outputs can represent an agent’s sensors and

effectors; they relate the agent to its environment.

These are the mechanisms that implement the cou-

pling between the environment and the agents. The

process is an autonomously-executing mapping that

changes the agents states.

The environment is deﬁned as an active entity. It

has its own Process that can change its State, inde-

pendent of the actions of its embedded agents. Inputs

and outputs of the embedded agents are coupled to el-

ements of the environment state, but the environment

does not distinguish which elements of state are cou-

pled in this fashion. That distinction depends on the

agents that exist at any moment, and the capabilities

of their sensors and effectors.

In (Rao et al., 1992) Rao et al. describe the char-

acteristics of a generic environment: 1) Agents can

evolve in many different ways in the environment;

2) the environment can be affected by several ac-

tions at the same time; 3) different goals may not

be achievable simultaneously; 4) the actions that best

meet the different objectives depend on the state of

the environment; 5) the environment can only be de-

tected locally; 6) the rate at which calculations and

actions can be performed is reasonably related to the

rate of change in the environment. Rao and his col-

leagues describe the typical characteristics of the out-

side world in which agent systems are deployed and

with which agent systems interact.

In (Demazeau, 2003) Demazeau considers the

four essential components of multi-agent systems to

be: 1) agents; 2) interactions (structuring elements of

internal interactions between entities), 3) organization

(structuring elements of entity sets within the MAS),

4) the environment deﬁned as domain-dependent ele-

ments to structure external interactions between enti-

ties.

In (Ferber, 1997) J.Ferber pointed out that multi-

agent systems can be used for the resolution of two

major categories of problems: the simulation of com-

plex phenomena and distributed problem solving.

We believe that the models described above are too

generic to develop a model that can deal with these

two issues effectively, and we think that this is the

main reason that many researchers ignore integrating

the environment in their models; often, the environ-

ment is indispensable only in the simulation of com-

plex phenomena. For our part, we think that multi-

agent systems are an abstraction of the system we

want to simulate. So, it is important to take into ac-

count that the agents act on the environment and the

environment acts on the agents as well, which is also

the case in the previous models. However, our opin-

ions diverge in that (1) the effects of the environment

are not experienced in the same way by all agents

(2) the agents perceive the environment through sen-

sors, so have information that is likely to be skewed

(3) agents may not feel certain effects of the envi-

Environment Engine for Situated MAS

131

ronment on them and ﬁnally (4) the environment is

the physical medium of the agents, and is similar to a

physics engine in that it consists of a set of laws that

will determine its possible actions. We can say that

our approach consists of proposing an environment-

centered model for the representation of complex phe-

nomena, and to incorporate the ideas that we deem

relevant from the models described above.

3 DESCRIPTION OF THE

ENVIRONMENT MODEL

We can distinguish two main categories of MAS use:

the ﬁrst being the study of complex phenomena, and

the second being distributed problem solving (Ferber,

1997). In the former, the environment plays an essen-

tial role. Studies of MAS focused on the environment

(Weyns et al., 2004; Weyns et al., 2015; Weyns and

Michel, 2015; Russell and Norvig, 2016; Ferber and

Weiss, 1999; Odell et al., 2003; Rao et al., 1992; De-

mazeau, 2003) show two types of difﬁculties: ﬁrst,

the deﬁnition of the environment is too broad, and the

term environment has several interpretations, causing

a lot of confusion. Second, the functionalities associ-

ated with the environment are integrated in and pro-

cessed by the agents. From our point of view, the

environment must be a separate entity, and must in-

teract with and inﬂuence the behaviour of agents. In

the remainder of this section we describe our vision

for the environment and propose a new MAS model

along with how to implement it. The model proposed

below is strongly inspired by the MAS reviewed in

the previous section. We have chosen the points that

we think are important in each model.

We think that the environment must be an au-

tonomous and active entity and not integrated into

the agents as we have seen previously, for example

in messages or brokers. Basically, our approach is

to represent the environment as a fully-ﬂedged entity,

as it is in (Parunak, 1997). First, we deﬁne the envi-

ronment as a space in which agents evolve. Second,

the environment is like a physics engine; it must in-

clude inviolable rules. Third, as is the case in classical

MAS, the agents act on the environment and change

its state. Finally, the environment can change the state

and attributes of agents in order to keep its rules invi-

olable; if an agent tries to transgress an environmental

rule, the environment changes the state of the agent in

order to maintain its integrity.

