We present an approach for the integration of Reinforcement Learning methods into Petri net based specifications of robot behaviors. Our work aims at opening an existing design methodology for embedded systems for the design of autonomous mechatronical systems with adaptive behavior. In order to combine Petri nets and learning methods, we modeled Q-Learning — a variant of Reinforcement Learning...

The use of supervised neural networks for the estimation of seismic source parameters from SAR interferometric data is presented in this paper. The RNGCHN software allowed the generation of the input-output pairs necessary for the learning phase of the net. After being trained, the net has been tested on real measured data. The obtained results encourage future developments of such an approach.

Roskilde University’s master’s programme in computer-mediated communication combines face-to-face seminars with net seminars focusing on collaborative project work. Net-based learning based on CSCW offers both advantages and pitfalls: (i) it helps to activate all students, (ii) it fosters complexity in organising tasks, (iii) asynchronous environment generates a need for synchronous communicati...

In this paper, typical analog combinatorial optimization approaches, such as Hopfield net, Hopfield-Lagrange net, Maximum entropy approach, Lagrange-Barrier approach, are systematically examined from the perspective of learning distribution. The minimization of a combinatorial cost is turned into a procedure of learning a simple distribution to approximate the Gibbs distribution induced from th...

We report on several test experiments with a mobile agent equipped with an artificial neural net control to achieve a basic route direction goal reflex in a 2-dimensional environment with obstacles. A real assembled 4tronix Initio robot kit agent is reproduced with its sensor and motor characteristics in a virtual environment for experimenting and comparing the behavior of its artificial neural...

Ship detection in the maritime domain awareness field has seen a significant shift towards deep-learning-based techniques as mainstream approach. However, most existing ship models adopt random sampling strategy for training data, neglecting complexity differences among samples and learning progress of model, which hinders efficiency, robustness, generalization ability. To address this issue, w...

Creating Petri Net domain models faces the same challenges that confront all knowledge-intensive AI performance systems: model specification, knowledge acquisition, and refinement. Thus, a fundamental question to investigate is the degree to which automation can be used. This paper formulates the learning task and presents the first machine learning method for Time Interval Petri Net (TIPN) dom...