29,441 research outputs found
Average-based Robustness for Continuous-Time Signal Temporal Logic
We propose a new robustness score for continuous-time Signal Temporal Logic
(STL) specifications. Instead of considering only the most severe point along
the evolution of the signal, we use average scores to extract more information
from the signal, emphasizing robust satisfaction of all the specifications'
subformulae over their entire time interval domains. We demonstrate the
advantages of this new score in falsification and control synthesis problems in
systems with complex dynamics and multi-agent systems.Comment: Accepted for publication in the proceedings of Conference on Decision
and Control 201
Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning
We consider the problem of steering a system with unknown, stochastic
dynamics to satisfy a rich, temporally layered task given as a signal temporal
logic formula. We represent the system as a Markov decision process in which
the states are built from a partition of the state space and the transition
probabilities are unknown. We present provably convergent reinforcement
learning algorithms to maximize the probability of satisfying a given formula
and to maximize the average expected robustness, i.e., a measure of how
strongly the formula is satisfied. We demonstrate via a pair of robot
navigation simulation case studies that reinforcement learning with robustness
maximization performs better than probability maximization in terms of both
probability of satisfaction and expected robustness.Comment: 8 pages, 4 figure
Robust satisfaction of temporal logic specifications via reinforcement learning
We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally-layered task given as a signal temporal logic formula. We represent the system as a finite-memory Markov decision process with unknown transition probabilities and whose states are built from a partition of the state space. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given specification and to maximize the average expected robustness, i.e. a measure of how strongly the formula is satisfied. Robustness allows us to quantify progress towards satisfying a given specification. We demonstrate via a pair of robot navigation simulation case studies that, due to the quantification of progress towards satisfaction, reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness with a low number of training examples
Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking
For exhaustive formal verification, industrial-scale cyber-physical systems
(CPSs) are often too large and complex, and lightweight alternatives (e.g.,
monitoring and testing) have attracted the attention of both industrial
practitioners and academic researchers. Falsification is one popular testing
method of CPSs utilizing stochastic optimization. In state-of-the-art
falsification methods, the result of the previous falsification trials is
discarded, and we always try to falsify without any prior knowledge. To
concisely memorize such prior information on the CPS model and exploit it, we
employ Black-box checking (BBC), which is a combination of automata learning
and model checking. Moreover, we enhance BBC using the robust semantics of STL
formulas, which is the essential gadget in falsification. Our experiment
results suggest that our robustness-guided BBC outperforms a state-of-the-art
falsification tool.Comment: Accepted to HSCC 202
Control with Probabilistic Signal Temporal Logic
Autonomous agents often operate in uncertain environments where their
decisions are made based on beliefs over states of targets. We are interested
in controller synthesis for complex tasks defined over belief spaces. Designing
such controllers is challenging due to computational complexity and the lack of
expressivity of existing specification languages. In this paper, we propose a
probabilistic extension to signal temporal logic (STL) that expresses tasks
over continuous belief spaces. We present an efficient synthesis algorithm to
find a control input that maximises the probability of satisfying a given task.
We validate our algorithm through simulations of an unmanned aerial vehicle
deployed for surveillance and search missions.Comment: 7 pages, submitted to the 2016 American Control Conference (ACC 2016)
on September, 30, 2015 (under review
Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks
Signal temporal logic (STL) provides a user-friendly interface for defining
complex tasks for robotic systems. Recent efforts aim at designing control laws
or using reinforcement learning methods to find policies which guarantee
satisfaction of these tasks. While the former suffer from the trade-off between
task specification and computational complexity, the latter encounter
difficulties in exploration as the tasks become more complex and challenging to
satisfy. This paper proposes to combine the benefits of the two approaches and
use an efficient prescribed performance control (PPC) base law to guide
exploration within the reinforcement learning algorithm. The potential of the
method is demonstrated in a simulated environment through two sample
navigational tasks.Comment: This is the extended version of the paper accepted to the 2019
American Control Conference (ACC), Philadelphia (to be published
Control with probabilistic signal temporal logic
Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions
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