Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLan with action rates, which specify the likelihood of exhibiting particular behaviour or of installing features at a specific moment or in a specific order. The enriched language (called PFLan) allows us to specify models of software product lines with probabilistic configurations and behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov chains. The Maude implementation of PFLan is combined with the distributed statistical model checker MultiVeStA to perform quantitative analyses of a simple product line case study. The presented analyses include the likelihood of certain behaviour of interest (e.g. product malfunctioning) and the expected average cost of products.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301

CASSL: Curriculum Accelerated Self-Supervised Learning

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for high-dimensional control require either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher- dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects

Status of the NASA/General Electric experimental clean combustor program, phase 3

The technology required to design and develop advanced commercial, conventional-takeoff-and-landing aircraft engines with significantly lower pollutant exhaust emissions levels than those of current-technology engines was generated and demonstrated. The target pollutant emissions reductions in tests of an advanced commercial aircraft turbofan engine were attained by developing advanced combustor designs. This technology is intended to be applicable to advanced military aircraft engines. The primary focus was on reducing the levels of the gaseous pollutant emissions

Variability and Evolution in Systems of Systems

In this position paper (1) we discuss two particular aspects of Systems of Systems, i.e., variability and evolution. (2) We argue that concepts from Product Line Engineering and Software Evolution are relevant to Systems of Systems Engineering. (3) Conversely, concepts from Systems of Systems Engineering can be helpful in Product Line Engineering and Software Evolution. Hence, we argue that an exchange of concepts between the disciplines would be beneficial.Comment: In Proceedings AiSoS 2013, arXiv:1311.319

Experimental clean combustor program, phase 1

Full annular versions of advanced combustor designs, sized to fit within the CF6-50 engine, were defined, manufactured, and tested at high pressure conditions. Configurations were screened, and significant reductions in CO, HC, and NOx emissions levels were achieved with two of these advanced combustor design concepts. Emissions and performance data at a typical AST cruise condition were also obtained along with combustor noise data as a part of an addendum to the basic program. The two promising combustor design approaches evolved in these efforts were the Double Annular Combustor and the Radial/Axial Combustor. With versions of these two basic combustor designs, CO and HC emissions levels at or near the target levels were obtained. Although the low target NOx emissions level was not obtained with these two advanced combustor designs, significant reductions were relative to the NOx levels of current technology combustors. Smoke emission levels below the target value were obtained

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab