We present a new general framework for online istic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The l is an extension of the existing Abstract Hidden ...
This paper presents an extensive evaluation, on artificial datasets, of EDY, an unsupervised algorithm for automatically synthesizing a Structured Hidden Markov Model (S-HMM) from ...
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
The hidden terminal problem is unique to wireless networks and as of now, there is very limited understanding about it's effects on network performance. Results are presented...
Structured Hidden Markov Models (S-HMM) are a variant of Hierarchical Hidden Markov Models; it provides an abstraction mechanism allowing a high level symbolic description of the k...