31 research outputs found
On Endogenous Random Consensus and Averaging Dynamics
Motivated by various random variations of Hegselmann-Krause model for opinion
dynamics and gossip algorithm in an endogenously changing environment, we
propose a general framework for the study of endogenously varying random
averaging dynamics, i.e.\ an averaging dynamics whose evolution suffers from
history dependent sources of randomness. We show that under general assumptions
on the averaging dynamics, such dynamics is convergent almost surely. We also
determine the limiting behavior of such dynamics and show such dynamics admit
infinitely many time-varying Lyapunov functions
On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization
Revealed preference theory studies the possibility of modeling an agent's
revealed preferences and the construction of a consistent utility function.
However, modeling agent's choices over preference orderings is not always
practical and demands strong assumptions on human rationality and
data-acquisition abilities. Therefore, we propose a simple generative choice
model where agents are assumed to generate the choice probabilities based on
latent factor matrices that capture their choice evaluation across multiple
attributes. Since the multi-attribute evaluation is typically hidden within the
agent's psyche, we consider a signaling mechanism where agents are provided
with choice information through private signals, so that the agent's choices
provide more insight about his/her latent evaluation across multiple
attributes. We estimate the choice model via a novel multi-stage matrix
factorization algorithm that minimizes the average deviation of the factor
estimates from choice data. Simulation results are presented to validate the
estimation performance of our proposed algorithm.Comment: 6 pages, 2 figures, to be presented at CISS conferenc
Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks
The paper considers a problem of detecting and mitigating biasing attacks on
networks of state observers targeting cooperative state estimation algorithms.
The problem is cast within the recently developed framework of distributed
estimation utilizing the vector dissipativity approach. The paper shows that a
network of distributed observers can be endowed with an additional attack
detection layer capable of detecting biasing attacks and correcting their
effect on estimates produced by the network. An example is provided to
illustrate the performance of the proposed distributed attack detector.Comment: Accepted for publication in Automatic
Regret Bounds for LQ Adaptive Control Under Database Attacks (Extended Version)
This paper is concerned with understanding and countering the effects of
database attacks on a learning-based linear quadratic adaptive controller. This
attack targets neither sensors nor actuators, but just poisons the learning
algorithm and parameter estimator that is part of the regulation scheme. We
focus on the adaptive optimal control algorithm introduced by Abbasi-Yadkori
and Szepesvari and provide regret analysis in the presence of attacks as well
as modifications that mitigate their effects. A core step of this algorithm is
the self-regularized on-line least squares estimation, which determines a tight
confidence set around the true parameters of the system with high probability.
In the absence of malicious data injection, this set provides an appropriate
estimate of parameters for the aim of control design. However, in the presence
of attack, this confidence set is not reliable anymore. Hence, we first tackle
the question of how to adjust the confidence set so that it can compensate for
the effect of the poisonous data. Then, we quantify the deleterious effect of
this type of attack on the optimality of control policy by providing a measure
that we call attack regret.Comment: 10 page
Regret-Guaranteed Safe Switching with Minimum Cost: LQR Setting with Unknown Dynamics
Externally Forced Switched (EFS) systems represent a subset of switched
systems where switches occur deliberately to meet an external requirement.
However, fast switching can lead to instability, even when all closed-loop
modes are stable. In this study, our focus is on an EFS scenario with
\textit{unknown system dynamics}, where the next mode to switch to is revealed
by an external entity in real-time as the switch occurs. The challenge is to
track the revealed sequence while (1) minimizing accumulated cost in a
regretful sense and (2) ensuring that the norm of the system's state does not
grow excessively-a property we refer to as 'the safety of switching.' Achieving
the latter involves requiring the closed-loop system to remain in each revealed
mode for some minimum dwell time, which must be learned online. We propose an
algorithm based on the principles of Optimism in the Face of Uncertainty. This
algorithm jointly establishes confidence sets for unknown parameters, devises a
feedback policy, and estimates a minimum dwell time for each revealed mode from
data. By precisely estimating dwell-time error, our strategy yields an expected
regret of , where and denote the total
switches and mode count, respectively. We benchmark this approach against
scenarios with known parameters