Abstract This paper addresses the problem of approximating set all solutions for Multi-objective Markov Decision Processes. We show that in vast majority interesting cases, number is exponential or even infinite. In order to overcome this difficulty we propose approximate by means a limited precision approach based on White’s multi-objective value-iteration dynamic programming algorithm. prove ...

We revise classical results in Argumentation-based Logic Programming, e.g., Defeasible Logic Programming (DeLP), under the umbrella of Satisfiability Modulo CHR. Through this language it is possible to reason on arguments and have an underlying theory solver to implement resolution of conflicts. Strict and defeasible rules, as well as certainty scores associated with such rules (e.g., Possibili...

In this paper, we show how a possibilistic uncertainty model can be used to represent and manipulate uncertainty about the states of a system and of its components. At the same time, we present a thorough study of the incorporation of this possibilistic uncertainty model in classical, two-state reliability theory. The possibilistic reliability of a component or system is introduced and studied....

We provide basic results for the development of a theory of possibilistic Markov processes. We define and study possibilistic Markov processes and possibilistic Markov chains, and derive a possibilistic analogon of the Chapman-Kolmogorov equation. We also show how possibilistic Markov processes can be constructed using one-step transition possibilities.