In order to think of the environment as a separate

entity, we must have a good idea of what the environ-

ment is. To this end, we will try to explain the com-

ponents and functions of an environment in parallel

with the real environment that we are representing.

Particularly in the simulation of complex phenomena,

we try to model the entities and the environment of

the system we want to simulate. If we take the exam-

ple of modelling a multi-agent system and a human

or robot agent that evolves in a natural environment,

the agent does not have a real view of its environ-

ment, but its knowledge of the environment is ﬁltered

through and/or distorted by the sensors through which

it observes it. We can deduce that the environment is

much more complex than the belief of the agent. In

this article we propose a model in which users can

model MAS for complex environments such as nat-

ural ones, for example. Generally speaking, in com-

plex systems many things happen, the agents act on

the environment and the environment also acts on the

agents. However, the agents can only feel the effects

of the environment if they have sensors to feel these

effects, which is the major point that differentiates our

model from the other models discussed in the previ-

ous section.

So, we begin by deﬁning a Multi-Agent System

as a three-tuple: a set of Agents, an Environment, and

Interactions between them :

MAS =< Agents, Environment, Interactions >

Agents = Agent

, ..., Agent

3.1 Environment

We propose that the environment be split into two

parts, the ﬁrst of which would be represented in a

monolithic process, while the second would be inte-

grated into the agents (distributed on all agents). 1)

The monolithic part of the environment represents the

physical and functional characteristics of the environ-

ment, such as: the size of the environment, the gravity,

etc., depending on the environment we want to rep-

resent. 2) Each agent in the system contains part of

the environment’s information, as shown in Figure 1.

Usually, this information concerns the agent itself; for

example: its position in the environment, its energy,

etc. It also includes all possible actions that can be

performed on the environment, such as move, leave a

trace, and so on.

The environment is governed by laws. The en-

vironment can be assimilated to a physics engine.

Agents evolve inside, and are consequently subject to

the laws of the environment. So we can consider that

the environment an entity that acts on the agents that

evolve within it. These agents can only actually feel

the effects of the environment only they are equipped

with sensors to perceive the impact of these laws. Of

course, the model considers that it is the responsibility

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

132

Figure 1: Agents and environment share attributes.

of the user (the MAS developer) to determine which

laws will be implemented by the environment, and it

is also up to the user to determine the attributes of

the agents and/or the environment that must change.

The environment and the agents are two distinct en-

tities: 1) The agents consist of a set of attributes and

knowledge of their possible actions on the environ-

ment; these agents can consult their attributes and can

perform actions on the environment. 2) The envi-

ronment has laws; it has the responsibility to enforce

them, and checks compliance with these laws at reg-

ular time intervals, otherwise automatically changing

the attributes of agents in order to respect those laws.

So, we can deﬁne the environment as a three-

tuple, States, Agents and Process:

Environment =< States, Rules, Process >

States =< SharedAttributes, InternalAttributes >

States. Is a set of attributes that completely deﬁne

the environment. The values of the attributes repre-

sent the state of the the environment at a given the

time. The attributes of the environment represent its

characteristics, such as the size of the environment,

positions of agents evolving in it and so on. We dis-

tinguish two types of attributes: Shared Attributes and

Internal Attributes. The ﬁrst kind of attribute is shared

with agents. The second is not shared with the agents,

typically being the inner properties of the environ-

ment itself.

Rules. The environment’s laws are rules that it must

uphold. For example, two agents must not be in the

same place at once, and the rules are written as fol-

lows:

rule =< expression ? action1 : action2 >

A rule is represented as an expression. It corre-

sponds to the law that the environment must check,

the two actions corresponding respectively to the ac-

tions that must be performed according to whether the

rule is respected or not. In the previous example (the

rule that prohibits two agents from being in the same

place at once), this can represented as follows: if an

agent tries to move to a place already occupied by

another agent, then prevent the movement; if not do

nothing.

