2,949 research outputs found
Parameterized Verification of Graph Transformation Systems with Whole Neighbourhood Operations
We introduce a new class of graph transformation systems in which rewrite
rules can be guarded by universally quantified conditions on the neighbourhood
of nodes. These conditions are defined via special graph patterns which may be
transformed by the rule as well. For the new class for graph rewrite rules, we
provide a symbolic procedure working on minimal representations of upward
closed sets of configurations. We prove correctness and effectiveness of the
procedure by a categorical presentation of rewrite rules as well as the
involved order, and using results for well-structured transition systems. We
apply the resulting procedure to the analysis of the Distributed Dining
Philosophers protocol on an arbitrary network structure.Comment: Extended version of a submittion accepted at RP'14 Worksho
Coherence-based Partial Exact Recovery Condition for OMP/OLS
We address the exact recovery of the support of a k-sparse vector with
Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a
noiseless setting. We consider the scenario where OMP/OLS have selected good
atoms during the first l iterations (l<k) and derive a new sufficient and
worst-case necessary condition for their success in k steps. Our result is
based on the coherence \mu of the dictionary and relaxes Tropp's well-known
condition \mu<1/(2k-1) to the case where OMP/OLS have a partial knowledge of
the support
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uni¯es the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
An Object-Oriented Framework for Explicit-State Model Checking
This paper presents a conceptual architecture for an object-oriented framework to support the development of formal verification tools (i.e. model checkers). The objective of the architecture is to support the reuse of algorithms and to encourage a modular design of tools. The conceptual framework is accompanied by a C++ implementation which provides reusable algorithms for the simulation and verification of explicit-state models as well as a model representation for simple models based on guard-based process descriptions. The framework has been successfully used to develop a model checker for a subset of PROMELA
A Notion of Dynamic Interface for Depth-Bounded Object-Oriented Packages
Programmers using software components have to follow protocols that specify
when it is legal to call particular methods with particular arguments. For
example, one cannot use an iterator over a set once the set has been changed
directly or through another iterator. We formalize the notion of dynamic
package interfaces (DPI), which generalize state-machine interfaces for single
objects, and give an algorithm to statically compute a sound abstraction of a
DPI. States of a DPI represent (unbounded) sets of heap configurations and
edges represent the effects of method calls on the heap. We introduce a novel
heap abstract domain based on depth-bounded systems to deal with potentially
unboundedly many objects and the references among them. We have implemented our
algorithm and show that it is effective in computing representations of common
patterns of package usage, such as relationships between viewer and label,
container and iterator, and JDBC statements and cursors
Solving Stochastic B\"uchi Games on Infinite Arenas with a Finite Attractor
We consider games played on an infinite probabilistic arena where the first
player aims at satisfying generalized B\"uchi objectives almost surely, i.e.,
with probability one. We provide a fixpoint characterization of the winning
sets and associated winning strategies in the case where the arena satisfies
the finite-attractor property. From this we directly deduce the decidability of
these games on probabilistic lossy channel systems.Comment: In Proceedings QAPL 2013, arXiv:1306.241
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
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