Abstract
In the mid-1990s, the Earth Observing System (EOS) will generate an estimated 10 Terabytes of data per day. Making full use of this enormous amount of data will require sophisticated technologies from real-time distributed Artificial Intelligence (AI) and data management. Without regard to the overall problems in distributed AI, this paper focuses on developing efficient models for doing query planning/scheduling in intelligent user interfaces that reside in a network environment. Before intelligent query/planning can be done, a model for real-time AI planning/scheduling must be developed. As connectionist models (CM) have shown promise in decreasing run-times, this paper proposes a connectionist approach to AI planning/scheduling. The solution involves merging a CM rule-based system to a general spreading activation model for the generation and selection of plans. A system has been implemented in the Rochester Connectionist Simulator and runs on a Sun 3/260.
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