In this paper, we revise the notion of Soft Constraint Automata, where automata transitions are weighted and consequently each action is associated with a preference value. We first relax the underlying algebraic structure that models preferences, with the purpose to use bipolar preferences (i.e., both positive and negative ones). Then, we equip automata with memory cells, that is, with an internal state to remember and update information from transition to transition. Finally, we revise automata operators, as join and hiding.
Soft constraint automata with memory
Gadducci, Fabio
Co-primo
Membro del Collaboration Group
;
2018-01-01
Abstract
In this paper, we revise the notion of Soft Constraint Automata, where automata transitions are weighted and consequently each action is associated with a preference value. We first relax the underlying algebraic structure that models preferences, with the purpose to use bipolar preferences (i.e., both positive and negative ones). Then, we equip automata with memory cells, that is, with an internal state to remember and update information from transition to transition. Finally, we revise automata operators, as join and hiding.File in questo prodotto:
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