1,556 research outputs found
Amnestically induced persistence in random walks
We study how the Hurst exponent depends on the fraction of the
total time remembered by non-Markovian random walkers that recall only the
distant past. We find that otherwise nonpersistent random walkers switch to
persistent behavior when inflicted with significant memory loss. Such memory
losses induce the probability density function of the walker's position to
undergo a transition from Gaussian to non-Gaussian. We interpret these findings
of persistence in terms of a breakdown of self-regulation mechanisms and
discuss their possible relevance to some of the burdensome behavioral and
psychological symptoms of Alzheimer's disease and other dementias.Comment: 4 pages, 3 figs, subm. to Phys. Rev. Let
Value Function Discovery in Markov Decision Processes with Evolutionary Algorithms
In this paper we introduce a novel method for
discovery of value functions for Markov Decision Processes
(MDPs). This method, which we call Value Function Discovery
(VFD), is based on ideas from the Evolutionary Algorithm field.
VFD’s key feature is that it discovers descriptions of value
functions that are algebraic in nature. This feature is unique,
because the descriptions include the model parameters of the
MDP. The algebraic expression of the value function discovered by
VFD can be used in several scenarios, e.g., conversion to a policy
(with one-step policy improvement) or control of systems with
time-varying parameters. The work in this paper is a first step
towards exploring potential usage scenarios of discovered value
functions. We give a detailed description of VFD and illustrate its
application on an example MDP. For this MDP we let VFD discover
an algebraic description of a value function that closely resembles
the optimal value function. The discovered value function is
then used to obtain a policy, which we compare numerically
to the optimal policy of the MDP. The resulting policy shows
near-optimal performance on a wide range of model parameters.
Finally, we identify and discuss future application scenarios of
discovered value functions
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Are Lock-Free Concurrent Algorithms Practically Wait-Free?
Lock-free concurrent algorithms guarantee that some concurrent operation will
always make progress in a finite number of steps. Yet programmers prefer to
treat concurrent code as if it were wait-free, guaranteeing that all operations
always make progress. Unfortunately, designing wait-free algorithms is
generally a very complex task, and the resulting algorithms are not always
efficient. While obtaining efficient wait-free algorithms has been a long-time
goal for the theory community, most non-blocking commercial code is only
lock-free.
This paper suggests a simple solution to this problem. We show that, for a
large class of lock- free algorithms, under scheduling conditions which
approximate those found in commercial hardware architectures, lock-free
algorithms behave as if they are wait-free. In other words, programmers can
keep on designing simple lock-free algorithms instead of complex wait-free
ones, and in practice, they will get wait-free progress.
Our main contribution is a new way of analyzing a general class of lock-free
algorithms under a stochastic scheduler. Our analysis relates the individual
performance of processes with the global performance of the system using Markov
chain lifting between a complex per-process chain and a simpler system progress
chain. We show that lock-free algorithms are not only wait-free with
probability 1, but that in fact a general subset of lock-free algorithms can be
closely bounded in terms of the average number of steps required until an
operation completes.
To the best of our knowledge, this is the first attempt to analyze progress
conditions, typically stated in relation to a worst case adversary, in a
stochastic model capturing their expected asymptotic behavior.Comment: 25 page
Deriving Mean Field Equations from Large Process Algebra Models
In many domain areas the behaviour of a system can be described at two levels: the behaviour of individual components, and the behaviour of the system as a whole. Often deriving one from the other is impossible, or at least intractable, especially when realistically large systems are considered. Here we present a rigorous algorithm which, given an individual based model in the process algebra WSCCS describing the components of a system and the way they interact, can produce a system of mean field equations which describe the mean behaviour of the system as a whole. This transformation circumvents the state explosion problem, allowing us to handle systems of any size by providing an approximation of the system behaviour. From the mean field equations we can investigate the transient dynamics of the system. This approach was motivated by problems in biological systems, but is applicable to distributed systems in general
- …