Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assi...
Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size ...
We present new algorithms for local planning over Markov decision processes. The base-level algorithm possesses several interesting features for control of computation, based on s...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...