Method for Verification Of A Multi agents System

Abstract— In this paper, we present a new formal Method for the Verification of a Multi agents Systems (MAS). We explain and applied a new method based on agents called Agents Petri Nets (APN). Our model describes each entity to model the system and verify the state of the agent and its behavior. This method use, mainly, the property of Petri Nets as deadline, state home, conflict, etc.. Therefore we use a formalism having a mathematical definition in order to verify some property of the interactive systems. To validate our contribution, we will deal with real example of a Multi agents System.

Keywords-component; Method, Multi agents Systems, Agent Petri Nets , Verification, Architecture

I. INTRODUCTION

The verification of MAS requires toll to check first characteristics and properties of agents, then, those of the system itself.

The increasingly intense of technologies and due to the explosive growth of Internet, developers are forced to find ways to lower the building system costs, create products in shorter times with higher quality, and deliver them on time [1]. In this context, the Multi agents paradigm has attracted much attention during the recent years [2].

In order to describe the behavior and the interactions of the entities of the system or the constraints on the variable characteristics of the system, we should make a dynamic modeling.

This type of system has grown into what is now one of the most active areas of research and development activity in computing. Many researchers have applied Petri nets (PN) to specify the multi-agent domain [3].

Others presented different approaches for modeling and verifying multi-agents behaviors using State/Transition [4]. Others, [5] and [6] presented a formal approach for modeling and verifying multi-agent plans using model checking and Petri Nets. Others researches use recursive Petri Nets to model multi-agent plans [7].

There are other works and approaches for multi-agent modeling and verification using formal approaches [8]. This paper builds a Petri nets model based interaction between agents, and analyzes the multi agents behaviors.

In fact Petri Net is a mathematical method [9] which has stronger ability to describe the dynamic property of

system perfectly, and its graphic favorite it to be more easily to be understood and accepted.

The remaining of this paper is organized as follows. The second section invokes the Multi agents system and its characteristics. Besides, the third section we explains the formal methods. The section fourth presents our formalism Agent Petri Nets. In fifth section, when we present an example of verification using the new Architecture of Agent. Finally, this document will be concluded, giving some perspectives.

II. MULTI AGENTS SYSTEM

A multi agent system permits to coordinate the behavior of agents, interacting and communicating in an environment.

A multi agents system is a system composed of multiple interacting agents, used to solve problems which are difficult or impossible for an individual agent to solve [5] and [10].

Agents are entities within an environment that they can sense and act. Several works have attempted to provide the attributes that agents might has.

In a MAS, interaction, communication, negotiation are required in order to achieve the goal. A simple agent is ready for collaboration [12]. No agent has a full global view of the system [13].

The quality of software is evaluated to the satisfaction of requirements recommended by both the user and the environment; a good system should maintain a consistent and correct behavior. Indeed, the shift to the realization may be possible only if the specification is well validated by comparison with the expected features of the system, hence the need for development of tools, formalisms to perform verification and validation of the model.

In this paper, we propose a formal framework for verification of agent-based applications. This ensures that each agent fulfills the role required. We are interested by agent within his group. The resulting system can be checked against requirement expressed in an APN Model.

Like most complex systems, the general issues of software engineering must be taken into account. For modeling multi agent systems, particularly, must take into account the ease of design and the validity of the model to ensure effective implementation.

Borhen MarzouguiISET Mednine

B.P. : w – 231 Gabès 6000 TUNISIA

marzougui_bor@yahoo.fr

Khaled Hassine Faculty of science, Gabès

Erriadh City, Zrig 6072 Gabès Tunisia

Khaled.hassine@gmail.com

Kamel Barkaoui CNAM, 292 Rue Saint-Martin

Paris 75 141, Cedex 03 Paris, France

kamel.barkaoui@cnam.fr

2011 Second International Conference on Intelligent Systems, Modelling and Simulation

978-0-7695-4336-9/11 $26.00 © 2011 IEEE

DOI 10.1109/ISMS.2011.21

62

2011 Second International Conference on Intelligent Systems, Modelling and Simulation

978-0-7695-4336-9/11 $26.00 © 2011 IEEE

DOI 10.1109/ISMS.2011.21

62

The existing verification methods, is developed for simple programs or systems for classical rules are not implicitly apply to multi-agent systems whose entities interact in a particular way.

Verification of the consistency model and its compatibility is very important, it remains an area of research attracting increasing attention from researchers who are trying to identify the basic standards of verification which means that the system should be conform to its specification[22]. Validation means that the system must do what the user really requires [14]. According to [15], there are two ways to examine and evaluate MAS: see society of agents for global overview or individual view based on the characteristics of a single agent whose properties affect the system behavior. [16], [17] approve that verify a model system returns to ensure the following factors:

• Sinfulness of the model: having functional results in translating,

• Accuracy: the structure model and the expected results should be similar,

• Simple: having the least complicated model that is able to reflect the complexity of reality,

• Quickness: ensuring that the best properties remain true,

• Stability: the model is insensitive to secondary factors,

Therefore, we say that a MAS model is consistent and coherent if the communication and interaction between agents results in a non-blocking system and do not lead to the deadlock when two agents. For example, A1 and A2 are found simultaneously in a state where A1 is awaiting a resource or a message sent by A2 and vice versa, also agent shouldn’t remain in a forged state.

III. FORMAL METHODS

The formal methods have been used to assure a level of precision, consistency and quite elevated accurateness. They are based on mathematical foundations to decrease the risks of uncertainty and vagueness. In the phase of the software conception, the formal methods help a particular language to express the properties descended of the problem specifications very rigorously [8].

