  We consider the problem of information fusion from multiple sensors of
different types with the objective of improving the confidence of inference
tasks, such as object classification, performed from the data collected by the
sensors. We propose a novel technique based on distributed belief aggregation
using a multi-agent prediction market 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 instantaneously as well as at
the end of the inference task, 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 or accurate reporting 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.
