55,433 research outputs found
A Hybrid Linear Logic for Constrained Transition Systems
Linear implication can represent state transitions, but real transition systems operate under temporal, stochastic or probabilistic constraints that are not directly representable in ordinary linear logic. We propose a general modal extension of intuitionistic linear logic where logical truth is indexed by constraints and hybrid connectives combine constraint reasoning with logical reasoning. The logic has a focused cut-free sequent calculus that can be used to internalize the rules of particular constrained transition systems; we illustrate this with an adequate encoding of the synchronous stochastic pi-calculus
A Hybrid Linear Logic for Constrained Transition Systems
International audienceLinear implication can represent state transitions, but real transition systems operate under temporal, stochastic or probabilistic constraints that are not directly representable in ordinary linear logic. We propose a general modal extension of intuitionistic linear logic where logical truth is indexed by constraints and hybrid connectives combine constraint reasoning with logical reasoning. The logic has a focused cut-free sequent calculus that can be used to internalize the rules of particular constrained transition systems; we illustrate this with an adequate encoding of the synchronous stochastic pi-calculus
A Hybrid Linear Logic for Constrained Transition Systems with Applications to Molecular Biology
Linear implication can represent state transitions, but real transition systems operate under temporal, stochastic or probabilistic constraints that are not directly representable in ordinary linear logic. We propose a general modal extension of intuitionistic linear logic where logical truth is indexed by constraints and hybrid connectives combine constraint reasoning with logical reasoning. The logic has a focused cut-free sequent calculus that can be used to internalize the rules of particular constrained transition systems; we illustrate this with an adequate encoding of the synchronous stochastic pi-calculus. We also present some preliminary experiments of direct encoding of biological systems in the logic
Conversion and verification procedure for goal-based control programs
Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System, developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a method for converting goal network control programs into linear hybrid systems is developed. The linear hybrid system can then be verified for safety in the presence of failures using existing symbolic model checkers. An example task is developed and successfully verified using HyTech, a symbolic model checking software for linear hybrid systems
Safety verification of a fault tolerant reconfigurable autonomous goal-based robotic control system
Fault tolerance and safety verification of control
systems are essential for the success of autonomous robotic
systems. A control architecture called Mission Data System
(MDS), developed at the Jet Propulsion Laboratory, takes
a goal-based control approach. In this paper, a method for
converting goal network control programs into linear hybrid
systems is developed. The linear hybrid system can then be
verified for safety in the presence of failures using existing
symbolic model checkers. An example task is simulated in
MDS and successfully verified using HyTech, a symbolic model
checking software for linear hybrid systems
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
Technical Report: A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints
This technical report is an extended version of the paper 'A Receding Horizon
Algorithm for Informative Path Planning with Temporal Logic Constraints'
accepted to the 2013 IEEE International Conference on Robotics and Automation
(ICRA). This paper considers the problem of finding the most informative path
for a sensing robot under temporal logic constraints, a richer set of
constraints than have previously been considered in information gathering. An
algorithm for informative path planning is presented that leverages tools from
information theory and formal control synthesis, and is proven to give a path
that satisfies the given temporal logic constraints. The algorithm uses a
receding horizon approach in order to provide a reactive, on-line solution
while mitigating computational complexity. Statistics compiled from multiple
simulation studies indicate that this algorithm performs better than a baseline
exhaustive search approach.Comment: Extended version of paper accepted to 2013 IEEE International
Conference on Robotics and Automation (ICRA
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