β Scribed by Omer F. Rana; David Kotz
No coin nor oath required. For personal study only.
Rapid advances in technologies such as networking, parallel computing and information management have led to the development of intelligent software components that act autonomously on the behalf of users, can analyse and access a diverse range of information, can react to changes in their environment, and can cooperate and coordinate their activities to complete a task or goal. Such components may be distributed across a network, and may work seamlessly to perform this goal, and do so without the direct intervention of a user. Such technology involves the integration of ideas from many different disciplines, such as artificial intelligence, parallel processing, knowledge sharing, object-oriented design, information retrieval, distributed workflows, and databases. The objectives of developing such systems are to provide an emergent functionality that allows a robust, flexible, and scalable approach to solving problems and providing services in various application domains.
In addition to advances leading to the development of such components-agents-a similar change of emphasis is occurring in parallel and high-performance computing. Such a change is driven by the rapid uptake of commodity computing technology in high-performance computing applications, such as general purpose processors employed in desktop machines and distributed software objects, for instance. Also, recent interest in large-scale distributed-computing environments, or computational grids, that provide dependable, consistent, and pervasive access to high-end computational resources, suggest the use of an agent as a more appropriate abstraction. These environments have the potential to change fundamentally the way we think about computing, as our ability to compute will no longer be limited to the resources we currently have at hand. The ability to integrate large-scale computing resources, on demand, will enable integration of sophisticated data analysis, image processing, and real-time control to be utilized within scientific instruments, simulations, and manufacturing. Additionally, resources of a nationwide, or continent-wide, strategic-computing reserve may be used to perform time-critical computational tasks in times of crisis, for example to perform diverse simulations as we plan responses to an oil spill. Agent-based computing is therefore the next obvious step. The use of techniques such as code mobility and speech-acts (through KQML and FIPA) open up new research challenges when used with approaches and themes familiar to high-performance computing. The combined use of commodity computing ideas and agent technologies could lead to new applications, in areas such as resource management, data mining, data warehousing, and electronic commerce.
Many issues remain in bringing about this change, however, such as understanding how agents can be deployed on a large scale, and how to build effective 'agent communities'. In agent systems with a large number of agents, or where the agent environment is highly dynamic or heterogeneous,
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