38,827 research outputs found
Abstraction-based verification of parameterized networks
The thesis presents a method to verify parameterized networks of finite state processes. The method is based on three main ideas. The first one consists in modeling an infinite family of networks by a single WS1S transition system, that is, a transition system whose variables are set (2nd-order) variables and whose transitions are described in WS1S. Then, we present methods that allow to abstract a WS1S system into a finite state system that can be model-checked. Finally, in order to verify liveness properties, we present an algorithm that allows to enrich the abstract system with strong fairness conditions while preserving safety of the abstraction. We prove applicability of the method by verifying several examples. Moreover, we present generalizations that allow to verify networks of processes with unbounded state space or networks with tree topologies
Parameterized synthesis of self-stabilizing protocols in symmetric networks
Self-stabilization in distributed systems is a technique to guarantee convergence to a set of legitimate states without external intervention when a transient fault or bad initialization occurs. Recently, there has been a surge of efforts in designing techniques for automated synthesis of self-stabilizing algorithms that are correct by construction. Most of these techniques, however, are not parameterized, meaning that they can only synthesize a solution for a fixed and predetermined number of processes. In this paper, we report a breakthrough in parameterized synthesis of self-stabilizing algorithms in symmetric networks, including ring, line, mesh, and torus. First, we develop cutoffs that guarantee (1) closure in legitimate states, and (2) deadlock-freedom outside the legitimate states. We also develop a sufficient condition for convergence in self-stabilizing systems. Since some of our cutoffs grow with the size of the local state space of processes, scalability of the synthesis procedure is still a problem. We address this problem by introducing a novel SMT-based technique for counterexample-guided synthesis of self-stabilizing algorithms in symmetric networks. We have fully implemented our technique and successfully synthesized solutions to maximal matching, three coloring, and maximal independent set problems for ring and line topologies
Parameterized Model-Checking for Timed-Systems with Conjunctive Guards (Extended Version)
In this work we extend the Emerson and Kahlon's cutoff theorems for process
skeletons with conjunctive guards to Parameterized Networks of Timed Automata,
i.e. systems obtained by an \emph{apriori} unknown number of Timed Automata
instantiated from a finite set of Timed Automata templates.
In this way we aim at giving a tool to universally verify software systems
where an unknown number of software components (i.e. processes) interact with
continuous time temporal constraints. It is often the case, indeed, that
distributed algorithms show an heterogeneous nature, combining dynamic aspects
with real-time aspects. In the paper we will also show how to model check a
protocol that uses special variables storing identifiers of the participating
processes (i.e. PIDs) in Timed Automata with conjunctive guards. This is
non-trivial, since solutions to the parameterized verification problem often
relies on the processes to be symmetric, i.e. indistinguishable. On the other
side, many popular distributed algorithms make use of PIDs and thus cannot
directly apply those solutions
Parameterized Verification of Safety Properties in Ad Hoc Network Protocols
We summarize the main results proved in recent work on the parameterized
verification of safety properties for ad hoc network protocols. We consider a
model in which the communication topology of a network is represented as a
graph. Nodes represent states of individual processes. Adjacent nodes represent
single-hop neighbors. Processes are finite state automata that communicate via
selective broadcast messages. Reception of a broadcast is restricted to
single-hop neighbors. For this model we consider a decision problem that can be
expressed as the verification of the existence of an initial topology in which
the execution of the protocol can lead to a configuration with at least one
node in a certain state. The decision problem is parametric both on the size
and on the form of the communication topology of the initial configurations. We
draw a complete picture of the decidability and complexity boundaries of this
problem according to various assumptions on the possible topologies.Comment: In Proceedings PACO 2011, arXiv:1108.145
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It enables the discovery and
acquisition of large repertoires of skills through self-generation,
self-selection, self-ordering and self-experimentation of learning goals. We
present an algorithmic approach called Intrinsically Motivated Goal Exploration
Processes (IMGEP) to enable similar properties of autonomous or self-supervised
learning in machines. The IMGEP algorithmic architecture relies on several
principles: 1) self-generation of goals, generalized as fitness functions; 2)
selection of goals based on intrinsic rewards; 3) exploration with incremental
goal-parameterized policy search and exploitation of the gathered data with a
batch learning algorithm; 4) systematic reuse of information acquired when
targeting a goal for improving towards other goals. We present a particularly
efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a
population-based policy and an object-centered modularity in goals and
mutations. We provide several implementations of this architecture and
demonstrate their ability to automatically generate a learning curriculum
within several experimental setups including a real humanoid robot that can
explore multiple spaces of goals with several hundred continuous dimensions.
While no particular target goal is provided to the system, this curriculum
allows the discovery of skills that act as stepping stone for learning more
complex skills, e.g. nested tool use. We show that learning diverse spaces of
goals with intrinsic motivations is more efficient for learning complex skills
than only trying to directly learn these complex skills
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