Large Intelligent Systems are so complex these days that an urgent need for designing such systems in best available way is evolving. Modeling is the useful technique to show a complex real world system into the form of abstraction, so that analysis and implementation of the intelligent system become easy and is useful in gathering the prior knowledge of system that is not possible to experiment with the real world complex systems. This paper discusses a formal approach of agent-based large systems modeling for intelligent systems, which describes design level precautions, challenges and techniques using autonomous agents, as its fundamental modeling abstraction. We are discussing Ad-Hoc Network System as a case study in which we are using mobile agents where nodes are free to relocate, as they form an Intelligent Systems. The designing is very critical in this scenario and it can reduce the whole cost, time duration and risk involved in the project.
Development teams during the system development life cycle, mostly uses functional analysis and data flow, or objectoriented modeling, which are not sufficient in many cases in themselves, to capture today's dynamic and flexible requirements of some of the current complex projects that are undertaken. Researchers are now seeking new methods and approaches that can help System designers to grapple with some of these problems. One of the new approaches that have been proposed is agent-based modeling. The fundamental notion on which Agent based engineering is autonomous agent. One Key reason to consider an agent as an autonomous system is capable of interacting with other agents in order to satisfy its design objectives, and a naturally appealing one for system designers [1]. Agent modeling in system engineering is a relatively young area, and there are, as yet, International Journal of Advanced Engineering Technology IJAET/Vol. I/ Issue I/April-June, 2010/95-103 no standard methodologies, development tools, or system architectures. System can be defined using multi agents as they can work concurrently to increase the performance of system. A design level strategy is needed to secure the fortune of designed system and to care all the coming problems in advance.
Agents also have their several different kinds of problems that need a different kind of treatment. Our work focus is on the design phase for large intelligent systems.
The idea of agents involves from artificial intelligence and neural networks.
Objects are passive in nature, without invocation message, mostly they never active [4], that is against the nature of Intelligent systems.
In object orient methods action choice is not defined, any member can invoke any publicly available object [4], while in Intelligent To handle the tremendous complexity and the new engineering challenges presented by intelligence, self learning, adaptive ness and seamless integration, developers need higher-level development constructs [7].
The Complexity of the system is obvious because synchronization and interaction (called as meet in agent modeling [8])
among the agents and different functionality of the system are tedious jobs to do.
Although we know many techniques from software engineering but still a better one is needed. There can be several issues in the agent based system design, but most of them will be system dependent that is applicable to only that particular application domain.
Here we are discussing main issues or say properties of agents that should maintain during the design and should continue till the implementation. These issues are above the design techniques [9]. We recommended designers to use modular and abstraction approach to reduce complexity. Hierarchical decomposition is also possible that depend • Agents are defined in the system to fulfill the system goal. They have their internal sub goals that they attempt to fulfill though their actions in the process. These sub goals are predefined in the system to access it or to do function according to them.
• Agents have the goals that are part of system goal. Agent can start some action to fulfill the goals, called proactivity. This is the action in advance, based on some information.
• Agents are an intelligent entity, so it can operate itself without the user interrupt. This property is autonomy. • Agents are the computational elements. They do computation, then do some action, reaction, and show intelligent behaviors, so their complexity will be high.
At design level, it should consider that user should unaware of complexity.
• Reactivity is the phenomenon that it perceives something from the environment and reacts to it. The reaction can be predefined or it can perform on its own. The reactivity should be according of the Proactivity. Proactivity is main goal of the system for which, all agents are working.
The reactivity of an agent can not be known priory, it’s a function of artificial intelligence that what it decide at that time.
At design time may be its not possible to address all these issue related to reactivity before implementation, But the Proactivity of a system should be clear and well defined.
In this paper we discuss the issues that have This kind of systems would be much helpful for military and other places where
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