12,109 research outputs found
Stop It, and Be Stubborn!
A system is AG EF terminating, if and only if from every reachable state, a
terminal state is reachable. This publication argues that it is beneficial for
both catching non-progress errors and stubborn set state space reduction to try
to make verification models AG EF terminating. An incorrect mutual exclusion
algorithm is used as an example. The error does not manifest itself, unless the
first action of the customers is modelled differently from other actions. An
appropriate method is to add an alternative first action that models the
customer stopping for good. This method typically makes the model AG EF
terminating. If the model is AG EF terminating, then the basic strong stubborn
set method preserves safety and some progress properties without any additional
condition for solving the ignoring problem. Furthermore, whether the model is
AG EF terminating can be checked efficiently from the reduced state space
From local averaging to emergent global behaviors: the fundamental role of network interconnections
Distributed averaging is one of the simplest and most studied network
dynamics. Its applications range from cooperative inference in sensor networks,
to robot formation, to opinion dynamics. A number of fundamental results and
examples scattered through the literature are gathered here and originally
presented, emphasizing the deep interplay between the network interconnection
structure and the emergent global behavior.Comment: 10 page
Novel Multidimensional Models of Opinion Dynamics in Social Networks
Unlike many complex networks studied in the literature, social networks
rarely exhibit unanimous behavior, or consensus. This requires a development of
mathematical models that are sufficiently simple to be examined and capture, at
the same time, the complex behavior of real social groups, where opinions and
actions related to them may form clusters of different size. One such model,
proposed by Friedkin and Johnsen, extends the idea of conventional consensus
algorithm (also referred to as the iterative opinion pooling) to take into
account the actors' prejudices, caused by some exogenous factors and leading to
disagreement in the final opinions.
In this paper, we offer a novel multidimensional extension, describing the
evolution of the agents' opinions on several topics. Unlike the existing
models, these topics are interdependent, and hence the opinions being formed on
these topics are also mutually dependent. We rigorous examine stability
properties of the proposed model, in particular, convergence of the agents'
opinions. Although our model assumes synchronous communication among the
agents, we show that the same final opinions may be reached "on average" via
asynchronous gossip-based protocols.Comment: Accepted by IEEE Transaction on Automatic Control (to be published in
May 2017
An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms
This paper presents a new exponential lower bound for the two most popular
deterministic variants of the strategy improvement algorithms for solving
parity, mean payoff, discounted payoff and simple stochastic games. The first
variant improves every node in each step maximizing the current valuation
locally, whereas the second variant computes the globally optimal improvement
in each step. We outline families of games on which both variants require
exponentially many strategy iterations
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