=Paper=
{{Paper
|id=None
|storemode=property
|title=Programming Electronic Institutions with Utopia
|pdfUrl=https://ceur-ws.org/Vol-592/PaperDemo08.pdf
|volume=Vol-592
|dblpUrl=https://dblp.org/rec/conf/caise/SchmittBAG10a
}}
==Programming Electronic Institutions with Utopia==
https://ceur-ws.org/Vol-592/PaperDemo08.pdf
Programming Electronic Institutions with
Utopia
Pierre Schmitt, Cédric Bonhomme, Jocelyn Aubert, and Benjamin Gâteau
Centre de Recherche Public Henri Tudor
Service Science and Innovation Dpt.
Luxembourg, G.D. of LUXEMBOURG
{firstname.lastname}@tudor.lu
http://www.tudor.lu
Abstract. In Multi-Agent Systems, Organizations are means to struc-
ture cooperation and collaboration between agents. MoiseInst is a nor-
mative Organization model giving the possibility to constraint agents
behaviour according to four dimensions (structural, functional, contex-
tual and normative). Mabeli as Electronic Institution model allows the
supervision of MoiseInst Organizations compliance through an arbitra-
tion system. The difficulty is to easily instantiate such Organizations
to obtain a dynamic entity in which agents can evolve. In this paper
we introduce Utopia, our Institution-oriented and Institution-based pro-
gramming framework. Utopia permits to easily and automatically set
up a MAS thanks to a XML MoiseInst Specification file. The frame-
work convert this file into an innovative mathematical structure namely
a recursive graph, and solve several optimization problems in order to
compute the most efficient role distribution. We show a concrete appli-
cation of the prototype through RED, an EUREKA/CELTIC European
project use-case.
1 Introduction
In human societies, Institutions define rules [1] that enclose all kinds of formal
or informal constraints used by human beings to interact. In Multi-Agent Sys-
tem domain, Electronic Institutions have been introduced to model rules with
normative systems [2]. That is why we define Electronic Institutions as a set
of agents which behave according to Norms and by taking into account their
possible violation (and sanction).
These last years Electronic Institution platforms have been improved thanks
to new services making them able to express cooperation schemes defined by the
user with an Organization Modelling Language such as for instance Moise+ [3],
Islander [4], OMNI [5]. The aim of these services is to constraint and supervise
agent’s actions and interactions in order for them to achieve some global Goals.
We call those explicit cooperation schemes Orgazination Specification (OS).
The model used to specify the organization of an Electronic Institution is
MoiseInst [6]. In this context, the functioning of the agents is supervised and
2 Programming Electronic Institutions with Utopia
controled with a set of Institution services regrouped in a specific “normative
middleware” called Synai on which the agents execute themselves.
This paper aims at presenting how it is possible to easily implement an
Electronic Institution specified with MoiseInst , supervised with Synai and
in which standard agents provided with the platform evolve and achieve their
Goals. For that, three steps have been needed:
1. Define the structure of data in which the OS will be stored.
2. Develop a set of agents working in and able to supervise an Organisation
Entity (OE) instantiating the OS defined by an user.
3. Develop a template of JADE based agents able to evolve in the OE (i.e. able
to play Roles and achieve Goals) by loading specific behaviours provided by
the user in order to execute actions achieving the Goals defined in the OS.
The paper is built as follows: in Section 2 we present rapidly MoiseInst and
Synai composing the foundations of our work. Section 3 deals with the im-
plementation of the framework (named Utopia) allowing the implementation of
such Electronic Institution. At last, before conclude, the Section 4 illustrates the
use of Utopia through an application of security policies deployment developped
in the context of European RED project.
2 Normative Organization Modelling
MoiseInst [6] is founded on the Moise+ organizational model [3]. It is com-
posed of the following components that are used to specify an Organisation of
agents in terms of structure, functioning, evolution and Norms (see Figure 1):
Fig. 1. MoiseInst , a normative Organization Specification model
– A Structural Specification (SS) defines: (i) the Roles that agents will play in
the Organization, (ii) the relations between these Roles in terms of author-
ity, communication or accointance, (iii) the Groups, additional structural
primitives used to define and organize sets of Roles;
– A Functional Specification (FS) defines global business processes that can be
executed by the different agents participating to the Organization according
to their Roles and Groups;
Programming Electronic Institutions with Utopia 3
– A Contextual Specification (CS) specifies, a priori, the possible evolution of
the Organization in terms of a state/transition graph;
– A Normative Specification (NS) defines the deontic relations gluing the three
independant Specification (SS, FS, CS). This NS clearly states rights and
duties of each Roles/Groups of SS on sets of Goals (Missions) of FS, within
specific states of CS.
