Behavioral Modeling of Multi Agent System: High Level Petri Net Based Approach

Behavioral Modeling of Multi Agent System: High Level Petri Net Based Approach

Rajib Kumar Chatterjee (Computer Centre, National Institute of Technology Durgapur, Durgapur, India), Neha Neha (Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India) and Anirban Sarkar (Department of Computer Applications, National Institute of Technology, Durgapur, India)
Copyright: © 2015 |Pages: 24
DOI: 10.4018/ijats.2015010104
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Modeling interactions between agents and the Multi-Agent System (MAS) behavior based on role based collaboration among the participating agents are the key factors to design of effective MAS dynamics. In this paper, a High level Multi Agent Petri Net called HMAP has been proposed which is capable of describing, analyzing and modeling dynamics of such MAS which are characterized as asynchronous, distributed, parallel and non-deterministic agent based systems. Proposed HMAP is also effective towards modeling roles, collaborations and interactions among the heterogeneous agents in MAS environment. Moreover the HMAP is useful in formal analysis of several behavioral properties of MAS like, Reachability, Home properties, Boundedness, Liveness and Fairness. The proposed mechanism has been illustrated using a suitable case study of Medical Emergency System. Moreover, to further validate the proposed concepts of HMAP, it has been simulated using Color Petri Net based tool called CPN Tool, with some restriction.
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Multi-Agent System (MAS) based computing promotes, designing and developing applications in terms of autonomous software entities (agents), situated in an environment, and that can flexibly achieve their goals by interacting with one another dynamically (Zambonelli et al., 2004). Besides autonomous nature, an agent exhibits several other crucial features including goals, capabilities, situatedness, proactive/reactiveness, knowledge driven, resource driven, event driven and heterogeneity; and which have been summarized in recent literatures (Zambonelli et al., 2004; Wooldridge et al., 2001; Biswas et al., 2008; Chatterjee et al., 2011) . Also dynamicity is the inherent characteristic for MAS due to event driven nature and features like autonomous and reactiveness. The initial state, knowledge and goals are set. MAS manage the things dynamically to achieve the preset goals. Coordination plays a fundamental role in MAS, since it allows agents to interact with one another in a productive way (Cabri et al., 2010). This can be achieved through the modeling of Interactions among the agents in the environment. Moreover, in MAS, each agent plays a specific set of Roles to interact with another agent or other environmental elements to achieve a pre specified goal. Further, the series of events and the responses to such events may occur dynamically in such system.

In this context an important challenge is to devise a mechanism to study the dynamic behavior of Agents in MAS at the design level. For such study and modeling of MAS behavior, it is to be ensured that, (i) system will achieve the goal with finite number of events and interactions, (ii) system will operate in deadlock-freeway, as the system will be handling the resources from the environment, (iii) system and environment should transform in acceptable states with the occurrences of events and interactions, (iv) the knowledge and the state of the resources are dynamically manageable. In view of these features, Petri Net (Murata et al., 1989) based approach is obvious choice for modeling of such dynamic behavior of MAS. Such Conceptual modeling of MAS is useful to study the architectural semantics and defines the components and their inter relationship to conceptualize the environment, agent, related events and interactions.

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