Partially Observable Markov Decision Processes have been studied widely as a model for decision making under uncertainty, and a number of methods have been developed to find the s...
In this work, a generalized method for learning from sequence of unlabelled data points based on unsupervised order-preserving regression is proposed. Sequence learning is a funda...
An efficient training method for block-diagonal recurrent neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static models, in order to...
Paris A. Mastorocostas, Dimitris N. Varsamis, Cons...
Abstract. In this paper, we introduce a framework composed of a syntax and its compositional Petri net semantics, for the specification and verification of properties (like authent...
Roland Bouroulet, Raymond R. Devillers, Hanna Klau...
Many problem-solving tasks can be formalized as constraint satisfaction problems (CSPs). In a multi-agent setting, information about constraints and variables may belong to differ...