The environment is represented as a monolithic

process with a set of attributes, and carries out checks

from time to time in order to respect its laws. Agents

using the environment must ﬁrst register with the en-

vironment. Whenever the agents modify their at-

tributes, they must inform the environment via a mes-

sage. For example, an agent that moves must there-

fore modify one or more of its attributes, and at this

point the agent informs the environment.

The environment also has the ability to change the

attributes of the agents. For example, if the shifting

is not possible because of a rule that is not respected,

then it informs the agent and returns the values of the

attributes to their old values.

Process. An environment has its own process that

can change its state, independently of the actions of

the embedded agents. The primary purpose of Pro-

cess is to implement dynamism in the environment

e.g. processes that check rules and execute actions,

the behavior of objects in the environment, etc.

3.2 Agent

Each agent is a tuple of State and Process:

Agents = Agent

, ..., Agent

Agent

=< States, process >

States =< SharedAttributes, InternalAttributes >

Figure 2: This image illustrates the relationship between

an agent and its environment. Agent and environment have

shared attributes, and each of them has its own attributes

that deﬁne it. The environment is also composed of laws.

Agents act on the environment through actions, and the en-

vironment can directly change agent attributes if the laws

are transgressed.

Environment Engine for Situated MAS

133

States. Agent attributes are a set of values that de-

ﬁne the agent. The structure and variability of these

values are not constrained by this deﬁnition, and dif-

ferences in these features are responsible for much of

the variation among different kinds of agents.

Agents are deﬁned with two types of attributes:

”Shared Attributes” and ”Internal Attributes”. Shared

Attributes are the part of the environment concerning

the agent e.g. position of agent in the environment.

Internal Attributes deﬁne the internal state of the en-

vironment e.g. the log of its movements, its goal, in-

ternal time etc.

Process. Processes are standalone mappings that

change the state of the agent. The agent can perform

these processes without being called by an external

entity. In terms of calculation, an agent has its own

virtual processor.

3.3 Agents and Environment

Interactions

We propose that the multi-agent system be composed

of agents and the environment; when both agents and

environment are time-based, the state values can vary

continuously. The change in a variable resulting from

such a ﬂow may be inﬁnitesimal, depending on the

Process in the receiving entity and the other energy

ﬂows in the system.

To summarize our approach, we can deﬁne an Envi-

ronmental engine-centered situated MAS as follows:

• The agent has ﬁltered (partial) and sometimes dis-

torted knowledge of its environment

• The environment is governed by laws, and these

laws may be known or unknown by the agents.

• The environment acts on the agents.

• Agents act on the environment with actions.

• Agents observe the environment through at-

tributes that can be inﬂuenced by the environment.

In our opinion, this deﬁnition is not always true for sim-

ple and non-complex environments, and for this reason we

have emphasized that our approach is suitable for complex

environments. In the contributions of Russell and Norvig

(Russell and Norvig, 2016), he speaks of a fully observable

and partially observable environment. We can take the two

examples quoted in (Russell and Norvig, 2016): 1) the au-

tomatic taxi which is confronted by a complex environment

and thus partially observable and 2) chess player that has a

complete view of their observable environment.

4 IMPLEMENTATION

In recent decades, many multi-agent architectures, de-

signs and models have emerged. This proliferation is

a sign of great interest for multi-agent systems. How-

ever, in many cases, the proposals are conceptual and

not supported by implementations to validate them.

This lack of implementation is due to the difﬁculty of

producing multi-agent systems due to the complexity

of the underlying concepts (coordination, interaction,

organization, etc). This complexity makes the major-

ity of existing systems difﬁcult to use, and virtually

impossible to use by non-specialists of multi-agent

systems.

There are two problems (as shown in our brief

analysis) to overcome: 1) to present a practical re-

alization to validate our multi-agent model and 2) to

present an easy implementation of use. In order to

propose a framework compliant with the standard and

easy to use for the MAS community, we have chosen

to implement the FIPA standard where possible. Cur-

rently, FIPA is limited in its capacity to describing the

architecture of the system as a whole, the structure

agents and communication between agents, etc. As

we have already mentioned earlier in this article, un-

fortunately it is difﬁcult to ﬁnd the concept of the en-

vironment in FIPA. As a result, regarding the imple-

mentation of the environment, we have not respected

any standard. Currently, the JADE multi-agent plat-

form is widely used by the scientiﬁc community, and

we have tried to take as much inspiration from it as

possible, always with the aim of proposing an easy-

to-use framework, and not making the task difﬁcult

for users with this new tool.

