184 research outputs found
Game Characterization of Probabilistic Bisimilarity, and Applications to Pushdown Automata
We study the bisimilarity problem for probabilistic pushdown automata (pPDA)
and subclasses thereof. Our definition of pPDA allows both probabilistic and
non-deterministic branching, generalising the classical notion of pushdown
automata (without epsilon-transitions). We first show a general
characterization of probabilistic bisimilarity in terms of two-player games,
which naturally reduces checking bisimilarity of probabilistic labelled
transition systems to checking bisimilarity of standard (non-deterministic)
labelled transition systems. This reduction can be easily implemented in the
framework of pPDA, allowing to use known results for standard
(non-probabilistic) PDA and their subclasses. A direct use of the reduction
incurs an exponential increase of complexity, which does not matter in deriving
decidability of bisimilarity for pPDA due to the non-elementary complexity of
the problem. In the cases of probabilistic one-counter automata (pOCA), of
probabilistic visibly pushdown automata (pvPDA), and of probabilistic basic
process algebras (i.e., single-state pPDA) we show that an implicit use of the
reduction can avoid the complexity increase; we thus get PSPACE, EXPTIME, and
2-EXPTIME upper bounds, respectively, like for the respective non-probabilistic
versions. The bisimilarity problems for OCA and vPDA are known to have matching
lower bounds (thus being PSPACE-complete and EXPTIME-complete, respectively);
we show that these lower bounds also hold for fully probabilistic versions that
do not use non-determinism
Characterising Probabilistic Processes Logically
In this paper we work on (bi)simulation semantics of processes that exhibit
both nondeterministic and probabilistic behaviour. We propose a probabilistic
extension of the modal mu-calculus and show how to derive characteristic
formulae for various simulation-like preorders over finite-state processes
without divergence. In addition, we show that even without the fixpoint
operators this probabilistic mu-calculus can be used to characterise these
behavioural relations in the sense that two states are equivalent if and only
if they satisfy the same set of formulae.Comment: 18 page
Deciding Probabilistic Bisimilarity Distance One for Probabilistic Automata
Probabilistic bisimilarity, due to Segala and Lynch, is an equivalence relation that captures which states of a probabilistic automaton behave exactly the same. Deng, Chothia, Palamidessi and Pang proposed a robust quantitative generalization of probabilistic bisimilarity. Their probabilistic bisimilarity distances of states of a probabilistic automaton capture the similarity of their behaviour. The smaller the distance, the more alike the states behave. In particular, states are probabilistic bisimilar if and only if their distance is zero.
Although the complexity of computing probabilistic bisimilarity distances for probabilistic automata has already been studied and shown to be in NP cap coNP and PPAD, we are not aware of any practical algorithm to compute those distances. In this paper we provide several key results towards algorithms to compute probabilistic bisimilarity distances for probabilistic automata. In particular, we present a polynomial time algorithm that decides distance one. Furthermore, we give an alternative characterization of the probabilistic bisimilarity distances as a basis for a policy iteration algorithm
A Unifying Approach to Decide Relations for Timed Automata and their Game Characterization
In this paper we present a unifying approach for deciding various
bisimulations, simulation equivalences and preorders between two timed automata
states. We propose a zone based method for deciding these relations in which we
eliminate an explicit product construction of the region graphs or the zone
graphs as in the classical methods. Our method is also generic and can be used
to decide several timed relations. We also present a game characterization for
these timed relations and show that the game hierarchy reflects the hierarchy
of the timed relations. One can obtain an infinite game hierarchy and thus the
game characterization further indicates the possibility of defining new timed
relations which have not been studied yet. The game characterization also helps
us to come up with a formula which encodes the separation between two states
that are not timed bisimilar. Such distinguishing formulae can also be
generated for many relations other than timed bisimilarity.Comment: In Proceedings EXPRESS/SOS 2013, arXiv:1307.690
Approximating a Behavioural Pseudometric without Discount for<br> Probabilistic Systems
Desharnais, Gupta, Jagadeesan and Panangaden introduced a family of
behavioural pseudometrics for probabilistic transition systems. These
pseudometrics are a quantitative analogue of probabilistic bisimilarity.
Distance zero captures probabilistic bisimilarity. Each pseudometric has a
discount factor, a real number in the interval (0, 1]. The smaller the discount
factor, the more the future is discounted. If the discount factor is one, then
the future is not discounted at all. Desharnais et al. showed that the
behavioural distances can be calculated up to any desired degree of accuracy if
the discount factor is smaller than one. In this paper, we show that the
distances can also be approximated if the future is not discounted. A key
ingredient of our algorithm is Tarski's decision procedure for the first order
theory over real closed fields. By exploiting the Kantorovich-Rubinstein
duality theorem we can restrict to the existential fragment for which more
efficient decision procedures exist
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