However, these methods use specific notations and concepts which often generate a weak legibility and a difficulty of integration in the development processes and certification [9]. The formal specifications are expressed in languages using syntaxes and quite precise and strict semantics. The automatic validations result from a solid theoretical base, thus the integration of several formal methods is indeed difficult [10].

A formal specification can bring many advantages. Actually, the methods are richer and permit a better representation of the static and dynamic aspects of the system [10]. It is possible to classify the methods of formal specification into two groups. Firstly, approaches based on

the states as Z [11], object - Z [12], B [13], VDM [14], etc. Such approaches represent the system by two parts: the first is static, which permits the description of constituent and their states, while the second is dynamic, which describes the changes of states.

Secondly, approaches based on the events and they describe the system by processes or communicating independent entities. Among these approaches we can mention: the Petri Nets [1], LOTOS [15], CSP [16], CCS [17], etc.

IV. FORMALISM AGENT PETRI NETS:APN

The usual Petri Nets as those of Object (OPN), Place/Transition (PT) and Colored suffer from a lack of expression to describe a system of large size characterized by the interactivity of the elements that they compose such as the multi agents system.

The aim of our work is to use a combination of joint multi-agent paradigm and Petri nets as defined in [21]. An agent is defined as an autonomous entity able to communicate with other agents [22].

A. Definition In a formal manner, Agent Petri Net [20] is defined by the 9-uplet: Q = <P, T, A, Meadow, Post, Prj, F, Ft, Envk>Where:

• P is a non-empty finished whole of places; • T a non-empty finished whole of of transitions; • A is a non-empty finished whole of agents; • Meadow: PxT N an application of front

incidence; • Post: PxT N a back application of incidence

corresponds to the arcs; • Prj: pre condition of firing; • F (Ai, Aj): agent relation function presenting the

condition of firing; • Ft: function agent that uses three variables :

Ft (tk) = <Per, value, Inter>; • Envk: Environment of work that describes multi

agents system.

B. Modeling rules At an instant t, an agent should be engaged in only one

environment Env. The agent should respect the precondition of crossing a transition Ti.

The relationship between the agents that they are already engaged in an environment Envj are described by a function witch described the criterion for adherence, and the number of times that this agent has been engaged in Envj.

The relationship between two agents that communicate is defined by a function (Ft) that described the data interchange and the behavior of each of them: Ft (tk) = <Per, Inter, Value>.

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C. Architecture of Agent Petri Nets In general, we define the Architecture of Agent in APN

by the following model:

Figure 1: Architecture of Agent in APN

Where:

ident: Index of the agent in question, Envj: name of the environment, d: degree of membership of the agent Ai in its environment Envj, df: number of times crossing the agent Ai, this value will be incremented after each crossing, Pk: current position of the agent, Pp: set of places already visited by the agent Ai, Ar: set of agents that are outstanding communication with Ai, F: value returned by agent relationship, Tt: Number of transition that is being crossed, Per: Value Per Service Agent, Value: value contained function agent, and describes the action performed, Inter: Inter value of the function agent.

The definitions we have made in previous sections lead us to define a new architecture of an Agent. In APN, each token (Agent) must have a number of properties and characteristics. While respecting the autonomy, intelligence, responsiveness, heterogeneity and sociability, in proposing a model form of new agent architecture. This model not only presents the internal structure of agent but also the location in an environment, its behavior, its relationship with other agents, its history of actions, its membership degree.

The operation of this model allows us to easily describe and analyze the functioning of multi-agent systems.

V. EXAMPLE OF VERIFICATION

To better understand the tasks of verifying APN, we take the example of a simple application that represents an

editor at APN. This application allows the user to draw graphical shapes in space that are drawing the square, arc and transition. The editor is composed of two elements: the palette to select object graph and create the space which will be positioned on drawing objects created. This application includes five agents:

• A1: Agent graphical editor, • A2: Agent palette, • A3: Agent canvas, • A4: Agent dialogue1 box, • A5: Agent dialogue2 box.

In this example, we will have the division of responsibilities of these five agents for carrying out different tasks. Links between agents are not necessarily channels. They can also translate relations of decomposition, that is to say that software design operations are encapsulation and refinement.

In Figure2, we present the Architecture of Agent “A1”

after firing T9.

A1

Env1 d=1 df1=6

P9 P1, P2, P3, P4, P6

A5 F=1

T9 Per=1 A5.IndexAgent [1] Inter=1

T2 : A2.TElement[1]

T3 : A1.TElement[1]

T5 : A4.NbreJeton

T6 : A1.NbreJeton

T8 : A1.TElement[2]

Figure 2: Architecture of Agent “A1” in APN

VI. CONCLUSION

We defined the framework of our work by expressing our position compared to works which treated the formal methods for verification of the multi agent systems.

In this paper we proposed a Method for specifying and verifying multi agents applications. Our contribution consists in defining a formal method to describe static and behavioral aspects of agents as well as collective aspects of multi agent systems. This method is based on Petri Nets Agents. Indeed, the modeling of MAS requires toll to check first characteristics and properties of agents, then, those of the system itself. Moreover, we defined an approach that helps to specify multi agents applications in a constructive way.

ident

Envj d df

Pk Pp

Ar F

Tt Per Value Inter

History

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In addition, Agent Petri Nets can verify two processes precisely and reveal internal structure of system. Therefore, via this method, the efficiency and exclude of comportment can be analyzed clearly.

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