The FIPA standard proposes a reference model for

multi-agent platforms. It proposes a general archi-

tecture (see image 3) for which it requires the exis-

tence of a certain number of specialized agents: 1)

Agent Management System (AMS): the agent that ex-

ercises supervisory control over the access and use of

the platform; they are responsible for resident agent

authentication and record control. Agent 2) Com-

munication Channel (ACC): the agent that provides

the route for basic interactions between agents in and

out of the platform; it is the implicit communication

method that offers reliable and accurate service for

message routing. 3) Directory Facilitator (DF): the

agent that provides a yellow page service to the multi-

agent platform.

The standard also speciﬁes the Agent Communi-

cation Language (FIPA-ACL). Agent communication

is based on sending messages. It should be noted

that there is no restriction on the technology used for

the implementation of the platform; for example, for

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

134

Figure 3: FIPA compliant platform to which we added the

environment module; the communication with the environ-

ment module and the platform is carried out by message

exchange.

communication the system can use email, CORBA,

etc. For our framework we used the CORBA com-

ponent for platform-to-queue exchange offered by the

OS for internal platform communication.

In order to integrate our contribution (environ-

ment) into a FIPA architecture, we have chosen to use

the same messaging system to ensure the exchange of

data between the environment and the agents, the im-

age 3 shows how the environment integrates into the

FIPA multi-agent architecture.

From the beginning, we have wanted to provide

an easy-to-use architecture in the form of a frame-

work, and so we have provided a set of classes as a

dynamic C++ library (see image 4; classes in gray are

the classes that make up the framework). Users must

use this library to create agents and the environment

(see image 4, classes in white that the user must over-

load). The framework also offers a graphical interface

that allows the user to choose the attributes to display

in order to enable them to have a graphical tool that is

sometimes useful, particularly in the simulation and

analysis of complex phenomena. We have also inte-

grated some features that go beyond the scope of this

article. They consist of a mechanism that allows a

snapshot to be made and the ability to return to this

snapshot state if you need to change the simulation

parameters and compare the results.

Figure 4: This image illustrates the general class diagram

of the MAS platform; in gray are the set of classes that con-

stitutes the framework, while white classes are classes that

the user must add.

We are providing a new FIPA-compliant multi-

agent framework, written to simplify situated MAS

implementation. Our framework is written in C++

and is available under the GPL license, for which the

source code and documentation can be downloaded

here

5 USE CASE

This section presents a practical case that is difﬁcult to

solve using conventional MAS that does not take the

environment and its laws into account. Subsequently,

we will propose an approach to solve this problem

with the architecture proposed in this article.

As an example we will be able to look at the prob-

lem of navigation among movable obstacles, as de-

scribed in (Wu et al., ; Stilman and Kuffner, 2007).

The robot evolves in a congested environment with

different obstacles. The obstacles are more or less

movable or immovable according to their sizes, their

weights and their shapes. The robot aims to reach a

certain position. So, the robot must ﬁnd a path among

the obstacles and push the obstacles that hinder its

passage in order to pass. The solutions proposed in

(Levihn et al., 2013; Van Den Berg et al., 2009; Stil-

man and Kuffner, 2005) are essentially based on de-

liberative and non-reactive approaches, which is to

say by determining the complete path of the robot be-

tween an initial and ﬁnal position using the robot’s

vision. In the presented article, the emphasis is on

how to move the objects and not on ﬁnding the opti-

mal path of least resistance. We propose that the robot

make an internal representation of its environment,

and then perform simulations before acting. Indeed,

drawing its path in the same way but simulating ac-

tions before taking them in the physical environment

will give the robot the advantage of predicting the be-

havior of the other agents and said environment. We

want to propose a solution that uses the multi-agent

system proposed in this article to perform the simula-

tion and the choice of the action.

The robot wants to make an internal representa-

tion of its environment in the form of an MAS in order

to perform simulations before acting. The simulation

will allow it to anticipate the behavior of other agents

in the environment, and will also allow it to simulate

an action before choosing to apply it in the real en-

vironment. In this way, the robot in the environment

already has an idea of the results of an action before

even applying it. This will enable it to verify whether

the action applied to the environment produces the

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index.php/recherche/mas/gagent/

Environment Engine for Situated MAS

135

desired results in the long-term, thus reinforcing its

knowledge of the different environmental agents and

the laws that govern the environment.