These four Specifications form the Organizational Specification (OS). The
Organizational Entity (OE) is then built by instantiating the OS through the
Agent playing roles, achieving goals and respecting active norms in valid con-
texts. The Synai [7] middleware manages and controls the functioning of this
OE . As depicted on Figure 2, Synai is composed by a set of manager agents
supervising the actions of agents “Agt” on the OE.
Fig. 2. Supervision by Synai of an OE
This layer is in charge of: (i) managing the life cycle of SS as entering/exiting
of agents within the Organization, or requesting/leaving of Roles or Groups by
the agents, (ii) coordination of the concurrent execution of FS as commitment
to Missions or achievement of Goals, etc, (iii) dynamic and evolution of the
Organization state through the CS, (iv) the monitoring and supervision of Norms
of NS activated/deactivated by the evolution of the Organization.
While agents evolve inside the organization, agents of Synai have to interpret
and “understand” the OS (in order to respect it or to control it). For that, we
need to structure the data of the organization and to this end, we chose recursive
graph.
3 Implementation of Utopia
Recursive graphs are innovative mathematical structures [8] widely used to have
a very generic representation of data. In our case, a recursive graph particularly
4 Programming Electronic Institutions with Utopia
meets the underlying needs of MoiseInst which is mostly recursive : Groups can
include others Groups, Missions can include others Missions, etc... Moreover, the
sub recursive graph extraction makes the data sharing more easier.
Utopia and its architecture using an Electronic Institution paradigm make
the essential problematics of setting up a Multi-Agent System easier. Indeed two
steps are needed:
1. Define the OS in a XML file (an authoring tool to specify the OS will be
developped later).
2. Develop specific behaviours (in java classes) that the generic agents will load
in order to execute actions achieving the goals defined in the OS.
4 Demonstration scenario
Our use-case is part of a demonstrator set up in the context of the RED project [9]
which defines and designs solutions to enhance the detection/reaction process,
improves the overall resilience of IP networks to attacks by embedding means to
enrich the alert with better characterized information, and additional informa-
tion about the origin and the impact of the security incident.
To provide the detection and reaction functionalities, RED proposes an ar-
chitecture containing a set of elements, depicted in Figure 3:
– ACE (Alert Correlation Engine): this element is in charge to receive alerts
from network nodes, and enhances the detection of attacks by combining
several diagnosis combinations.
– PIE (Policy Instantiation Engine): this element receives the information
about attacks from the ACE and instantiates new security policies to re-
act to the attack in a high level reaction loop. This paper is focused on this
element.
– PDP (Policy Decision Point): this element receives the new security policies
defined by the PIE and deploies them in the enforcement points.
– RDP (Reaction Decision Point): this element receives the information about
attacks from the ACE and decides of how to act in a mid level reaction loop.
– PEP/REP (Policy Enforcement Point/Reaction Enforcement Point): This
component, outside the RED node, enforces the security policies provided
by the PDP and the reaction provided by the RDP. It also performs an
immediate low level reaction.
RED proposes three different types of reaction based on level of diagnosis
required to apply them:
– Immediate reaction, which is an automatic response with a diagnosis based
on the capabilities embedded in the device and decided by the PEP/REP,
– Short term reaction, where the diagnosis is done with a limited and local
vision of the monitored information system, decided by the RDP based on
the information provided by the ACE and which does not instantiate new
security policies,
Programming Electronic Institutions with Utopia 5
Fig. 3. RED architecture
– Long term reaction, where the diagnosis is done with a global vision of the
monitored information system, decided by the PIE and which generate new
security policies based on the ACE alerts which are sent to the PDP to
deploy them in PEP.
A multi-agent system is used to represent RED nodes. Each component is
represented by an agent playing a Role (ACE, RDP, PIE, PDP, REP, PEP) of
the node which is represented as a MoiseInst Organization. In the following, we
will describe the Goals that agents have to achieve in a context of a black-hole
attack.
