Nearly Monotonic Problems: A Key to Effective FA/C Distributed Sensor Interpretation?

The functionally-accurate, cooperative (FA/C) distributed problem-solving paradigm is one approach for organizing distributed problem solving among homogeneous, cooperating agents. A key assumption of the FA/C model has been that the agents' local solutions can substitute for the raw data in determining the global solutions. This is not the case in general, however. Does this mean that researchers' intuitions have been wrong and/or that FA/C problem solving is not likely to be effective? We suggest that some domains have a characteristic that can account for the success of exchanging mainly local solutions. We call such problems nearly monotonic. This concept is discussed in the context of FA/C-based distributed sensor interpretation.

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