214 research outputs found
Generalized labelled Markov processes, coalgebraically
Coalgebras of measurable spaces are of interest in probability theory as a formalization of Labelled Markov Processes (LMPs). We discuss some general facts related to the notions of bisimulation and cocongruence on these systems, providing a faithful characterization of bisimulation on LMPs on generic measurable
spaces. This has been used to prove that bisimilarity on single LMPs is an equivalence, without assuming the state space to be analytic. As the second main contribution, we introduce the first specification rule format to define well-behaved composition operators for LMPs. This allows one to define process description languages on LMPs which are always guaranteed to have a fully-abstract semantics
Measurable Stochastics for Brane Calculus
We give a stochastic extension of the Brane Calculus, along the lines of
recent work by Cardelli and Mardare. In this presentation, the semantics of a
Brane process is a measure of the stochastic distribution of possible
derivations. To this end, we first introduce a labelled transition system for
Brane Calculus, proving its adequacy w.r.t. the usual reduction semantics.
Then, brane systems are presented as Markov processes over the measurable space
generated by terms up-to syntactic congruence, and where the measures are
indexed by the actions of this new LTS. Finally, we provide a SOS presentation
of this stochastic semantics, which is compositional and syntax-driven.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
A framework for protein and membrane interactions
We introduce the BioBeta Framework, a meta-model for both protein-level and
membrane-level interactions of living cells. This formalism aims to provide a
formal setting where to encode, compare and merge models at different
abstraction levels; in particular, higher-level (e.g. membrane) activities can
be given a formal biological justification in terms of low-level (i.e.,
protein) interactions. A BioBeta specification provides a protein signature
together a set of protein reactions, in the spirit of the kappa-calculus.
Moreover, the specification describes when a protein configuration triggers one
of the only two membrane interaction allowed, that is "pinch" and "fuse". In
this paper we define the syntax and semantics of BioBeta, analyse its
properties, give it an interpretation as biobigraphical reactive systems, and
discuss its expressivity by comparing with kappa-calculus and modelling
significant examples. Notably, BioBeta has been designed after a bigraphical
metamodel for the same purposes. Hence, each instance of the calculus
corresponds to a bigraphical reactive system, and vice versa (almost).
Therefore, we can inherith the rich theory of bigraphs, such as the automatic
construction of labelled transition systems and behavioural congruences
Bigraphical models for protein and membrane interactions
We present a bigraphical framework suited for modeling biological systems
both at protein level and at membrane level. We characterize formally bigraphs
corresponding to biologically meaningful systems, and bigraphic rewriting rules
representing biologically admissible interactions. At the protein level, these
bigraphic reactive systems correspond exactly to systems of kappa-calculus.
Membrane-level interactions are represented by just two general rules, whose
application can be triggered by protein-level interactions in a well-de\"ined
and precise way. This framework can be used to compare and merge models at
different abstraction levels; in particular, higher-level (e.g. mobility)
activities can be given a formal biological justification in terms of low-level
(i.e., protein) interactions. As examples, we formalize in our framework the
vesiculation and the phagocytosis processes
Computing Probabilistic Bisimilarity Distances for Probabilistic Automata
The probabilistic bisimilarity distance of Deng et al. has been proposed as a
robust quantitative generalization of Segala and Lynch's probabilistic
bisimilarity for probabilistic automata. In this paper, we present a
characterization of the bisimilarity distance as the solution of a simple
stochastic game. The characterization gives us an algorithm to compute the
distances by applying Condon's simple policy iteration on these games. The
correctness of Condon's approach, however, relies on the assumption that the
games are stopping. Our games may be non-stopping in general, yet we are able
to prove termination for this extended class of games. Already other algorithms
have been proposed in the literature to compute these distances, with
complexity in and \textbf{PPAD}. Despite the
theoretical relevance, these algorithms are inefficient in practice. To the
best of our knowledge, our algorithm is the first practical solution.
The characterization of the probabilistic bisimilarity distance mentioned
above crucially uses a dual presentation of the Hausdorff distance due to
M\'emoli. As an additional contribution, in this paper we show that M\'emoli's
result can be used also to prove that the bisimilarity distance bounds the
difference in the maximal (or minimal) probability of two states to satisfying
arbitrary -regular properties, expressed, eg., as LTL formulas
A Faster-Than Relation for Semi-Markov Decision Processes
When modeling concurrent or cyber-physical systems, non-functional
requirements such as time are important to consider. In order to improve the
timing aspects of a model, it is necessary to have some notion of what it means
for a process to be faster than another, which can guide the stepwise
refinement of the model. To this end we study a faster-than relation for
semi-Markov decision processes and compare it to standard notions for relating
systems. We consider the compositional aspects of this relation, and show that
the faster-than relation is not a precongruence with respect to parallel
composition, hence giving rise to so-called parallel timing anomalies. We take
the first steps toward understanding this problem by identifying decidable
conditions sufficient to avoid parallel timing anomalies in the absence of
non-determinism.Comment: In Proceedings QAPL 2019, arXiv:2001.0616
Complete Axiomatization for the Bisimilarity Distance on Markov Chains
In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al. for the class of finite labelled Markov chains. Our axiomatization is given in the style of a quantitative extension of equational logic recently proposed by Mardare, Panangaden, and Plotkin (LICS\u2716) that uses equality relations t =_e s indexed by rationals, expressing that "t is approximately equal to s up to an error e".
Notably, our quantitative deductive system extends in a natural way the equational system for probabilistic bisimilarity given by Stark and Smolka by introducing an axiom for dealing with the Kantorovich distance between probability distributions
On the Metric-Based Approximate Minimization of Markov Chains
We address the behavioral metric-based approximate minimization problem of Markov Chains (MCs), i.e., given a finite MC and a positive integer k, we are interested in finding a k-state MC of minimal distance to the original. By considering as metric the bisimilarity distance of Desharnais at al., we show that optimal approximations always exist; show that the problem can be solved as a bilinear program; and prove that its threshold problem is in PSPACE and NP-hard. Finally, we present an approach inspired by expectation maximization techniques that provides suboptimal solutions. Experiments suggest that our method gives a practical approach that outperforms the bilinear program implementation run on state-of-the-art bilinear solvers
Timed Comparisons of Semi-Markov Processes
Semi-Markov processes are Markovian processes in which the firing time of the
transitions is modelled by probabilistic distributions over positive reals
interpreted as the probability of firing a transition at a certain moment in
time. In this paper we consider the trace-based semantics of semi-Markov
processes, and investigate the question of how to compare two semi-Markov
processes with respect to their time-dependent behaviour. To this end, we
introduce the relation of being "faster than" between processes and study its
algorithmic complexity. Through a connection to probabilistic automata we
obtain hardness results showing in particular that this relation is
undecidable. However, we present an additive approximation algorithm for a
time-bounded variant of the faster-than problem over semi-Markov processes with
slow residence-time functions, and a coNP algorithm for the exact faster-than
problem over unambiguous semi-Markov processes
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