A semantic web architecture for advocate agents to determine preferences and facilitate decision making

The world-wide-web (WWW) today consists of distinct, isolated islands of data and metadata. In the near future we expect the availability of a critical mass of data and metadata for use by intelligent agents that act on behalf of human users. These agents would identify, propose and capture new opportunities to assist human users in satisfying their goals, by traversing and acting on this semantically rich and abundant information. We envision a new class of agents, their networks and their communities that exist for the sole purpose of serving as their human "master's" Advocates - Advocate Agents. Advocate Agents learn a human's goals and preferences, collaborate with other agents, mine semantic content, identify new opportunities for action, propose them and finally transact them, while always keeping the human "in-the-loop." This paper discusses this class of distributed, intelligent, Advocate Agents, their potential uses, and proposed architectures and techniques that provide a conceptual framework for these networked agent societies to collaborate in the achievement of their human user's goals.

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