Tracking and data association
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
A framework for the analysis of sophisticated control
A framework for the analysis of sophisticated control
A Framework for the Analysis of Sophisticated Control in Interpretation Systems
A Framework for the Analysis of Sophisticated Control in Interpretation Systems
A unified approach to dynamic coordination: planning actions and interactions in a distributed problem solving network
Autonomous Agents and Multi-Agent Systems
Policies for sharing distributed probabilistic beliefs
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Information sharing for distributed intrusion detection systems
Journal of Network and Computer Applications
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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.