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The Pleiades Project has two overriding goals: 1) to develop distributed agent-based architectures that are composed of negotiating and learning agents, and 2) to apply these architectures to everyday activity management and information problems. To meet these goals, we have developed a number of software agent systems, including agents for managing personal calendars, web agents that provide guided tours of websites, and a visitor hosting agent system that manages the process of connecting faculty members with campus visitors who share similar interests.

Our approach is committed to developing machine learning methods that will endow personal software agents with the ability to customize automatically to the needs of their users.

The Calendar Apprentice
The Calendar Apprentice (CAP) is an interactive assistant that acquires knowledge through routine use by observing users' actions. CAP provides an editing and email interface to an online calendar. It learns users' scheduling preferences through routine use, enabling it to give customized scheduling advice to each user. Each night, CAP automatically runs a learning process--based on a method of decision tree induction--in order to refine the set of rules it uses to give scheduling advice. One of the advantages of this approach to machine learning is that the rules in CAP are typically understandable to users, which may encourage user editing and evaluation.

The WebWatcher
WebWatcher, a learning apprentice like CAP, is a tour guide agent for the Web that accompanies users and offers suggestions about where to go next. Given that users often feel disoriented when encountering websites for the first time, the WebWatcher acts as a guide, offering suggestions based on its knowledge of each user's interests, the location and relevance of various texts available, and the ways in which previous users have interacted with the website. As WebWatcher accompanies the user, it offers suggested links by adding eyeball icons to its selections. The WebWatcher agent learns to suggest appropriate links for users by analyzing the training examples of previous tours, in which user-selected links are annotated with the keywords of users who chose them. A second learning strategy involves reinforcement, in which the agent augments a given link using words encountered in pages downstream of it. A third approach to learning combines these two approaches, and has been shown to be more effective than other learning methods.

The Visitor-Hoster
The Visitor-Hoster system is designed to help a human secretary organize a visit in an academic environment. According to the demands of this task, the visitor's schedule must be arranged to accommodate the schedules of faculty members who share interests with the visitor. We have developed a layered architecture to meet the demands of hosting a campus visitor. This architecture deploys task-specific software agents that help users perform tasks by communicating with each other and/or querying and exchanging information with information-specific agents. Task agents include a Personnel Finder agent that locates information about the visitor, a Scheduling Task agent that manages the visitor's calendar of meetings with faculty, and various Calendar Apprentice (CAP) agents that manage and interact with faculty calendars. Information agents engage databases and other sources of information, as in the Interest Agent that locates faculty members whose interests match the visitor's areas of interests.

Thales is a successful application of Retsina multi-agent technology that integrates three sources of information to make a prediction of satellite visibility:

  • Geographical coordinates of the region of observation;
  • Weather forecast websites for the region of observation;
  • Passage of visible satellites over the area at the specified time.

    Click here for a demonstration.

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For more information, visit the Pleiades Project Home Page.



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