This paper proposes an optimal approach to infinite-state action planning exploiting automata theory. State sets and actions are characterized by Presburger formulas and represent...
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the gen...
Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contai...
Abstract. Existing algorithms for regular inference (aka automata learning) allows to infer a finite state machine by observing the output that the machine produces in response to ...