In order for the agent-based applications to be truly autonomous and decentralized, heterogeneous multi-agent systems themselves must communicate and interoperate with each other. To solve the problem, we take a two-step approach. First, message-level interoperability is realized by a gateway agent that interconnects heterogeneous multi-agent systems. Second, higher-level interoperation of conversations, which consist of bi-directional streams of messages, is achieved by dynamically negotiating the interaction protocols. We demonstrate the concept, technique and implementation of integrating multi-agent systems and show how the method improves the assurance of real-world applications in autonomous decentralized systems.

Wireless technology has become widely popular and an important means of communication. A key issue in delivering wireless services is the problem of congestion which has an adverse impact on the Quality of Service (QoS), especially timeliness. Although a lot of work has been done in the context of RRM (Radio Resource Management), the deliverance of quality service to the end user still remains a challenge. Therefore there is need for a system that provides real-time services to the users through high assurance. We propose an intelligent agent-based approach to guarantee a predefined Service Level Agreement (SLA) with heterogeneous user requirements for appropriate bandwidth allocation in QoS sensitive cellular networks. The proposed system architecture exploits Case Based Reasoning (CBR) technique to handle RRM process of congestion management. The system accomplishes predefined SLA through the use of Retrieval and Adaptation Algorithm based on CBR case library. The proposed intelligent agent architecture gives autonomy to Radio Network Controller (RNC) or Base Station (BS) in accepting, rejecting or buffering a connection request to manage system bandwidth. Instead of simply blocking the connection request as congestion hits the system, different buffering durations are allocated to diverse classes of users based on their SLA. This increases the opportunity of connection establishment and reduces the call blocking rate extensively in changing environment. We carry out simulation of the proposed system that verifies efficient performance for congestion handling. The results also show built-in dynamism of our system to cater for variety of SLA requirements.

Grid computing is an open, heterogeneous and highly dynamic environment based on the principles of service oriented computing. It focuses on basic infrastructure for coordinated resource sharing among virtual organizations to achieve high performance and availability. However, use of existing Grid computing environment is quite complex and requires a lot of human intervention. In order to avoid this intervention, enhancements are required in bringing autonomy and semantics in existing Grid infrastructure. Semantics would act as glue for autonomy in the process of efficient resource discovery and utilization. Several ontologies and ontology languages have been proposed in this regard which not only have some shortcoming but also poses a sort of overhead for the Grid environment. On the other hand, agents are autonomous problem solving entities, and can negotiate semantically for interoperation with each other in dynamic environments. Inspired from the concept of Autonomous Decentralized Systems, we propose that the above mentioned goals can be achieved by integrating FIPA Multi Agent Systems with the Grid Service Architecture and hence to lay the foundation for Autonomous Semantic Grid. Autonomous Semantic Grid system architecture is aimed to provide an improved infrastructure by bringing autonomy, semantic interoperability and decentralization in the Grid computing for emerging applications. This paper then presents implementation details of first milestone toward Autonomous Semantic Grid realization based on a middleware, namely AgentWeb Gateway for integration of Multi Agent Systems and Grid Service Architecture. Evaluation of the system has also been performed over a number of application scenarios.