The robot uses its various sensors to observe its

environment. For each object detected in the environ-

ment, the robot represents it as an agent in the MAS.

So, each agent represented in the MAS corresponds to

an object in the environment. Moreover, the robot en-

riches this agent with attributes and possible actions

to perform on this agent. For example, the robot ob-

serves its environment in which an obstacle hinders

its passage, the robot makes a representation in the

form of an MAS of its environment, it represents the

obstacle as being an agent which is at a distance (x,y)

with respect to it, and the agent is enriched with at-

tributes (shape of the object, size, weight). Of course,

if the robot does not know the exact characteristics, it

will make an estimate and check its database to see if

it is already familiar with said object. The robot also

enriches the environment by describing how this en-

vironment reacts through laws. In this article we limit

ourselves to describing the advantages of using the

ADM presented previously, and we do not describe

the methods used for obstacle detection and estima-

tion.

For the robot to apply the proposed solution, it

needs an MAS with an environment and agents.

6 DISCUSSION

In this section, we seek to compare our model of

the environment in relation to Ferber’s (Ferber et al.,

2004) well-known approach, namely AGRE (Agent-

Group-Role and Environment).

In their AGRE (Agent-Group-Role and Environ-

ment) architecture, Ferber and his colleague offer an

architecture centered around organization, in which

agents are organized into groups according to their

skills and the environment is a space that may be

physical (i.e. geometrical) or social. Physical

spaces (called areas) and social spaces represent AGR

groups. But, the environment plays the role of a phys-

ical medium to locate agents. Agents can do actions

in the environment through primitives.

We believe that this approach only allows the

modelling of phenomena for which the organization

is already known, or those which are already known

to be naturally organized. In our approach, however,

we model only the characteristics (attributes) and the

constraints (laws) of the system to bring out the orga-

nization. Moreover, for perspective on our work, we

want to put this model into practice in that the agents

must discover the environment as well as the laws that

govern it only through experimentation. We will give

a practical case for our model. The idea is to entrust

the task of modelling a multi-agent system to a ma-

chine that observes a real environment. The objec-

tive is for the machine to observe its environment and

build its own model, modifying the parameters until it

(and you) have obtained observations that you under-

stand to be the laws that govern the real environment.

So, the difﬁculty of our approach lies in the fact

that there is no generic way to represent the environ-

ment; we must ﬁrst deﬁne the components of the en-

vironment and the laws that govern it, and ﬁnally the

way in which it interacts with the agents that evolve

in it.

It is important to emphasize that we are forced

to propose a new implementation of a multi-agent

platform that integrates the two new elements of our

model: 1) the environment and its characteristics and

2) the attributes of the agents. We have experimented

with integrating these two elements into an existing

platform before; as JADE is a platform that is widely

used by the MAS community and is under an open

source license, which made it easy for us to add mod-

iﬁcations. In our previous proposal (Djerroud and

Cherif, 2018), we chose to propose an extension for

JADE without modiﬁcation of the existing code. Un-

fortunately, we were not able to integrate all the ele-

ments while respecting this latter constraint, and for

this reason we are proposing a new platform.

7 CONCLUSIONS AND

PERSPECTIVES

A study on the role of environment in MAS has taught

us that the environment is an implicit part of MAS; it

is often represented as integrated into agent commu-

nication or brokers. This leads to poor engineering

practices and prevents MAS users from harnessing the

full potential of the environment in MAS.

In this article, we present the environment as be-

ing an explicit part of MAS. In addition, we deﬁne the

environment as a ﬁrst-order abstraction in MAS that

has multiple roles: First, the environment is the part

of the world with which agents interact, and in which

agents’ effects will be observed and evaluated. Sec-

ond, the environment describes the world in which the

agents evolve through laws and attributes, and it has

the ability to act on the agents and modify their states

in order to respect the rules of the represented world.

Finally, we propose an implementation that inte-

grates an environment in order to provide a design

space that can be exploited by the designer and help

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136

manage the enormous engineering challenges of com-

plex MAS applications.

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