4.1 Black-hole attack and countermeasures
In our scenario Alice and Bob are communicating with help of a VoIP service
provided by a SIP server. A Malicious node executes an attack structured in two
successive steps. First, the Malicious node changes the ARP tables of Alice, Bob
and the SIP Server (ARP poisoning) in order to have all the trafic routed by
itself. Then, it carries out a black-hole attack by dropping (not retransmitting)
the packets. As a result, the conversation between Alice and Bob cannot progress.
Once the attack succeeded, an intrusions detection tool detects the attack
and sends alerts to the PIE and the RDP through the ACE. The agent playing
the Role of RDP have to apply a short term reaction by asking PEP to delete
their ARP entries corresponding to the MAC address of the malicious node. The
agent playing the Role of PIE aims at implementing new policies forbidding the
input and the forward of trafic coming from the malicious node (via its MAC
address) and adding static ARP entries binding the real IP addresses and MAC
addresses. Then the PIE agent sends these new policies to PDP which transform
them into script and/or executable command regarding to PEP’s specifications
(type, host, OS, etc.). At last, agents playing PEP Role have to execute command
6 Programming Electronic Institutions with Utopia
and/or scripts on the device they interface. We will see more precisely in the next
section how an Organization is implemented with Utopia in order to represent
a RED node as an Electronic Institution.
4.2 Implementation with Utopia
Utopia make possible to easily deploy a MAS where agents play the appropriate
Roles, namely ACE, PIE, PDP and PEP from a simple Structural Specification.
Thanks to cardinalities, the MAS composition can respect the RED architecture
: ACE, PIE and PDP are played by only one agent and PEP are distributed
over the network devices.
We can handle the agent behaviour after an attack with a simple Functional
Specification : four Missions (one for each agent) composed by two Goals run
in parallel, one dedicated to messages reception, the other to message sending.
The following shows Domain Knowledge Specification of the goals binding them
to their corresponding java classes that the user have to provide, and the FS
coming from the OS XML file. There is no grouping of goals in missions, that’s
why the FS is so simple.
[...]
[...]
gPIESend
gACESend
gPDPListen
gPEPListen
gPEPIPListen
The Normative Specification only force the four agents playing the Roles of
ACE, PIE, PDP and PEP to do their associated Missions, that is to say, to run
two Java Goal implementations. Obviously, each Goal implementation allow the
specialization of the agents, and thanks to Utopia’s primitive functions, it is very
easy to send or receive messages and XML alerts.
5 Conclusion
In this paper we described an Electronic Institution programming framework
named Utopia based on MoiseInst for the Organization Specification and on
recursive graph for the Organization representation. Thanks to a recursive graph,
Programming Electronic Institutions with Utopia 7
all the homogeneous data are stored in an unique recursive structure, allowing
us to easily distribute the shared information between agents of Utopia using
concepts such as sub-recursive graphs.
With the RED use-case we showed how easily the essential problematics of
setting up a Multi-Agent System could be solved with Utopia and its powerful
architecture using an Electronic Institution paradigm. Actually Utopia allows
to simply deploy a MAS without any need of network programming (as Socket
coding or thread management). Furthermore, with this kind of network abstrac-
tion, the implementation of RED is completely reusable: we can run the system
on many different networks. Moreover, it is far easier to brings into the MAS de-
velopment many security specialists, as Electronic Institution permits to clearly
separate the different system Goals and thus, the different security problematics.
Despite the easiness of implementing a working Electronic Institution that
Utopia brings, as demonstrated in a real use-case, some improvements can be
considered. Actually, the way of managers and supervisor to control the function-
ning of the organization is basically a centralized arbitration system. However
the multi-agent system principles advocate decentralization. As a consequence,
a first evolution could be done in order to obtain an Electronic Institution al-
lowing the distribution of the OE and Synai without putting the optimization
of the role distribution aside. Moreover, the agents’ decision taking mechanisms
could be improved to exhibit a smarter behaviour in order to choose the right
Goals to achieve at the right time more efficiently.
Acknowledgment This work has been funded by Luxembourger FNR-CORE
project TITAN (C08/IS/21).
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