TY - GEN
T1 - Prediction market-based information aggregation for multi-sensor information processing
AU - Jumadinova, Janyl
AU - Dasgupta, Prithviraj
N1 - Funding Information:
1 This research has been sponsored as part of the COMRADES project funded by the Office of Naval Research, grant number N000140911174.
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2013.
PY - 2013
Y1 - 2013
N2 - Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the event’s outcome. We consider an analogous problem of information fusion from multiple sensors of different types with the objective of improving the confidence of inference tasks, such as object classification. We develop a multi-agent prediction market-based technique to solve this information fusion problem. To monitor the improvement in the confidence of the object classification as well as to dis-incentivize agents from misreporting information, we have introduced a market maker that rewards the agents based on the quality of the submitted reports. We have implemented the market maker’s reward calculation in the form of a scoring rule and have shown analytically that it incentivizes truthful revelation by each agent. We have experimentally verified our technique for multi-sensor information fusion for an automated landmine detection scenario. Our experimental results show that, for identical data distributions and settings, using our information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques for information fusion for landmine detection.
AB - Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the event’s outcome. We consider an analogous problem of information fusion from multiple sensors of different types with the objective of improving the confidence of inference tasks, such as object classification. We develop a multi-agent prediction market-based technique to solve this information fusion problem. To monitor the improvement in the confidence of the object classification as well as to dis-incentivize agents from misreporting information, we have introduced a market maker that rewards the agents based on the quality of the submitted reports. We have implemented the market maker’s reward calculation in the form of a scoring rule and have shown analytically that it incentivizes truthful revelation by each agent. We have experimentally verified our technique for multi-sensor information fusion for an automated landmine detection scenario. Our experimental results show that, for identical data distributions and settings, using our information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques for information fusion for landmine detection.
KW - Information fusion
KW - Landmine detection
KW - Multi-sensor aggregation
KW - Prediction market
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U2 - 10.1007/978-3-642-40864-9_6
DO - 10.1007/978-3-642-40864-9_6
M3 - Conference contribution
AN - SCOPUS:85006469018
SN - 9783642408632
T3 - Lecture Notes in Business Information Processing
SP - 75
EP - 89
BT - Agent-Mediated Electronic Commerce
A2 - David, Esther
A2 - Robu, Valentin
A2 - Stein, Sebastian
A2 - Kiekintveld, Christopher
A2 - Shehory, Onn
PB - Springer Verlag
T2 - International Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, AMEC/TADA 2012
Y2 - 4 June 2012 through 4 June 2012
ER -