Maximum a posteriori path estimation with input trace perturbation: algorithms and application to credible rating of human routines

Authors:
Daniel H. Wilson;Matthai Philipose
Affiliations:
Robotics Institute, Carnegie Mellon University, Pittsburgh PA;Intel Research, Seattle, WA
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

Rating how well a routine activity is performed can be valuable in a variety of domains. Making the rating inexpensive and credible is a key aspect of the problem. We formalize the problem as MAP estimation in HMMs where the incoming trace needs repair. We present polynomial time algorithms for computing minimal repairs with maximal likelihood for HMMs, Hidden Semi-Markov Models (HSMMs) and a form of HMMs constrained with a fragment of the temporal logic LTL. We present some results to show the promise of our approach.