We are investigating techniques for developing distributed and adaptive collections of agents that coordinate to retrieve, lter and fuse information relevant to the user, task and situation, as well as anticipate a user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for constructing agents. We call this reusable multi-agent computational infrastructure RETSINA Reusable Task Structure-based Intelligent Network Agents. It has three types of agents. Interface agents interact with the user receiving user speci cations and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We h a ve implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making the PLEIADES system, and nancial portfolio management the WARREN system.
The Retsina agent architecture REF defines three type of agent classes: interface agents, task agents and information agents.
Distributed intelligent agents
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Computer Science