A mobile agent is an executing program that can migrate during execution from machine to machine in a heterogeneous network. On each machine, the agent i n teracts with stationary service agents and other resources to accomplish its task. Mobile agents are particularly attractive in distributed informationretrieval applications. By moving to the location of an information resource, the agent can search the resource locally, eliminating the transfer of intermediate results across the network and reducing end-toend latency. In this chapter, we rst discuss the strengths of mobile agents, and argue that although none of these strengths are unique to mobile agents, no competing technique shares all of them. Next, after surveying several representative mobile-agent s y s t e m s , w e examine one speci c information-retrieval application, searching distributed collections of technical reports, and consider how mobile agents can be used to implement this application e ciently and easily. Then we spend the bulk of the chapter describing two planning services that allow mobile agents to deal with dynamic network environments and information resources: (1) planning algorithms that let an agent c hoose the best migration path through the network, given its current task and the current network conditions, and (2) planning algorithms that tell an agent h o w to observe a c hanging set of document s i n a w ay that detects changes as soon as possible while minimizing overhead. Finally, we consider the types of errors that can occur when information from multiple sources is merged and ltered, and argue that the structure of a mobile-agent application determines the extent to which these errors a ect the nal result.
Information push and information pull have recently emerged as useful concepts to describe the operation of distributed information resources. Information push, in particular, is becoming closely associated with intelligent agent functionality. Loosely speaking, if a user requests and receives a very speci c piece of information, this is information pull. If information is sent i n anticipation of the user's need, or the agent's response includes information not directly solicited, then the situation is characterized as information push. Intuitively, junk mail electronic or paper , television newscasts and wirefeeds are examples of information push. New web services such a s P ointcast and Informant are examples of more selective push technologies. Web browsing, library searches, and telephone white pages are traditional examples of information pull. Clearly, these categorizations can be ambiguous and are easily lost in semantics. The main goal of this paper is to formalize these concepts and describe a mathematical framework around which further work can be more precise. Speci cally, w e develop a stochastic framework based on Markov models to describe an ambient e n vironment and an agent system. Depending on the relationships between the environment, the agent and the user's performance criterion, a continuum of possible information push and pull scenarios can be described. Some basic analytic results concerning the operation of a push pull information system are derived.
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