We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty ...
Recent dynamic local search (DLS) algorithms such as SAPS are amongst the state-of-the-art methods for solving the propositional satisfiability problem (SAT). DLS algorithms modi...
Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. I...
In this paper, we present a new technique for extracting regions of interest (ROI) applying a local watershed transformation. The proposed strategy for computing catchment basins ...
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...