105 research outputs found
Rich time series classification using temporal logic
© 2017 MIT Press Journals. All rights reserved. Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not 'readable' in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framework for finding a temporal logic classifier specified in a human-readable form. The classifier is represented as a signal temporal logic (STL) formula that is expressive in capturing spatial, temporal and logical relations from a continuous-valued dataset over time. In the framework, we first find a set of representative logical formulas from the raw dataset, and then construct an STL classifier using a treebased clustering algorithm. We show that the framework runs in polynomial time and validate it using simulated examples where our framework is significantly more efficient than the closest existing framework (up to 920 times faster)
Experiments in the coordination of large groups of robots
The use of large groups of robots, generally called swarms, has gained increased attention in recent years. In this paper, we present and experimentally validate an algorithm that allows a swarm of robots to navigate in an environment containing unknown obstacles. A coordination mechanism based on dynamic role assignment and local communication is used to help robots that may get stuck in regions of local minima. Experiments were performed using both a realistic simulator and a group of real robots and the obtained results showed the feasibility of the proposed approach
Temporal logic control for piecewise-affine hybrid systems on polytopes
Abstract-In this paper, a method is proposed for the design of control laws for hybrid systems with continuous inputs. The objective is to influence their behavior in such a way that the discrete component of the closed-loop system satisfies a given condition, described by a temporal logic formula. For this purpose, a transition system is constructed, by abstracting from the continuous dynamics of the hybrid system. It is shown that a controller for this transition system, realizing the given control objective, corresponds to a controller for the original hybrid system, realizing the same objective, and vice versa
Automatic deployment of autonomous cars in a robotic urban-like environment
Abstract-We present a computational framework and experimental setup for deployment of autonomous cars in a miniature Robotic Urban-Like Environment (RULE). The specifications are given in rich, human-like language as temporal logic statements about roads, intersections, and parking spaces. We use transition systems to model the motion and sensing capabilities of the robots and the topology of the environment and use tools resembling model checking to generate robot control strategies and to verify the correctness of the solution. The experimental setup is based on Khepera III robots, which move autonomously on streets while observing traffic rules
On Abstraction-Based Controller Design With Output Feedback
We consider abstraction-based design of output-feedback controllers for
dynamical systems with a finite set of inputs and outputs against
specifications in linear-time temporal logic. The usual procedure for
abstraction-based controller design (ABCD) first constructs a finite-state
abstraction of the underlying dynamical system, and second, uses reactive
synthesis techniques to compute an abstract state-feedback controller on the
abstraction. In this context, our contribution is two-fold: (I) we define a
suitable relation between the original system and its abstraction which
characterizes the soundness and completeness conditions for an abstract
state-feedback controller to be refined to a concrete output-feedback
controller for the original system, and (II) we provide an algorithm to compute
a sound finite-state abstraction fulfilling this relation.
Our relation generalizes feedback-refinement relations from ABCD with
state-feedback. Our algorithm for constructing sound finite-state abstractions
is inspired by the simultaneous reachability and bisimulation minimization
algorithm of Lee and Yannakakis. We lift their idea to the computation of an
observation-equivalent system and show how sound abstractions can be obtained
by stopping this algorithm at any point. Additionally, our new algorithm
produces a realization of the topological closure of the input/output behavior
of the original system if it is finite-state realizable
BioDiVinE: A Framework for Parallel Analysis of Biological Models
In this paper a novel tool BioDiVinEfor parallel analysis of biological
models is presented. The tool allows analysis of biological models specified in
terms of a set of chemical reactions. Chemical reactions are transformed into a
system of multi-affine differential equations. BioDiVinE employs techniques for
finite discrete abstraction of the continuous state space. At that level,
parallel analysis algorithms based on model checking are provided. In the
paper, the key tool features are described and their application is
demonstrated by means of a case study
Reachability in Biochemical Dynamical Systems by Quantitative Discrete Approximation (extended abstract)
In this paper, a novel computational technique for finite discrete
approximation of continuous dynamical systems suitable for a significant class
of biochemical dynamical systems is introduced. The method is parameterized in
order to affect the imposed level of approximation provided that with
increasing parameter value the approximation converges to the original
continuous system. By employing this approximation technique, we present
algorithms solving the reachability problem for biochemical dynamical systems.
The presented method and algorithms are evaluated on several exemplary
biological models and on a real case study.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Abstraction-Based Parameter Synthesis for Multiaffine Systems
International audienceMultiaffine hybrid automata (MHA) represent a powerful formalism to model complex dynamical systems. This formalism is particularly suited for the representation of biological systems which often exhibit highly non-linear behavior. In this paper, we consider the problem of parameter identification for MHA. We present an abstraction of MHA based on linear hybrid automata, which can be analyzed by the SpaceEx model checker. This abstraction enables a precise handling of time-dependent properties. We demonstrate the potential of our approach on a model of a genetic regulatory network and a myocyte model
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