18,256 research outputs found
Counting Complexity for Reasoning in Abstract Argumentation
In this paper, we consider counting and projected model counting of
extensions in abstract argumentation for various semantics. When asking for
projected counts we are interested in counting the number of extensions of a
given argumentation framework while multiple extensions that are identical when
restricted to the projected arguments count as only one projected extension. We
establish classical complexity results and parameterized complexity results
when the problems are parameterized by treewidth of the undirected
argumentation graph. To obtain upper bounds for counting projected extensions,
we introduce novel algorithms that exploit small treewidth of the undirected
argumentation graph of the input instance by dynamic programming (DP). Our
algorithms run in time double or triple exponential in the treewidth depending
on the considered semantics. Finally, we take the exponential time hypothesis
(ETH) into account and establish lower bounds of bounded treewidth algorithms
for counting extensions and projected extension.Comment: Extended version of a paper published at AAAI-1
Extended RDF: Computability and Complexity Issues
ERDF stable model semantics is a recently proposed semantics for
ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs.
In this paper, we elaborate on the computability and complexity issues of the
ERDF stable model semantics. Based on the undecidability result of ERDF
stable model semantics, decidability under this semantics cannot be achieved,
unless ERDF ontologies of restricted syntax are considered. Therefore, we
propose a slightly modified semantics for ERDF ontologies, called ERDF #n-
stable model semantics. We show that entailment under this semantics is, in
general, decidable and also extends RDFS entailment. Equivalence statements
between the two semantics are provided. Additionally, we provide algorithms
that compute the ERDF #n-stable models of syntax-restricted and general
ERDF ontologies. Further, we provide complexity results for the ERDF #nstable
model semantics on syntax-restricted and general ERDF ontologies.
Finally, we provide complexity results for the ERDF stable model semantics
on syntax-restricted ERDF ontologies
A model checking approach to the parameter estimation of biochemical pathways
Model checking has historically been an important tool to
verify models of a wide variety of systems. Typically a model has to exhibit
certain properties to be classed āacceptableā. In this work we use
model checking in a new setting; parameter estimation. We characterise
the desired behaviour of a model in a temporal logic property and alter
the model to make it conform to the property (determined through
model checking). We have implemented a computational system called
MC2(GA) which pairs a model checker with a genetic algorithm. To
drive parameter estimation, the fitness of set of parameters in a model is
the inverse of the distance between its actual behaviour and the desired
behaviour. The model checker used is the simulation-based Monte Carlo
Model Checker for Probabilistic Linear-time Temporal Logic with numerical
constraints, MC2(PLTLc). Numerical constraints as well as the
overall probability of the behaviour expressed in temporal logic are used
to minimise the behavioural distance. We define the theory underlying
our parameter estimation approach in both the stochastic and continuous
worlds. We apply our approach to biochemical systems and present
an illustrative example where we estimate the kinetic rate constants in
a continuous model of a signalling pathway
Probabilistic Bisimulation: Naturally on Distributions
In contrast to the usual understanding of probabilistic systems as stochastic
processes, recently these systems have also been regarded as transformers of
probabilities. In this paper, we give a natural definition of strong
bisimulation for probabilistic systems corresponding to this view that treats
probability distributions as first-class citizens. Our definition applies in
the same way to discrete systems as well as to systems with uncountable state
and action spaces. Several examples demonstrate that our definition refines the
understanding of behavioural equivalences of probabilistic systems. In
particular, it solves a long-standing open problem concerning the
representation of memoryless continuous time by memory-full continuous time.
Finally, we give algorithms for computing this bisimulation not only for finite
but also for classes of uncountably infinite systems
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