50 research outputs found
Control Strategies for COVID-19 Epidemic with Vaccination, Shield Immunity and Quarantine: A Metric Temporal Logic Approach
Ever since the outbreak of the COVID-19 epidemic, various public health
control strategies have been proposed and tested against the coronavirus
SARS-CoV-2. We study three specific COVID-19 epidemic control models: the
susceptible, exposed, infectious, recovered (SEIR) model with vaccination
control; the SEIR model with shield immunity control; and the susceptible,
un-quarantined infected, quarantined infected, confirmed infected (SUQC) model
with quarantine control. We express the control requirement in metric temporal
logic (MTL) formulas (a type of formal specification languages) which can
specify the expected control outcomes such as "the deaths from the infection
should never exceed one thousand per day within the next three months" or "the
population immune from the disease should eventually exceed 200 thousand within
the next 100 to 120 days". We then develop methods for synthesizing control
strategies with MTL specifications. To the best of our knowledge, this is the
first paper to systematically synthesize control strategies based on the
COVID-19 epidemic models with formal specifications. We provide simulation
results in three different case studies: vaccination control for the COVID-19
epidemic with model parameters estimated from data in Lombardy, Italy; shield
immunity control for the COVID-19 epidemic with model parameters estimated from
data in Lombardy, Italy; and quarantine control for the COVID-19 epidemic with
model parameters estimated from data in Wuhan, China. The results show that the
proposed synthesis approach can generate control inputs such that the
time-varying numbers of individuals in each category (e.g., infectious, immune)
satisfy the MTL specifications. The results also show that early intervention
is essential in mitigating the spread of COVID-19, and more control effort is
needed for more stringent MTL specifications
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples
Despite the fact that deep reinforcement learning (RL) has surpassed
human-level performances in various tasks, it still has several fundamental
challenges. First, most RL methods require intensive data from the exploration
of the environment to achieve satisfactory performance. Second, the use of
neural networks in RL renders it hard to interpret the internals of the system
in a way that humans can understand. To address these two challenges, we
propose a framework that enables an RL agent to reason over its exploration
process and distill high-level knowledge for effectively guiding its future
explorations. Specifically, we propose a novel RL algorithm that learns
high-level knowledge in the form of a finite reward automaton by using the L*
learning algorithm. We prove that in episodic RL, a finite reward automaton can
express any non-Markovian bounded reward functions with finitely many reward
values and approximate any non-Markovian bounded reward function (with
infinitely many reward values) with arbitrary precision. We also provide a
lower bound for the episode length such that the proposed RL approach almost
surely converges to an optimal policy in the limit. We test this approach on
two RL environments with non-Markovian reward functions, choosing a variety of
tasks with increasing complexity for each environment. We compare our algorithm
with the state-of-the-art RL algorithms for non-Markovian reward functions,
such as Joint Inference of Reward machines and Policies for RL (JIRP), Learning
Reward Machine (LRM), and Proximal Policy Optimization (PPO2). Our results show
that our algorithm converges to an optimal policy faster than other baseline
methods
Third Conference on Artificial Intelligence for Space Applications, part 1
The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles
Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy