3,790 research outputs found
Distributed Computing with Adaptive Heuristics
We use ideas from distributed computing to study dynamic environments in
which computational nodes, or decision makers, follow adaptive heuristics (Hart
2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly
"best replying" to others' actions, and minimizing "regret", that have been
extensively studied in game theory and economics. We explore when convergence
of such simple dynamics to an equilibrium is guaranteed in asynchronous
computational environments, where nodes can act at any time. Our research
agenda, distributed computing with adaptive heuristics, lies on the borderline
of computer science (including distributed computing and learning) and game
theory (including game dynamics and adaptive heuristics). We exhibit a general
non-termination result for a broad class of heuristics with bounded
recall---that is, simple rules of behavior that depend only on recent history
of interaction between nodes. We consider implications of our result across a
wide variety of interesting and timely applications: game theory, circuit
design, social networks, routing and congestion control. We also study the
computational and communication complexity of asynchronous dynamics and present
some basic observations regarding the effects of asynchrony on no-regret
dynamics. We believe that our work opens a new avenue for research in both
distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion
of v1. Revised version will appear in the proceedings of Innovations in
Computer Science 201
Computational paradigm for dynamic logic-gates in neuronal activity
In 1943 McCulloch and Pitts suggested that the brain is composed of reliable
logic-gates similar to the logic at the core of today's computers. This
framework had a limited impact on neuroscience, since neurons exhibit far
richer dynamics. Here we propose a new experimentally corroborated paradigm in
which the truth tables of the brain's logic-gates are time dependent, i.e.
dynamic logicgates (DLGs). The truth tables of the DLGs depend on the history
of their activity and the stimulation frequencies of their input neurons. Our
experimental results are based on a procedure where conditioned stimulations
were enforced on circuits of neurons embedded within a large-scale network of
cortical cells in-vitro. We demonstrate that the underlying biological
mechanism is the unavoidable increase of neuronal response latencies to ongoing
stimulations, which imposes a nonuniform gradual stretching of network delays.
The limited experimental results are confirmed and extended by simulations and
theoretical arguments based on identical neurons with a fixed increase of the
neuronal response latency per evoked spike. We anticipate our results to lead
to better understanding of the suitability of this computational paradigm to
account for the brain's functionalities and will require the development of new
systematic mathematical methods beyond the methods developed for traditional
Boolean algebra.Comment: 32 pages, 14 figures, 1 tabl
Two snap-stabilizing point-to-point communication protocols in message-switched networks
A snap-stabilizing protocol, starting from any configuration, always behaves
according to its specification. In this paper, we present a snap-stabilizing
protocol to solve the message forwarding problem in a message-switched network.
In this problem, we must manage resources of the system to deliver messages to
any processor of the network. In this purpose, we use information given by a
routing algorithm. By the context of stabilization (in particular, the system
starts in an arbitrary configuration), this information can be corrupted. So,
the existence of a snap-stabilizing protocol for the message forwarding problem
implies that we can ask the system to begin forwarding messages even if routing
information are initially corrupted. In this paper, we propose two
snap-stabilizing algorithms (in the state model) for the following
specification of the problem: - Any message can be generated in a finite time.
- Any emitted message is delivered to its destination once and only once in a
finite time. This implies that our protocol can deliver any emitted message
regardless of the state of routing tables in the initial configuration. These
two algorithms are based on the previous work of [MS78]. Each algorithm needs a
particular method to be transform into a snap-stabilizing one but both of them
do not introduce a significant overcost in memory or in time with respect to
algorithms of [MS78]
Survey of Distributed Decision
We survey the recent distributed computing literature on checking whether a
given distributed system configuration satisfies a given boolean predicate,
i.e., whether the configuration is legal or illegal w.r.t. that predicate. We
consider classical distributed computing environments, including mostly
synchronous fault-free network computing (LOCAL and CONGEST models), but also
asynchronous crash-prone shared-memory computing (WAIT-FREE model), and mobile
computing (FSYNC model)
A Statistical Learning Theory Approach for Uncertain Linear and Bilinear Matrix Inequalities
In this paper, we consider the problem of minimizing a linear functional
subject to uncertain linear and bilinear matrix inequalities, which depend in a
possibly nonlinear way on a vector of uncertain parameters. Motivated by recent
results in statistical learning theory, we show that probabilistic guaranteed
solutions can be obtained by means of randomized algorithms. In particular, we
show that the Vapnik-Chervonenkis dimension (VC-dimension) of the two problems
is finite, and we compute upper bounds on it. In turn, these bounds allow us to
derive explicitly the sample complexity of these problems. Using these bounds,
in the second part of the paper, we derive a sequential scheme, based on a
sequence of optimization and validation steps. The algorithm is on the same
lines of recent schemes proposed for similar problems, but improves both in
terms of complexity and generality. The effectiveness of this approach is shown
using a linear model of a robot manipulator subject to uncertain parameters.Comment: 19 pages, 2 figures, Accepted for Publication in Automatic
Formal assessment of some properties of Context-Aware Systems
Context-Aware systems are becoming useful components in autonomic and
monitoring applications and the assessment of their properties is an important
step towards reliable implementation, especially in safety-critical
applications. In this paper, using an avalanche/landslide alert system as a
running example, we propose a technique, based on Boolean Control Networks, to
verify that the system dynamics has stable equilibrium states, corresponding to
constant inputs, and hence it does not exhibit oscillatory behaviors, and to
establish other useful properties in order to implement a precise and timely
alarm system
Verifying the distributed real-time network protocol RTnet using Uppaal
RTnet is a distributed real-time network protocol for fully-connected local area networks with a broadcast capability. It supports streaming real-time and non-realtime traffic and on-the-fly addition and removal of network nodes. This paper presents a formal analysis of RTnet using the model checker Uppaal. Besides normal protocol behaviour, the analysis focuses on the fault-handling properties of RTnet, in particular recovery after packet loss. Both qualitative and quantitative properties are presented, together with the verification results and conclusions about the robustness of RTnet
Formal Synthesis of Control Strategies for Positive Monotone Systems
We design controllers from formal specifications for positive discrete-time
monotone systems that are subject to bounded disturbances. Such systems are
widely used to model the dynamics of transportation and biological networks.
The specifications are described using signal temporal logic (STL), which can
express a broad range of temporal properties. We formulate the problem as a
mixed-integer linear program (MILP) and show that under the assumptions made in
this paper, which are not restrictive for traffic applications, the existence
of open-loop control policies is sufficient and almost necessary to ensure the
satisfaction of STL formulas. We establish a relation between satisfaction of
STL formulas in infinite time and set-invariance theories and provide an
efficient method to compute robust control invariant sets in high dimensions.
We also develop a robust model predictive framework to plan controls optimally
while ensuring the satisfaction of the specification. Illustrative examples and
a traffic management case study are included.Comment: To appear in IEEE Transactions on Automatic Control (TAC) (2018), 16
pages, double colum
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